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i Thesis for the Degree of Doctor of Philosophy EFFICIENCY IMPROVEMENTS IN WASTE-TO-ENERGY COMBUSTION PROCESSES Method Development and Evaluation Francis Chinweuba Eboh
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Thesis for the Degree of Doctor of Philosophy
EFFICIENCY IMPROVEMENTS
IN WASTE-TO-ENERGY COMBUSTION PROCESSES Method Development and Evaluation
Francis Chinweuba Eboh
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Efficiency improvements in waste-to-energy combustion processes: Method development and evaluation Copyright 2019 © Francis Chinweuba Eboh Swedish Centre for Resource Recovery Faculty of Textiles, Engineering and Business University of Borås SE-501 90 Borås, Sweden Digital version: http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-21801 ISBN 978-91-88838-47-6 (printed) ISBN 978-91-88838-48-3 (pdf) ISSN 0280--381X, Skrifter från Högskolan i Borås, nr. 100 Cover photo: Waste-to-energy plant (photo by David Castor) Printed in Sweden by Stema Specialtryck AB Borås 2019
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Abstract
The current increase being experienced in the generation of waste endangers human health and the
environment. One possible way of addressing this issue is to minimise it by reusing or recycling large
fractions of waste materials. A suitable approach for treating undesired end products remaining after
recycling is the energy recovery method. The electrical efficiency of this technology, however, is generally
low when compared with other solid fuel-fired combustion plants as a result of low steam properties.
Furthermore, there is lack of efficient methods to evaluate the performance of this system. The energy
method, normally used, does not account for exergy destruction due to entropy generated within the system. In this thesis, an exergy model for estimating the maximum available energy in a municipal solid waste and
a modified exergy-based method for calculating the improvement potential in a waste-to-energy plant are
developed. The exergy model was obtained from estimations of the higher heating value and standard
entropy of municipal solid waste from the elemental compositions of the waste using statistical analysis.
The improvement potential was derived by comparing the exergy destruction of the real process with its
corresponding theoretical process. It was applied in a solid-waste fired heat and power plant to investigate
possible improvements in the system as well as the cost of the improvements. The different improvement
modifications considered include the re-arrangement of air heaters, the introduction of a reheater, flue gas
condensation and an integrated gasification-combustion process. Modelling, simulation and cost
estimations were performed with the Aspen Plus software. The results showed that the present proposed exergy model was more accurate than the previous models
for estimating the maximum available energy in waste material, as the proposed model incorporates all the
major elemental constituents as well as the physical composition of the solid waste. Moreover, the results
obtained from the higher heating value model show a good correlation with the values measured, and are
comparable with other recent and previous models. Furthermore, it was found that 64 % of the total exergy
destruction in the process plant investigated can be reduced, while the boiler was identified as a component
with the greatest potential for making improvements to the plant. Although the integrated gasification-
combustion technology with flue gas condensation has the highest exergy efficiency, its higher capital cost
exceeds all other alternatives. The improvement modifications with flue gas condensation not only provide
the highest heat production but also the highest net present value. This indicates that flue gas condensation
has a significant impact on the overall income generated by waste-to-energy combined heat and power
industries. Keywords: Solid waste, exergy, entropy, higher heating value, improvement potential, waste-to-energy plant, efficiency improvement, cost evaluation, simulation, modelling
SVANENMÄRKET
Trycksak3041 0234
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List of Publications
This thesis is based on the results presented in the following articles:
I. Eboh F.C., Ahlström P, Richards T. Exergy analysis of solid-fuel fired heat and power
plants: A review. Energies 2017; 10(2).
II. Eboh F.C., Ahlström P., Richards T. Estimating the specific chemical exergy of
municipal solid waste. Energy Science and Engineering 2016;4(3):217-231.
III. Eboh F.C., Ahlström P., Richards T. Evaluating improvements in a waste-to-energy
combined heat and power plant. Case Studies in Thermal Engineering 2019;14.
IV Eboh F.C., Andersson B-Å., Richards T. Economic evaluation of improvements in a
waste-to-energy combined heat and power plant. Waste Management 2019;100:75-83.
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Statement of Contributions
The contributions made by Francis Chinweuba Eboh to the appended papers:
Paper I: Responsible for the idea, literature review, writing of the manuscript and its
revision.
Paper II: Responsible for the idea, statistical analysis, model estimations, writing of the
manuscript and its revision.
Paper III: Responsible for the idea, process modelling and simulation, efficiency evaluation,
writing of the manuscript and its revision.
Paper IV: Responsible for the idea, process modelling and simulation, economic evaluation,
writing of the manuscript and its revision.
Publication not included in this thesis
Eboh F.C., Ahlström P., Richards T. Method of Estimating Absolute Entropy of Municipal
Solid Waste. International Journal of Power and Energy Engineering 2016;10.
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Conference Contributions and Award
Eboh F.C., Ahlström P., Richards T. Method of Estimating Absolute Entropy of Municipal
Solid Waste, 18th International Conference on Exergy, Exergy Systems Analysis and
Optimization in Zurich (Switzerland) July 21-22, 2016.
Award: Certificate of Best Paper Award from World Academy of Science, Engineering and
Technology (WASET).
Eboh F.C., Ahlström P., Richards T. Performance Evaluation of a Waste-to-Energy Power
Plant: An Exergetic Approach, 25th European Biomass Conference and Exhibition in Stockholm
(Sweden) June 12-15, 2017.
Eboh F.C., Ahlström P., Richards T. Development of an Exergy-Based Method to Evaluate the
Improvement Potential of Fluidized Bed Waste-to-Energy Plant, 13th International Conference
on Energy Sustainability: The American Society of Mechanical Engineers, (Washington) USA
July 14-17, 2019.
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Preface
This thesis is a partial requirement for a Ph.D. degree in Energy Technology at the Swedish Centre
for Resource Recovery, Faculty of Textiles, Engineering and Business, University of Borås,
Sweden. The dissertation was written under the supervision of Professor Tobias Richards,
Associate Professor Peter Ahlström and Associate Professor Anita Pettersson. The efficiency calculation of a waste combustion process can be evaluated by using not only the
energy content in the waste but also using the maximum available energy as input to the system,
as this will account for the irreversibility in the process due to the entropy generated in the waste
material. Furthermore, the proper evaluation of efficiency improvement made to a process
demands knowledge of possible improvements. This thesis presents methods for calculating the
maximum energy available in municipal solid waste and for evaluating improvements made in a
municipal waste-to-energy combined heat and power plant. Moreover, considering recent
developments in this sector, these methods may also be used to evaluate the profitability of the
energy recovered from waste technology.
Francis Chinweuba Eboh
November 2019
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Nomenclature
E energy rate (kW) Ex exergy rate (kW) e specific exergy (kJ/kg) m mass flow rate (kg/s) Q heat transfer rate (kW) R2 coefficient of determination S entropy (kJ/K) s specific entropy (kJ/kg·K) W work transfer rate (kW) Subscripts a available c cold D destruction e energy ex exergy f flow h hot i input k component of a process L lost max maximum o output p product rp real process tp theoretical process 0 reference temperature Superscripts AV available ch chemical UN unavailable Abbreviations AAE average absolute error ABE average bias error BC boiler combustor BCP base case plant BFB bubbling fluidized bed C carbon
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CFB circulating fluidized bed Cl chlorine COND condenser CP condensate pump DH district heating DRT deaerator ECO economizer EVA evaporator FBB fluidized bed boiler FG flue gas FGC flue gas condenser FWH feed-water heater FWP feed-water pump H hydrogen HPT high-pressure turbine IP improvement potential IPT intermediate-pressure turbine IRR internal rate of return LPT low-pressure turbine M modification MIX mixer MSW municipal solid waste N nitrogen NPV net present value O oxygen P pressure PSAH primary steam air heater S sulphur SH superheater SPLIT splitter SSAH secondary steam air heater STDRUM steam drum SW steam and water TP theoretical process T temperature W water Greek letters ∆ change 𝜂𝜂 efficiency
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List of Tables and Figures
Table 2.1. The physical classification of MSW…………………..………………………………..5
Table 2.2. European Union average emission limit values for waste combustion …………...…….7
Table 4.1. Results from the analysis of a coal-fired thermal plant…………………..……..……...22
Table 4.2. Standard chemical exergy and standard entropies of various compounds..…………...24
Table 5.1. Design parameters of the plant……………….………………………...……………...30
Table 5.2. Evaluation of the improvement in efficiency in a combined heat and power plant…….33
Table 5.3. Evaluation of the improvement in efficiency in a power plant…………………...…....33
Table 6.1. The parameters of the base case plant and the different improvement
modifications made…………………………………...………………………...…….35
Figure 2.1. Process diagram of waste combustion producing electricity and heat…....……………8
Figure 2.2. Schematic diagram of a solid waste-fired grate boiler ………..……………..…………9
Figure 2.3. Moving grate during combustion………………………………………………...…...10
Figure 2.4. Waste reception (bunker) and the crane………………………………………………10
Figure 2.5. Schematic diagram of a solid-waste fired fluidized bed boiler……………..………....11
Figure 4.1. Comparison between the experimental and estimated HHV using the developed
Correlation………………………………………...….……………………………..26
Figure 4.2. Comparison between the experimental and estimated HHV using the Channiwala
and Parikh correlation ………………………………...……………………………..26
Figure 4.3. Comparison between the experimental and estimated HHV using the Sheng and
Azevedo correlation..……………………………………………………....………..26
Figure 4.4. Comparison between the experimental and estimated HHV using
Dulong’s correlation…………………………………………………………………26
Figure 5.1. Schematic diagram of the municipal heat and power plant fired by solid waste………29
Figure 5.2. The base case plant, modelled in Aspen Plus……………………………..…………..30
Figure 5.3. The exergy efficiency of the boiler and overall process plant………...……………....31
Figure 5.4. Percentage of the improvement potential of the components in the
base case process plant……………………………...……...………………………..32
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Figure 6.1. The ratio of the increment in the capital cost to the increment in the exergy
efficiency and capital cost per total revenue……………...…...………………….….36
Figure 6.2. Net Present Value (NPV) and Internal Rate of Return (IRR) for the base case
and the various modifications………………………………………………………..37
Figure 6.3. Net Present Value for plants producing electricity only………………………………38
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Table of Contents
Abstract………………………………………………………………………………………….iii
List of Publications………………………………………………………..……………………..v
Statement of Contributions………………………………………………………….………….vi
Conference Contributions and Award…………………………………………..……………vii
Preface……………………………………………………………………….…………...………ix
Nomenclature……………………………………………………………………...………….…xi
List of Tables and Figures……………………………………...…………………..…………xiii
Chapter 1…………………………………………………………………………..………………1
Introduction…………………………………………………………………………...………….1
1.1 Aim…………………………………………………………………………...……………2
1.2 Research Questions……………………………………………………...…………………3
1.3 Outline of the Thesis……………………………………………………….………………3
Chapter 2…………………………………………………………………………………………..5
Background……………………………………………………………………..………………..5
2.1 Solid Waste as a Fuel…………………………………………………………...………….5
2.2 Waste-to-energy Combustion Technology………………………………...………………6
2.3 Combustion Firing Systems………………………………………………………………..8
2.3.1 Grate Boiler………………………………………………………...……………..……9
2.3.2 Fluidized Bed Boiler…………………………………………….…………………....11
Chapter 3……………………………………………………………………………………..…..13
Overview of Efficiency Evaluation Methods……………………………………………….…13
3.1 Energy Efficiency Method………………………………………………………..………13
3.2 Exergy Efficiency Method………………………………………..………………………14
3.3 R1-formula Method……………………………………………………...……………….16
3.4 Improvement Potential Method…………….…………………………………………….17
3.5 Chemical Exergy of Solid Fuels…………………………………………….……………18
3.6 Research Necessary…………………………………………………………...………….18
Chapter 4……………………………………………………………………..…………………..21
Improving Efficiency Evaluation Methods……………………………………………………21
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4.1 Using Exergy as a Method for Evaluating Efficiency…….…………...…………………21
4.2 Estimating the Chemical Exergy of Solid Waste…………………………………………22
4.2.1 Estimating the Higher Heating Value and Standard Entropy of MSW……………….24
4.3 Improvement Potential Methods…………………………………………………….……27
Chapter 5…………………………………………………………………………………..……..29
Evaluating Efficiency Improvement…….………………………………………………...…..29
5.1 The Base Case Plant………………………………………………………………………29
5.2 Different Efficiency Improvement Methods……………………………………..……….32
Chapter 6……………………………………………………………………………………..…..35
Economic Evaluations of Efficiency Improvement……….………………………..…………35
6.1 Cost of Improving Efficiency……………………………………………..…...…………36
6.2 Profitability Evaluation…………………………………………………………...………37
Chapter 7………………………………………………………………………..………………..39
Validation………………….………….…………………………………………..……….……39
Chapter 8…………………………………………………………………..……………………..41
Conclusions…………………………….………………………………………….……….……41
Chapter 9..…………………………………………………………………...…………………...43
Future Work………………………………………………………………….....………………43
Acknowledgements……………………………………………………………....……………..45
References…………………………………………………………………………………….…49
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Chapter 1
Introduction
The generation of solid waste is an inevitable by-product of human activity and civilization in
general, and increases in quantity as a result of growth in population, industrialization and
urbanization [1, 2]. The traditional indiscriminate disposal of waste in landfill has become a major
environmental problem that pollutes the air, land, ground water and endangers human health [3].
Landfilling contributes to about 5 % of total greenhouse gases (CH4, N2O, CO2) emissions that
affect global warming and climate change [4]. The ability to provide solutions of how the large
quantities of waste generated can be managed effectively is one of the greatest challenges facing
both the present and future generations [5]. One possible approach is to minimize the amount of
waste produced through reusing or recycling large fractions of waste materials [6]. A suitable
method for treating undesired the end products remaining after recycling is the energy recovery
approach [5]. Sweden, for instance, is an example of a country with efficient management of solid
waste: landfilling has been significantly reduced to 0.5 % and about 50 % of the solid waste
generated by households in 2017 was treated in waste-to-energy plants [7].
The energy recovery method of waste treatment does not only help in treating non-reusable and
non-recyclable amounts of waste but also in the conversion of valuable energy resource into
electricity and heat [8]. The technology used involves both thermochemical and biological
processes [9], and is selected depending on the type, chemical composition and energy content of
the waste and, finally, the overall efficiency. Thermochemical processes are more efficient than
biological ones in terms of faster reaction rates and larger reductions in the mass and volume of
the solid waste [10] and can be divided into three main groups: combustion, gasification and
pyrolysis. Of these, combustion technology is the most widely used method for treating waste
materials of different types and size [11,12]. It is a commercially viable option for the conversion
of solid fuels into heat and power, and contributes to over 97 % of the world’s production of bio-
energy [10,13].
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Waste-to-energy combustion technology has its own drawbacks. One of the major issues of this
method is the low efficiency of utilizing the waste fuel as a result of the low heating values caused
by the high contents of moisture and oxygen in the waste [13]. The average heating values of solid
waste is about 10 MJ/kg [12] compared to 15-19 MJ/kg and 20-30 MJ/kg on dry basis for biomass
materials and coals, respectively [10]. Furthermore, the electrical efficiency of waste combustion
process plant is generally low when compared with other solid fuels due to the low steam properties
that are employed in order to prevent fouling, slagging and corrosion of the heat exchanger tubes
in the boiler [14, 15]. The typical electrical efficiency of a waste combustion plant is in the range
of 18-26 % whereas average efficiencies of 35 %, 45 % and 38 % are reported for coal, natural gas
and oil-fired power plants, respectively [12]. Moreover, the technology for recovering energy from
waste is capital intensive when considering the high financial investments and high maintenance
and operating costs involved [16]: the investment cost is about three times greater than for a
woodchip CHP and four times greater than for a pulverized coal power plant [17]. This thesis
addresses some of the challenges mentioned above by providing methods for evaluating efficiency
and using them to evaluate various improvements, as well as their cost implications, to the state-
of-the-art techniques employed in waste-to-energy combustion plants.
1.1 Aim
The aim of this thesis is to improve the methods used to evaluate the performance of a waste-to-
energy combined heat and power plant that combusts both household and industrial waste with
respect to enhancing efficiency and profitability. The specific objectives are:
1. To examine the existing efficiency evaluation methods used in a solid-fuel fired heat and
power plant and develop an exergy model for calculating the maximum available energy
in a municipal solid waste plant (Papers I and II).
2. To determine the potential for improvement in a municipal heat and power plant fired by
solid waste (Paper III).
3. To evaluate the cost of making improvements in efficiency and profitability in a waste
combustion plant (Paper IV).
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1.2 Research Questions
This thesis is based on the following research questions:
How can the maximum available energy and entropy generated during the solid waste
conversion process be calculated?
What is the best way to evaluate efficiency improvements made in a waste-to-energy
combustion plant?
How is the maximum theoretical efficiency of a waste combustion plant determined?
What are the best options for improvement based on the state-of-the-art technology applied
in municipal solid-waste fired heat and power plants with respect to the cost and economic
viability of the different improvements?
1.3 Outline of the Thesis
This thesis is divided into the following chapters:
Chapter 1 introduces the thesis and its aim.
Chapter 2 presents the background and the research required.
Chapter 3 is an overview of the various methods used for evaluating the efficiency of a
process.
Chapter 4 discusses the applications of exergy analysis in the evaluation of thermal
conversion processes fired by solid fuels. It also presents the modified exergy
improvements method introduced in this thesis.
Chapter 5 describes the base case plant used in this thesis, along with the various efficiency
improvement methods that are based on the state-of-the-art technology applicable to
municipal waste-to-energy combined heat and power plants.
Chapter 6 examines the costs involved with the different improvement modifications in
order to ascertain the economic viability of process plants.
Chapter 7 compares the key results obtained in this work with previous studies.
Chapters 8 and 9 provide a summary of the key findings and suggestions for future research
work, respectively.
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Chapter 2
Background
2.1 Solid Waste as a Fuel
Municipal solid waste (MSW) is normally referred to as trash or garbage [18]. It is considered as
unwanted material that is discarded as useless or worthless [17]. Its physical composition is
heterogeneous and varies with time, life-style, economic status as well as geographical region [19,
20, 21]. The main constituents of municipal solid waste is shown in Table 2.1 [20].
Table 2.1[20] The physical classification of MSW.
Physical classification Explanation Organics Food residue Rice, cooked food remains, meat, vegetable, fruit waste Wood waste Waste wood, disposable chopsticks, bamboo, flowers, grass, leaves,
branches Paper Tetrapak paperboard, office paper, toilet paper, newsprint, magazines Textiles Clothes, cloth shoes, cotton, chemical fibres Plastics All kinds of plastic: film, bottles, tubes, bags, toys Rubber Rubber shoes, worn out tyres Inorganics Metals Iron wire, cans, metal parts, pans Glass Glass: fragments, bottles, mirrors, balls Tiles Stones, tiles, cement, ceramic Ash Slag, soil Other Batteries, plaster
The energy recovered from solid waste depends on its physical composition [22], proximate and
ultimate analyses and heating value. The proximate analysis determines the moisture content,
along with the amounts of ash and volatile and fixed carbon in the waste, while the ultimate
analysis provides the elemental constituents in the waste fuel, such as carbon, hydrogen, oxygen,
nitrogen, sulphur and chlorine. The heating value is the energy content released during combustion
of the organic components of solid waste [23]. Statistical modelling using correlation from the
physical composition [24, 23], proximate analysis [24] and ultimate analysis [24] have been
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applied to evaluate the heating value of municipal solid waste. According to Zhou et al. [20], the
variation in heating value has a significant effect on the stable operation of a waste combustion
plant and its monthly average lower heating value should therefore exceed 4.1 MJ/kg to ensure
complete combustion.
2.2 Waste-to-energy Combustion Technology
The combustion of solid waste in industrial scale started around the end of the 19th century, with
the first plants being constructed in England, the USA and Germany. The plants operated in an
open environment, without energy recovery, with two main purposes: reducing the volume of the
waste generated, so called incineration and promoting public health by controlling the spread of
cholera that emanated from landfill [17, 26]. At that time, the technology used was very simple:
brick-lined cell ovens had a fixed metal grate over an ash pit, with one opening at the top or side
of the oven for loading and another opening for removing the solid residues [27]. Since then, there
has been significant advancement in the fields of energy efficiency and emission control of the
system, one of which is a waste stream with increasing pollutants that now have the potential to
be treated [26].
Waste-to-energy plants are well established in Europe, being the result of heavy taxes imposed on
landfilling and bans on landfilling of unprocessed waste in some countries. The emission control
from their operation has been driven by regulations passed by the European Union using the
Directive for Waste Incineration in Europe, which means that all plants must be equipped with
flue gas treatment technologies and operate within the average emission limit values as shown in
Table 2.2 [28, 29]. This is to meet stringent regulatory standards to ensure that all emissions to air
are well-controlled [30]. European countries had 429 waste-to-energy plants from which 96
million tonnes of waste were combusted in 2017 [31], with Sweden and Denmark having the
highest incineration capacity of 591 and 587 kg/capita, respectively [32]. Sweden, for instance,
has about 34 waste-to-energy combustion plants and recovers more energy from waste per capita
than any other country in Europe [7]. The capacity of waste combustion plants in Sweden is, in
fact, greater than the amount of combustible waste than the country produces: in 2017, a total of
6,150,150 tonnes of industrial and household waste were treated and converted into more than 18.3
TWh of energy, of which 2.2 TWh was for electricity and 16.1 TWh for heating [7]. An average
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plant in Europe uses the grate-based combustion system that produces 546 kWh and 640 kWh of
electricity per ton of waste with a gross energy efficiency of 18 % and 22 %, respectively [26]. In
addition, these plants typically use steam parameters of 40 bar and 400 oC, respectively, for
pressure and temperature, and use waste with the average net calorific value of 10. 44 MJ/kg [26].
The increase in the capacity of these plants, the steam properties, integration processes (such as
air heater, feed water heater and reheater) and co-generation (heat and power) have led to an
enhancement in the overall efficiency of energy recovery from waste (Paper III).
Table 2.2[28, 29] European Union average emission limit values for waste combustion plants. Air emission Values Particulate matter 10 Sulphur dioxide (SO2) 50 Nitrogen oxides (NOx) 200 Hydrochloric acid (HCl) 10 Fluorine acid (HF) 1 Carbon monoxide (CO) 50 Heavy metals 0.5 Cadmium (Cd) 0.05 Mercury (Hg) 0.05 Dioxin and furans 0.1
All values in mg/Nm3 (except for Dioxin and furans, ng/Nm3).
Advancement in the waste-to-energy sector has been observed in other countries as well. In the
United States in 2014, 33 of 136 million tonnes of waste generated were treated in energy recovery
plants with a total capacity of 2.5 GW that produced 14.3 TWh of electricity [33]. In the late 1980s,
the waste combustion process was introduced in China; the technology has since made huge
advancement, with the total capacity reaching 46 million tonnes a year between 2013 and 2014,
and a power generation of 18.7 TWh [33]. In Japan, waste combustion is the most widely-used
technology for waste treatment, due to the lack of land suitable for landfill sites [27]. Japan has
the largest number of waste combustion plants in the world, according to Tan et al. [34]: over 80
% of their MSW is combusted in 1,900 plants.
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Energy recovery has other advantages apart from the production of heat and power. The waste-to-
energy combustion process not only reduces the volume and mass of solid waste by 90 % and 70
%, respectively [35] but is also an important additional source of renewable energy because half
of the energy utilized in solid waste is biogenic [27]. Recovering energy from waste decreases the
overall amount of CO2 emitted to the environment when compared to the CH4 and CO2 generated
in landfill operations: the impact of CH4 as a greenhouse gas is 21 times higher than that of CO2
[36, 37]. Furthermore, waste combustion helps in the detoxification and the destruction of
pathogenic organisms that are hazardous to public health [38].
2.3 Combustion Firing Systems
The combustion of a heterogeneous mixture of solid waste material involves several steps: drying
and degassing (to remove moisture content and volatile organic compounds), followed by
pyrolysis and gasification. The final step is oxidation, where CO2 and H2O are formed along with
the release of energy as a result of the exothermic reactions [17]. The energy released is utilized
in the Rankine steam cycle for the production of heat and electricity. The process diagram of a
typical waste combustion for combined heat and power plant is shown in Figure 2.1.
Figure 2.1. Process diagram of waste combustion producing electricity and heat.
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There are two main types of combustion firing systems used in facilities where energy is recovered
from waste: (i) stoker or grate-fired and (ii) fluidized bed boiler systems. The firing system selected
depends on the extent of preparation of the solid waste to be used as fuel and the design of the
combustion system [17]. Solid waste used as received, without size reduction and separation of
metals, is more suited to a grate boiler than a fluidized-bed boiler (FBB) [39].
2.3.1 Grate Boiler
The grate-fired boiler is the waste combustion technology used most widely in the world [27]. It
is normally designed to combust solid waste fuel without the need of major pre-treatment, which
is placed on top of a grate with primary air passing up through it and secondary air passing over
it. This technology is comprised of a stoker or fuel-feeding system, a moving grate assembly to
support the burning mass of fuel, an underfire or primary air beneath the grate, a secondary air
system to complete the combustion process and limit atmospheric emissions, and an ash or residual
discharge system [39]. A schematic diagram of a grate boiler is shown in Figure 2.2, while the
combustion process in a moving grate and the waste reception facility are presented in Figures 2.3
and 2.4, respectively.
Figure 2.2. Schematic diagram of a solid waste-fired grate boiler: (1) fuel feed, (2) primary air, (3) secondary air, (4) bottom ash, (5) superheaters, (6) economizer.
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Figure 2.3. Moving grate during combustion.
Figure 2.4. Waste reception (bunker) and the crane.
According to Kitto and Stultz [39], at normal operation, the primary air accounts for about 60 %
of the total air flow whilst the rest comes from the secondary or overfire air, supplied through the
air ports located in the front and rear walls of the furnace. Reduction of excess air in the boiler can
be achieved by recirculating the flue gas, which combines with the secondary air and maintains
the total flow volume of the combustion air while decreasing the amount of excess air: this helps
to improve the boiler’s efficiency and reduce the formation of NOx [39]. A grate-fired boiler has
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two essential advantages: the capacity to handle lump-sized waste and the ability to accommodate
waste fuel with fluctuating properties [27].
2.3.2 Fluidized Bed Boiler
In a fluidized bed boiler (FBB), finely crushed and sorted waste fuel is required to ensure
fluidization of the fuel within the bed. A schematic diagram of this technology is shown in Figure
2.5.
Figure 2.5. Schematic diagram of a solid-waste fired fluidized bed boiler: (1) fluidized bed, (2) fuel feed, (3) primary air, (4) secondary air, (5) empty gas pass, (6) superheaters, (7) cyclone and (8) economizer.
The operation of a fluidized bed boiler involves burning solid waste in an air-suspended bed of
inert material such as sand (known as the bed material) and sometimes limestone (when it is added
to control SO2) at the bottom of the combustion chamber. It consists of an air distributor with a
large number of nozzles placed over the bottom of the furnace [17]. Fluidization occurs when the
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air flow causes the bed particles to suspend in the upward stream and start behaving as a fluid [37].
Normally, the fluidized bed temperature is limited to the range of 800 to 900 oC in order to prevent
the ash and bed particles from agglomerating [27].
There are two main types of fluidized beds in such boilers: bubbling fluidized bed (BFB) and
circulating fluidized bed (CFB). In a BFB boiler, the velocity of the air does not cause the particles
to move out of the bed, and is the preferred option for moderately-sized boilers [17]. In a CFB
boiler, the velocity of the air distributor is sufficiently high to carry some bed material out of the
bed container, i.e. it has higher fluidizing velocities, which increase erosion and the fan power
required [27]. In this technology, the bed material is recirculated back to the boiler by means of a
cyclone to ensure the continuous supply of particles [17].
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Chapter 3
Overview of Efficiency Evaluation Methods
Evaluations and analysis of energy conversion processes are crucial for efficient utilization of
energy resources in the system [40]. It enables adequate decisions to be made regarding efficiency
improvement and the profitability of the process based on the inefficiencies and/or losses
identified. Various different evaluation methods have been used in waste-to-energy conversions
and thermal process plants and include, for example, the energy efficiency method, the exergy
efficiency method, the R-formula and the improvement potential method.
3.1 Energy Efficiency Method
The energy efficiency method is the one used most often to evaluate the performance of a thermal
system. Also known as the first law efficiency method, it is based on the first law of
thermodynamics or the conservation of energy [41, 42]. It is defined as the ratio of the useful
energy output rate to the energy input rate in the system [43], and is calculated using Equation
(3.1):
𝜂𝜂e =��𝐸o��𝐸i= 1 − ��𝐸L
��𝐸i (3.1)
where ��𝐸i and ��𝐸o are the energy input and useful energy output rates, respectively, and ��𝐸L is the
rate of energy loss.
The energy method has been employed to evaluate improvements made to waste-to-energy
processes. Main and Maghon [44] used energy analysis to access different improvement options
for enhancing the efficiency of modern energy from waste (EFW) facilities located in
Hameln/Germany, Arhus/Denmark, Heringen/Germany, Naples/Italy and Ruedersdorf
(Berlin)/Germany. In the Afval Energie Bedrijf waste-to-energy plant in Amsterdam, a net
electrical efficiency exceeding 30 % was achieved when improvement modifications were applied
[45]. Evaluation has also been carried out for a typical EFW plant in Germany operating with
steam parameters of 40 bar and 380 °C, an excess air ratio of 1.75, a condensate pressure of 150
14
mbar and a net plant efficiency of 20.6 % [46]. Increases in net efficiency of 21.3 %, 23.4 %, 24.0
% and 28.1 % were calculated for an excess air ratio reduced to 1.4, condensate pressure reduced
to 30 mbar, steam parameters increased to 74 bar/ 480 °C and steam parameters increased to 130
bar/ 480 °C, respectively, using intermediate reheat.
Even though the energy method is mostly used for system evaluation in a waste combustion plant,
the use of this analysis alone for performance criteria in a thermal process is deemed inadequate:
it is bound to lead to misconception, misevaluation and poor decision-making [47]. It does not
account for irreversibilities within the system, providing only information of inputs and outputs of
energy in the process [48]. Hence, the concept of second law analysis, based on exergy efficiency,
was introduced.
3.2 Exergy Efficiency Method
Exergy analysis has been shown to be an effective tool in furthering the goal of attaining a more
efficient use of energy resources [49]. Its aim is to identify the locations and magnitudes of
thermodynamic irreversibilities in a process. Exergy analysis explicitly takes the effects of the
surroundings into account: it provides a more realistic picture of improvement potentials compared
to a pure energy analysis. On the other hand, a definition of the state of the surroundings is not
always unambiguous, leaving some uncertainties in the analysis. Exergy is the maximum amount
of work that can be obtained from a stream of matter as it comes to equilibrium with a reference
environment [50]. It combines the utilization of the first and second laws of thermodynamics, using
the concepts of irreversibilities identified as a result of entropy generated within the system [51].
Furthermore, exergy is not subject to conservation law, hence it accounts for losses due to
irreversibility during any process [50].
For the steady-state process, assuming that the changes in kinetic and potential energy in this
particular system can be neglected, the exergy destruction rate for the overall system can be
determined from the exergy rate balance, and is given in Equation (3.2):
��𝐸𝑥𝑥D = ∑ ��𝐸𝑥𝑥ii − ∑ ��𝐸𝑥𝑥oo + ∑ [1 − 𝑇𝑇0𝑇𝑇j]j ��𝑄j − W (3.2)
15
where ��𝐸xi and ��𝐸xo are the input and output, respectively, of the system’s exergy rate, ��𝑄j is the
heat transfer rate in at position j through the boundary at temperature 𝑇𝑇j and W is the net work
transfer rate out across the boundary of the system.
In the case of two separate fluid streams interacting (such as in boiler heat exchangers, condensers,
air preheaters and feedwater heaters), the exergy destruction rates are obtained from Equation
(3.3), assuming no heat loss to the surroundings, as follows:
��𝐸𝑥𝑥D = ∑ (��𝑚h,i𝑒𝑒𝑥𝑥h,i + ��𝑚c,i𝑒𝑒𝑥𝑥c,i)i − ∑ (��𝑚h,o𝑒𝑒𝑥𝑥h,o + ��𝑚c,o𝑒𝑒𝑥𝑥c,o)o (3.3)
where ��𝑚h and ��𝑚c are the mass flow rate of the hot and cold stream, respectively.
The process exergy efficiency for a heat and power plant, 𝜂𝜂ex, is expressed as Equation (3.4):
𝜂𝜂ex = ��𝐸𝑥𝑥Qh+��𝑊net∑ ��𝐸𝑥𝑥ii
= ∑ (Exergy available)a∑ (Exergy input)i
(3.4)
where ��𝐸𝑥𝑥Qh is the exergy flow rate associated with the production of heat and Exi is the exergy
input rate given in Equation (3.5).
��𝐸𝑥𝑥i = ��𝑚f · 𝑒𝑒𝑥𝑥ch (3.5)
where 𝑒𝑒𝑥𝑥ch is the specific exergy of the fuel. For a solid-waste fuel, such as municipal solid waste,
the specific chemical exergy can be calculated by Equation (3.6): this is taken from the model
developed in Paper II and is based on the elemental composition of waste fuel.
𝑒𝑒𝑥𝑥ch = 376.461C + 791.018H − 57.819O + 45.473N − 1536.242S + 100.981Cl (kJ/kg) (3.6)
where C, H, O, N, S and Cl are the content of carbon, hydrogen, oxygen, nitrogen, sulphur and
chlorine, respectively, in solid waste in wt %.
16
Exergy analysis has been used widely in the evaluation of thermal processes. Bejan et al. [52]
investigated the application of the exergy method in the thermal process of a co-generation system
that included a gas turbine and heat-recovery steam generator. This method has been applied in
the coal combustion process [53-70], the biomass conversion process [71-76] and the coal-biomass
co-combustion process [77-79]. In the case of waste-to-energy combustion, Solheimslid et al. [8]
used different methods to calculate the chemical exergy of solid waste by employing correlations,
the chemical exergy obtained from the combustion equation and the absolute entropy to determine
the exergy efficiency of a combined heat and power plant fired by municipal solid waste in Bergen,
Norway. They found the results of the different methods to be in good agreement. Grosso et al.
[80] found that exergy analysis was a more reliable measure of performance criteria in waste
incineration plants in Europe than the energy recovery efficiency analysis proposed in the Waste
Frame Directive [81].
The exergy method has been used as an effective method of evaluating the performance of a system
because it enables the main sources of inefficiency to be identified and provides directions for
improvement enhancement within the system This method alone, however, does not provide
information of the improvements that are possible (improvement potential) in a real process.
3.3 R1-formula Method
The R1-formula method is proposed by the European Union, under the new Waste Frame Work
Directive (WFD), used as a guideline for improving the energy performance of waste-to-energy
combustion plants. The directive allows waste combustion plants to be classified as recovery
operations rather than disposals, provided than the energy recovery efficiency is higher than a
designated threshold of equal to or above 0.60 for installations in operation and sanctioned before
1st January 2009, and 0.65 for installations sanctioned after 31st December 2008 [81]. The R1-
formula is given in Equation (3.7) thus:
Energy efficiency = 𝐸𝐸p−(𝐸𝐸f+𝐸𝐸i)0.97∗(𝐸𝐸w+𝐸𝐸f) (3.7)
where 𝐸𝐸p is the annual energy produced as heat or electricity. It is calculated with energy in the
form of electricity being multiplied by 2.6 and heat produced for commercial use multiplied by 1.1
17
(GJ/year). 𝐸𝐸f is the annual energy input to the system from fuels contributing to the production of
steam (GJ/year). 𝐸𝐸w is the annual energy contained in the treated waste calculated using the net
calorific value of the waste (GJ/year). 𝐸𝐸i is the annual energy imported, excluding 𝐸𝐸w and 𝐸𝐸f (GJ/year). 0.97 is a factor accounting for energy losses due to bottom ash and radiation.
The use of the R1-formula for the evaluation of the energy performance of waste combustion may
be inadequate because it does not consider the size of the plant and its operation based on climate
regions. In addition, the energy efficiency method used does not account for the quality of the
energy in the heat produced during district heating.
3.4 Improvement Potential Method
The need to improve the exergy-based method of evaluation led to the introduction of the exergy
improvement potential, which was proposed by van Gool [82] for industrial processes, given in
Equation (3.8) as:
IPk = (1 − 𝜂𝜂𝑒𝑒𝑒𝑒,𝑟𝑟𝑟𝑟)ExDk,rp (3.8)
This method, which has been applied to the evaluation of energy systems in the UK [83] in a coal
thermal plant [84] and for solar energy [85], relates the improvement potential to the inefficiency
experienced in a system with its exergy efficiency. Nevertheless, the improvement is limited to the
current performance of the specific real process system without taking any future development in
the system into consideration. The method does not compare a specified process with its theoretical
process for potential advancement and relative progression in the system.
Improvement that has been made to exergy analysis has led to the development of advanced exergy
analysis [86], which involves dividing the destruction of exergy into two parts: avoidable and
unavoidable. Avoidable exergy destruction is defined as the irreversibility that can be prevented
through performance enhancement, whilst unavoidable exergy destruction occurs as a result of
physical, technological and economical constraints. Here, the improvement potential lies in the
former, i.e. avoidable, part of exergy destruction. The avoidable exergy destruction rate for a
component, k, is obtained from Equation (3.9):
18
Ex𝐷𝐷,𝑘𝑘AV = Ex𝐷𝐷,𝑘𝑘 − Ex𝐷𝐷,𝑘𝑘UN (3.9)
This method has been applied to a gas turbine co-generating system [86], a combined cycle power
plant [87], a fluidized bed boiler [88] and a geothermal power plant [89]. The unavoidable exergy
destruction rate is determined by selecting the most important thermodynamic parameters of the
component being studied to give its maximum achievable efficiency [86]. Although this method
compares the real process with an advanced process, the efficiency improvement is limited to
current technological constraints. Moreover, efficiency limited by technology is not predictable: it
may change over time for a given process [90] as a result of subjective decisions [86].
3.5 Chemical Exergy of Solid Fuels
The chemical exergy of the fuel in question is a basic property to be considered in the system
analysis of energy conversion processes: it is used to estimate the maximum available energy
entering the system for performance evaluation and optimization of the process. However, the
exergy values of some solid fuels with unknown structure and chemical compositions cannot be
determined directly due to the lack of standard absolute entropy values [91]. Hence, many models
have been proposed based on the characteristics of known homogeneous organic substances in the
fuel. Rant [92] was the first person to model the chemical exergy of a structurally complicated
material by using organic substances of known absolute entropies. Future improvement of Rant’s
model was carried out by Szargut and Styrylska [93], who took the elemental composition of the
fuels into consideration. Some elements, such as sulphur and chlorine, however, were not
considered. The use of standard entropy and chemical constituents of solid fuels to model the
chemical exergy have also been investigated specifically for coal conversion [94-97] and biomass
conversion processes [98].
3.6 Research Necessary
The use of an efficient evaluation method to assess the performance of a thermal process,
specifically a waste-to-energy combustion process, may ensure that adequate decisions are taken
towards making improvements in the efficiency of the system. Although some improvements have
been made in this area, previous efficiency evaluation methods have at least one of the following
19
limitations: inability to consider the entropy generated within a system; inability to consider what
improvements are possible in a process; limiting improvements based on the current performance
and technological constraints of the system. In addition, as mentioned previously, the chemical
exergy of solid waste is vital for the performance analysis and optimization of a combustion
process. Here, not only the energy content of the solid waste is considered but also the entropy
generated in the waste conversion process. Previous models have specifically considered the
chemical exergy of solid fuels such as coal and biomass. These models cannot be used for
estimating the chemical exergy of substances containing elements other than C, H, O, N, and S or
for combustible materials, such as certain categories of leather, plastic and rubber, that all form a
part of municipal solid waste.
Two exergy-based methods, applicable to the waste combustion process, are therefore proposed
in this thesis: an exergy model to calculate the maximum available energy in a solid waste fuel and
a modified exergy method to determine the improvement that is possible in a process. Both
methods were applied to a solid-waste fired heat and power plant to evaluate improvements, taking
into consideration the state-of-art-technologies used in the system. Furthermore, the cost and
profitability of different improvement measures were evaluated to ascertain the economic viability
of the process modifications.
20
21
Chapter 4
Improving Efficiency Evaluation Methods
Improving the efficiency of a process is important for the advancement of energy recovery and
productivity; it can only be accurate when a suitable method is used. In this chapter, an overview
is given of exergy analyses used for the efficiency evaluation of solid fuel conversion process.
This is followed by the introduction of methods for estimating the maximum available energy in a
solid waste and calculating improvements possible in waste-to-energy combustion plant.
4.1 Using Exergy as a Method for Evaluating Efficiency
A comprehensive study was carried out on the use of exergy analysis with respect to evaluating,
comparing performance and suggesting improvements for solid fuel-fired heat and power plants
that use coal, biomass and a combination of these feedstocks as fuels (Paper I). The exergy and
energy efficiency, the exergy destruction in each component and the overall process based on the
mass, energy, entropy and exergy balance were compared. This was done by reviewing research
work from the literature, which includes both existing plants and simulated processes.
The various studies of solid fuel-fired plants identify the boiler as the component with the highest
exergy destruction, which is a result of irreversible combustion reactions, and the large temperature
difference between the combustion gas and the feedwater. The results also showed that the overall
exergy efficiency and exergy destruction are lower than the overall energy efficiency and heat loss,
respectively. Table 4.1 shows the exergy destruction, heat loss and entropy generation of different
components in a coal-fired thermal plant [53]. It can be seen that the higher the entropy generated,
the greater the exergy destruction. According to energy analysis, the major energy losses in the
plant were due to heat rejection in the condenser as a result of the large enthalpy difference between
the turbine and the condenser, whereas exergy analysis showed that less than 20 % of the total
exergy destruction is to be found in this component. It substantiates the fact that using the energy
method for efficiency evaluation is bound to lead to misconception, misevaluation and poor
decision-making [47].
22
Table 4.1[53] Results from the analysis of a coal-fired thermal plant. Component Exergy destruction
(kW) Heat Loss (kW)
Entropy Generation (kW/K)
Boiler 73046 12663 3312.0 Turbine 6403 3242 17.2 Air-cooled condenser 1622 33283 3.3 De-aerator 886 71 1.4 LP heater 552 336 2.4 HP heater 759 65 3.7 Boiler feed pump 375 140 0.0 Generator 550 656 0.9 Total 84193 50456 3339.9
An decrease in the amount of excess air, with a reduction in flue gas losses, and an increase in the
temperature of the combustion air have shown to improve both the boiler performance and the
overall exergy efficiency [56, 99]. The boiler’s efficiency can also be enhanced by using a
feedwater heater to reduce the temperature difference between the flue gas inside the boiler and
the working fluid [58].
4.2 Estimating the Chemical Exergy of Solid Waste
Solid waste is a heterogeneous substance in nature, with a complex structure and lacking an exact
value of its chemical exergy. The direct value of the chemical exergy of a material with a
complicated structure is difficult to determine [100] when the standard chemical exergy of a
substance that is in the environment is unavailable. Therefore, the standard chemical exergy of a
substance that is not present in the environment can be evaluated by considering a reaction of the
substance with other substances for which the chemical exergies are known [52,101]. In this
section, a model for calculating the maximum available energy in solid waste is proposed (Paper
II). It is based on the elemental composition of the waste that involves C, H, O, N, S and Cl on a
dry ash-free (daf) basis. The model was obtained from the standard exergy values of waste
combustion products as well as from estimations of the higher heating value and standard entropy
of MSW using statistical analysis. Here, 1 kg of MSW (daf), expressed as 𝐶𝐶m𝐻𝐻n𝑁𝑁p𝑂𝑂q𝐶𝐶𝐶𝐶r𝑆𝑆t, is
considered to undergo complete combustion at standard state under steady conditions to produce
carbon dioxide, water, nitrogen, hydrogen chloride and sulphur dioxide, the standard chemical
exergies of which are known. The standard chemical exergy and standard entropies of various
23
compounds are presented in Table 4.2, while the combustion reaction of the solid waste is given
in Equation (4.1) as follows:
𝐶𝐶m𝐻𝐻n𝑁𝑁p𝑂𝑂q𝐶𝐶𝐶𝐶r𝑆𝑆t + (m + t − q2 + n−r
4 ) O2 → m𝐶𝐶𝑂𝑂2 + (n−r2 ) 𝐻𝐻2O + P
2 𝑁𝑁2 + rHCl + t𝑆𝑆𝑂𝑂2 (4.1)
where m, n, p, q, r and t are the numbers of atoms of C, H, N, O, Cl, and S, respectively, in kmol/kg
MSW.
The maximum work that occurs when there is no irreversibility in a system is obtained from the
energy and entropy balances in Equation (4.1) for the steady state under standard conditions, and
is expressed in Equation (4.2) thus:
𝑊𝑊max = 𝐻𝐻𝐻𝐻𝐻𝐻msw − To [𝑠𝑠mswo + (m + t − q
2 + n−14 ) 𝑠𝑠O2
o − m𝑠𝑠CO2o − (n−1
2 ) 𝑠𝑠H2Oo − p
2 𝑠𝑠N2o − r𝑠𝑠Hcl
o − t𝑠𝑠SO2o ] (4.2)
where 𝐻𝐻𝐻𝐻𝐻𝐻msw is the higher heating value obtained from the heat of reaction of the combustion
process, ∆𝐻𝐻𝑟𝑟𝑜𝑜, presented in Equation (4.3) [96]:
∆𝐻𝐻𝑟𝑟𝑜𝑜 = −𝐻𝐻𝐻𝐻𝐻𝐻 (4.3)
Considering the waste combustion reaction in Equation 4.1 at adiabatic process with no
irreversibility, the exergy balance equation is given in Equation (4.4) as:
0 = 𝑊𝑊max + 𝑒𝑒MSW + (m + t − q2 + n−1
4 ) 𝑒𝑒O2o − m𝑒𝑒CO2
o − (n−12 ) 𝑒𝑒H2O
o − p2 𝑒𝑒N2
o − r𝑒𝑒Hclo − t𝑒𝑒SO2
o (4.4)
where 𝑒𝑒 is the specific chemical exergy. By substituting Equation (4.2) into Equation (4.4), the
specific chemical exergy of MSW (daf), 𝑒𝑒MSW, can be presented as Equation (4.5):
𝑒𝑒MSW = 𝐻𝐻𝐻𝐻𝐻𝐻MSW − To [𝑠𝑠mswo + (m + t − q
2 + n−14 ) 𝑠𝑠O2
o − m𝑠𝑠CO2o − (n−1
2 ) 𝑠𝑠H2Oo − p
2 𝑠𝑠N2o − r𝑠𝑠Hcl
o − t𝑠𝑠SO2o ] + m𝑒𝑒CO2
o +
(n−12 ) 𝑒𝑒H2O
o + p2 𝑒𝑒N2
o + r𝑒𝑒Hclo + t𝑒𝑒SO2
o − (m + t − q2 + n−1
4 ) 𝑒𝑒O2o (4.5)
24
where eo and so are the standard exergy in kJ/mol and the standard entropy in kJ/mol·K,
respectively, and given in Table 4.2.
Table 4.2 Standard chemical exergy and standard entropies of various compounds. Substance e𝑜𝑜(kJ/mol) s𝑜𝑜(kJ/mol·K) CO2 19.87 0.214 H2Ol 0.95 0.070 O2 3.97 0.205 N2 0.72 0.192 SO2 310.93 0.248 SiO2 1.636 0.041 HCl 85.5 0.187 CaO 129.881 0.038 K2O 412.544 0.102 P2O5 377.155 0.117 Al2O5 4.479 0.051 MgO 62.417 0.027 Fe2O3 17.656 0.087 SO3 242.003 0.257 Na2O 296.32 0.075 MnO 122.390 0.060 ZnO 37.080 0.042 Cr 538.610 0.024 Pb 226.940 0.065 As 477.040 0.035 Cd 290.920 0.052 Cl 163.940 0.166
l, liquid phase. Source: [49, 98]
4.2.1 Estimating the Higher Heating Value and Standard Entropy of MSW
The higher heating value of the waste was derived statistically using a regression model based on
the elemental composition of six categories of combustible waste fractions, namely food, wood,
paper, textiles, plastics and rubber. Here, 56 and 30 data points of MSW were used for derivation
and validation of the correlation, respectively. For further validation, the HHV of waste derived
was compared with the experimental values and the previous models.
25
The standard entropy of MSW was derived from organic compounds of known standard entropies
located in the molecular structure of the various waste fractions mentioned earlier. The molecular
structure was obtained from the organic polymers in each of the waste fractions. For instance,
cellulose, hemicellulose and lignin are the three major polymers found in wood waste, in which
organic compounds such as glucose, lactose, galactose, sorbose, sucrose, xylose and
hydroxybenzoic acid are present. Polymers such as proteins and lipids are seen in food waste, in
which urea, lactic acid, malic acid, maleic acid, citric acid and stearic acid, all with known standard
entropies, are found. Based on the standard entropies and the elemental compositions of the
selected organic substances, a correlation was statistically obtained for the standard entropy of the
waste fractions and the mixture.
The models derived for the higher heating value, the standard entropy and the exergy of solid waste
are presented in Equations (4.6), (4.7) and (4.8), respectively.
HHV = 0.364C + 0.863H − 0.075O + 0.028N − 1.633S + 0.062Cl (MJ/kg) (4.6)
𝑠𝑠mswo = 0.0101C + 0.0630H + 0.0106O + 0.0108N + 0.0155S + 0.0084Cl (kJ/K · kg) (4.7)
𝑒𝑒msw = 376.461C + 791.018H − 57.819O + 45.473N − 1536.242S + 100.981Cl (kJ/kg) (4.8)
The results of the validation of the derived model of HHV and comparisons with published
correlations using the experimental values of 30 samples of MSW in the different categories of
food, wood, plastic, textile, rubber and paper waste are depicted in Figures 4.1–4.4. R2, AAE and
ABE denote the coefficient of determination, the average absolute error and average bias error of
a correlation, respectively. These three parameters are the important statistical criteria employed
to assess correlations [102]. A higher R2 value with smaller values of AAE and ABE mean a higher
accuracy of the estimation.
26
Figure 4.1. Comparison between the experimental and Figure 4.3. Comparison between the experimental and estimated HHV using the developed correlation. estimated HHV using the Sheng and Azevedo [104] correlation.
Figure 4.2. Comparison between the experimental and estimated Figure 4.4. Comparison between the experimental and HHV using the Channiwala and Parikh [103] correlation. estimated HHV using Dulong’s correlation [103].
The developed correlation for HHV shows a better estimation than the previous models where the
average absolute error, AAE, and average bias error, ABE are concerned. The ratio of exergies to
heating values of 1.036 of the developed model is similar to the 1.047 obtained from the Szargut
and Styrylska comparison [93], which is the model commonly used for evaluating the chemical
exergy of solid fuels. It shows that the present model is reliable and accurate, while the slight
variation in the ratio is due to the different types of fuel used.
0
10
20
30
40
50
0 10 20 30 40 50
HHVe
st (M
J/kg
)
HHVexp (MJ/kg)
R2 = 0.95AAE = 5.7%ABE = 0.03%
0
10
20
30
40
50
0 10 20 30 40 50
HHVe
st (M
J/kg
)
HHVexp (MJ/kg)
0
10
20
30
40
50
0 20 40 60
HHVe
st (M
J/kg
)
HHVexp (MJ/kg)
0
10
20
30
40
50
0 10 20 30 40 50
HHVe
st (M
J/kg
)
HHVexp (MJ/kg)
R2 = 0.92 AAE= 9.7% ABE = -3.7%
R2 = 0.95 AAE= 11.8% ABE = -4.8%
R2= 0.95 AAE= 6.5% ABE = 2.3%
27
4.3 Improvement Potential Method
The improvement potential provides information of the improvement that is possible to attain in
both the components and the overall process. It gives a more realistic description of the changes
that are possible with respect to the constraints of the conversion pathway selected. In Paper III, a
modified exergy-based method is introduced that links the exergy efficiency to the total exergy
destruction and compares the exergy destruction of the real process with its theoretical process. It
is presented in Equation (4.9) thus:
𝐼𝐼𝐼𝐼k = ��𝐸𝑥𝑥Dk,rp − (1−𝜂𝜂𝑒𝑒𝑒𝑒,𝑡𝑡𝑡𝑡)𝜂𝜂𝑒𝑒𝑒𝑒,𝑡𝑡𝑡𝑡
��𝐸𝑥𝑥Pk,rp (4.9)
where ��𝐸𝑥𝑥Dk,rp and ��𝐸𝑥𝑥Pk,rp are the exergy destruction and product exergy, respectively, of a
particular component in the real process and (1−𝜂𝜂ex,tp)
𝜂𝜂ex,tp is the exergy destruction per unit of product
exergy under theoretical conditions.
The theoretical process can be determined when the optimal values of the real process have been
reached. It is achieved by using the parameters of each component of the process plant that give
its maximum efficiency without being limited technologically by the cost and properties of the
materials used in the system. These parameters are subject to constraints: the combustion process
in the boiler that converts the chemical energy in the fuel into thermal energy in its heat exchanger,
and a Rankine cycle that converts the energy in the steam into electricity and district heat. In the
boiler combustion process, the adiabatic flame temperature (the maximum temperature that can be
achieved for the given fuel) is chosen and used to optimize the overall system based on variations
in steam pressure and extraction pressures. Although it is not anticipated that technological
enhancements will reach their theoretical limits the latter do, however, provide information of the
progress that is possible and the improvements that are necessary in the former.
28
29
Chapter 5
Evaluating Efficiency Improvement
The modified exergy-based method, Equation 4.9, was used to evaluate the improvement potential
of both the individual components and the overall base case plant. The results were then used to
decide upon which technical modifications should be introduced.
5.1 The Base Case Plant
The base case plant is a typical grate boiler of waste-to-energy combined heat and power plant. A
schematic diagram of the plant is shown in Figure 5.1, while Figure 5.2 shows the Aspen Plus
model of the process plant.
Figure 5.1 Schematic diagram of the municipal heat and power plant fired by solid waste.
30
Figure 5.2. The base case plant, modelled in Aspen Plus.
The plant, the design data of which is reported in Table 5.1, has a waste fuel input of 100 MWth
with a lower heating value and a moisture content of 11.6 MJ/kg and 33.1 %, respectively. It
consists of two air heaters (employing steam), a boiler with a combustion section and a heat
exchanger section, a turbine, a condenser, a condensate pump, a feed-water pump, a de-aerator and
a feed-water heater. The chemical analysis of the waste fuel in weight percentage, calculated on
a dry basis (db) is as follows: (C: 46.2); (H: 6.1); (O: 28.03); (N: 1.1); (S: 0.2); (Cl: 0.47) and (Ash:
17.9) [105, 106].
Table 5.1 Design parameters of the plant.
Parameter Value Fuel energy input (MW) 100 Steam pressure (bar) 50 Steam temperature (oC ) 420 Extraction pressure in HPT (bar) 10 Extraction pressure in IPT (bar) 5 Extraction pressure in LPT (bar) 2.5 Condensation pressure (bar) 1 Flue gas recirculation (%) 20 Excess air (%) 39 Stack temperature (°C) 160
DECOMP
BC
SPLIT1
ECO
SPLIT2SPLIT3
SPLIT4 SPLIT5MIX1
COND
CP
FWHDRT
SSAH PSAH
MIX2
FWP
SPLIT6
EVA SH
STDRUM
HPT IPTLPT1 LPT2
MSW2
Q-DECOMP
FG1
ASH
FG5
W12
FG2
AIR1
S5
S4
S8
S7
S10 S13
S12
SW2
W7SW3
W3W4
W5
W8
W9
W6
W10
AIR2
S9
W1
W2
W11
AIR3
FG6
FGOUTFG3
FG4
SW1
S2
W13
MSW1
S3 S6
S11
FUEL(MSW)
WATER(W)
STEAM(S)STEAM/WATER (SW)
AIRFLUE GAS (FG)
ASH
Combustion Process
S1
31
The theoretical process of the plant was obtained using the adiabatic flame temperature of the
waste fuel, calculated as 1677 °C, operating under stoichiometric air conditions in the boiler
combustor. For the boiler heat exchanger and other heat exchangers of the plant, a pressure drop
of zero and a minimum temperature difference of 0.1 °C was assumed. An isentropic efficiency of
100 % was assumed for the pumps and the steam turbine.
The results as determined from Equation (4.9) show that 64 % of the improvement possible in the
overall process plant can be improved in theory: the boiler was identified as being the component
with the greatest potential for making improvements in the plant. However, constraints in the
combustion process and Rankine cycle mean that the efficiency improvement of the boiler and the
overall process with exergy efficiencies of 37 % and 25 % will never excess 62 % and 56 %,
respectively (Figure 5.3).
Figure 5.3. The exergy efficiency of the boiler and overall process plant.
Figure 5.4 shows that the steam turbine and condensate pump have the highest potential for
improvements when compared with their theoretical processes. Here, 100 % of the theoretical
efficiency can be achieved, which means there is a possibility of utilizing the total exergy
destructions in these components.
0
20
40
60
80
100
120
BCP TP
Exer
gy e
ff.(%
)
Overallprocess
Boiler
32
Figure 5.4. Percentage of the improvement potential of the components in the base case process plant.
5.2 Different Efficiency Improvement Methods
The different improvement modifications were applied in the base case plant based on the
component with the highest potential for improvement, taking into consideration the state-of-the-
art technology in the system investigated (Paper III). These involve changing the bed material to
reduce the amount of excess air, an integrated gasification-combustion process and the
introduction of a reheater. In addition, the re-arrangement of air heaters and flue gas condensation
to reduce the loss of sensible and latent heat by the flue gas through the stack were also considered.
The results of the various improvement modifications investigated for combined heat and power
production, and electricity production alone, are shown in Tables 5.2 and 5.3.
0
10
20
30
40
50
60
70
80
90
100
BC ST COND CP FWP FWH DRT PSAH SSAH
IPk
(%)
33
Table 5.2. Evaluation of the improvement in efficiency in a combined heat and power plant. Parameter Unit BCP M1 M2 M3 M4 M5 M6 M7 Base
case plant
Excess air reduction
Flue gas conden-sation
High steam parameter plus reheater
M1+M2+M3 Waste gasification plus gas boiler
M5+flue gas conden-sation
Changing the medium for pre-heating air
Electricity production
MW 18.72 18.81 18.72 21.73 21.73 24.16 24.16 18.52
District heating production
MW 58.97 60.07 67.49 55.90 66.40 58.38 61.53 60.84
Boiler exergy effic. % 36.8 37.0 36.8 39.8 41.1 50.5 50.5 37.3 Boiler energy effic. % 81.8 83.0 81.8 81.8 82.2 85.0 85.0 83.0 Exergy effic. % 25.2 25.5 26.3 27.4 28.8 30.1 30.5 25.3 Energy effic. % 77.0 78.5 86.1 77.0 87.4 81.8 84.9 79.0 Exergy loss MW 2.1 2.0 1.5 2.1 1.3 1.6 1.4 1.6
Table 5.3. Evaluation of the improvement in efficiency in a power plant. Parameter Unit BCP M1 M3 M5 M7 Base case
plant Excess air reduction
High steam parameter plus reheater
Waste gasification plus gas boiler
Changing the medium for pre-heating air
Electricity production MW 24.7 25.0 27.5 30.1 24.7 Exergy efficiency % 23.5 23.7 25.8 28.4 23.5 Exergy efficiency increment % - 0.8 10 21 - Energy efficiency % 24.3 24.5 26.7 29.4 24.3 Energy efficiency increment - 0.8 10 21 -
Flue gas condensation proved to be the best option for enhancing the efficiency of the district
heating process for a combined heat and power plant. The improvement modification that involved
changing the medium for heating the air from steam to flue gas is the best method for the
production of heat without flue gas condensation. The integrated gasification-combustion and
reheating processes were found to be the best options, having the highest exergy efficiency for the
production of electricity alone. However, the improvement alternatives have to be balanced against
the higher capital, maintenance and operating costs of the equipment.
34
35
Chapter 6
Economic Evaluation of Efficiency Improvements
The fact that waste-to-energy plants are capital intensive necessitated an economic evaluation of
both improvement and productivity. Evaluation of the costs and profitability of improvements
made in a waste-combustion plant enables informed decisions to be made of the various
enhancement options with respect to the economic viability of the system. In Paper IV, the cost
and profitability of different modifications aimed at improving efficiency in a waste-to-energy
plant are considered: these include the re-arrangement of air heaters, the introduction of a reheater,
flue gas condensation (FGC) and an integrated gasification-combustion process. Table 6.1
provides detailed improvement modifications for the base case plant. The base case and the various
modifications are evaluated and compared when operating either as a combined heat and power
plant or just as a power plant. Modelling, simulation and cost estimations were performed with the
Aspen Plus software.
Table 6.1 The parameters of the base case plant and the different improvement modifications made. Variables Unit BCP M1 M2 M3 M4 M5 M6 M7 Base
case plant
Flue gas Condensation (FGC)
High steam parameter + reheater
M2 + FGC
Waste gasification + gas boiler
M4 + FGC
Changing the medium for pre- heating air
M6 + FGC
Energy input MW 100 100 100 100 100 100 100 100 Extraction press. HPT bar 10 10 14 14 10 10 10 10 Extraction press. IPT bar 5 5 5 5 5 5 5 5 Extraction press. LPT bar 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 Flue gas recirculation % 20 20 20 20 20 20 20 20 Excess air % 39 39 39 39 5 5 39 39 Stack temperature o C 160 110 160 110 160 110 130 110 Steam temperature o C 420 420 440 440 540 540 420 420 Steam pressure bar 50 50 130 130 121 121 50 50 Reheat steam temp. o C - - 320 320 - - - - Reheat steam press. bar - - 14 14 - - - - Sources: BCP is from the design data of a typical waste plant; M2 and M3 are modified from the operation conditions of Afval Energie Bedrijf, Amsterdam [45]; M4 and M5 are modified from the operation conditions of a waste gasification plant in Lahti, Finland [17].
35
Chapter 6
Economic Evaluation of Efficiency Improvements
The fact that waste-to-energy plants are capital intensive necessitated an economic evaluation of
both improvement and productivity. Evaluation of the costs and profitability of improvements
made in a waste-combustion plant enables informed decisions to be made of the various
enhancement options with respect to the economic viability of the system. In Paper IV, the cost
and profitability of different modifications aimed at improving efficiency in a waste-to-energy
plant are considered: these include the re-arrangement of air heaters, the introduction of a reheater,
flue gas condensation (FGC) and an integrated gasification-combustion process. Table 6.1
provides detailed improvement modifications for the base case plant. The base case and the various
modifications are evaluated and compared when operating either as a combined heat and power
plant or just as a power plant. Modelling, simulation and cost estimations were performed with the
Aspen Plus software.
Table 6.1 The parameters of the base case plant and the different improvement modifications made. Variables Unit BCP M1 M2 M3 M4 M5 M6 M7 Base
case plant
Flue gas Condensation (FGC)
High steam parameter + reheater
M2 + FGC
Waste gasification + gas boiler
M4 + FGC
Changing the medium for pre- heating air
M6 + FGC
Energy input MW 100 100 100 100 100 100 100 100 Extraction press. HPT bar 10 10 14 14 10 10 10 10 Extraction press. IPT bar 5 5 5 5 5 5 5 5 Extraction press. LPT bar 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 Flue gas recirculation % 20 20 20 20 20 20 20 20 Excess air % 39 39 39 39 5 5 39 39 Stack temperature o C 160 110 160 110 160 110 130 110 Steam temperature o C 420 420 440 440 540 540 420 420 Steam pressure bar 50 50 130 130 121 121 50 50 Reheat steam temp. o C - - 320 320 - - - - Reheat steam press. bar - - 14 14 - - - - Sources: BCP is from the design data of a typical waste plant; M2 and M3 are modified from the operation conditions of Afval Energie Bedrijf, Amsterdam [45]; M4 and M5 are modified from the operation conditions of a waste gasification plant in Lahti, Finland [17].
36
6. 1 Cost of Improving Efficiency
The cost of improving efficiency compares the enhancement made to improve the efficiency of a
process with its capital cost increment. It is determined as the ratio of the increment in the capital
cost to the increment in the exergy efficiency (Paper IV). The results for the ratio of capital cost
increment to increment in exergy efficiency and capital per total revenue for the base case plant
and the different improvement alternatives investigated are shown in Figure 6.1.
Figure 6.1. The ratio of the increment in the capital cost to the increment in the exergy efficiency and capital cost per total revenue.
Modification 6, which involves re-arrangement of the air heating medium (from steam to flue gas),
is the best option for improvement with regards to the lowest capital cost increment per unit
increase in efficiency: here, 0.6 % decrease in the capital cost of the base case plant was obtained.
The improvement modifications involving waste gasification processes (M4 and M5) have the
highest exergy efficiency as a result of increasing the steam properties to 540 oC and 121 bar from
420 oC and 50 bar. However, the capital cost for making improvements to efficiency and the total
revenue earned in these modifications exceed other alternatives. Modifications 1 and 7, which
involves the integration of flue gas condensation in the base case plant and the plant in which the
air heater was re-arranged, are the best options for capital cost per total unit of revenue generated.
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
M1 M2 M3 M4 M5 M6 M7
Ratio
of c
apita
l cos
t to
exer
gy e
fficie
ncy
incr
emen
t [−]
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
M1 M2 M3 M4 M5 M6 M7
Capi
tal c
ost/
Tota
l rev
enue
[−]
37
6. 2 Profitability Evaluation
Figure 6.2 shows the net present value (NPV) and the internal rate of return (IRR) of the process
plants investigated.
Figure 6.2. Net Present Value (NPV) and Internal Rate of Return (IRR) for the base case and the various modifications.
Modifications 1 and 7, with the highest NPV and IRR, are more economically viable than other
improvement alternatives. This can be attributed to the fact that flue gas condensation is integrated
in them, which contributes to the enhancement of heat production and the overall income earned.
Modifications 2, 4 and 5 have lower NPV and IRR than the base case plant, i.e. the base case plant
is seen to be more economically viable, and hence they are not the best options for improving the
efficiency of the plant investigated. It does show, however, that they are in fact profitable because
the NPV is positive. Further economic evaluation was conducted on the different improvement
methods for waste plants producing electricity only. The results show that these process plants
have a negative NPV (Figure 6.3), indicating that they are not economically feasible. This
illustrates that a district heating network contributes significantly to the profitability of a waste-to-
energy plant.
0
50
100
150
200
250
BCP M1 M2 M3 M4 M5 M6 M7
Net P
rese
nt V
alue
(M$)
0
2
4
6
8
10
12
14
BCP M1 M2 M3 M4 M5 M6 M7
Inte
rnal
rate
of r
etur
n (%
)
38
Figure 6.3. Net Present Value for plants producing electricity only.
-300
-250
-200
-150
-100
-50
0BCP M2 M4 M6
NPV
(M$)
39
Chapter 7
Validation
The results obtained from both the method developed and the waste-to-energy combustion plant
investigated can be compared with the following studies:
The ratio obtained for the specific exergy of municipal solid waste with the higher heating
value was 1.036 (Paper II). This is similar to the ratio of 1.047 calculated using the Szargut
and Styrylska [93] model, which is the method most commonly used for estimating the
specific chemical exergy of solid fuels.
The average exergy efficiency of waste-to-energy combustion plants in Sweden was
calculated as being 26 % by Grosso et al. [80], which is comparable to the 25 % exergy
efficiency obtained from the plant investigated in this thesis (Paper III and IV).
The capital investment cost of $ 176 million obtained for an input of 27 ton/h of waste used
in the base case plant (Paper IV) is comparable with the investment cost estimated between
$ 145 and $ 207 million for a plant with a waste combustion capacity of between 25 and
35 ton/h fuel input [17].
The internal rate of return (IRR) of 12.21 % calculated from one of the modified process
plants in Paper IV is in agreement with the 12.21 % and 12.22 % obtained by Udomsri et
al. [107] and Zhao et al. [108], respectively, for a waste-to-energy plant with a similar
arrangement.
The results for the economic evaluation of waste-to-energy plants producing only
electricity showed that gate fees have a significant effect on the economic viability of the
process (Paper IV). This is also in agreement with the results obtained from previous
economic assessments of energy recovered from solid waste [109].
40
41
Chapter 8
Conclusions
In this thesis, two modified exergy-based methods for calculating the maximum energy in solid
waste and for evaluating the improvements that are possible in a process have been presented. The
two methods are very important for the performance analysis and optimization of the waste
combustion process, assessing the efficiency of improvements and improving the profitability of
a waste-to-energy combustion plant. The main findings of this thesis are:
The exergy method for evaluation of the efficiency of a process is superior to the energy
method and determines the destruction of exergy. Used alone, however, the exergy method
does not provide information of possible improvements that may be made in a system.
The model developed for estimating the maximum energy available in municipal solid
waste also takes the main elemental compositions of the waste into consideration, and is
more accurate when compared with other models.
The modified exergy-based method introduced provides a more realistic description of the
changes that are possible with respect to the constraints of the conversion pathway selected.
It identifies the maximum limits for improving the efficiency of the system and provides
information of improvements that may not be changed for a given process.
A district heating network generates the most profitable source of income for waste process
plants: it has a significant impact on the viability of the project over the expected range of
variations in both level of income and production costs.
The improvement methods that have been identified as having a high degree of efficiency
enhancement do not necessarily make them more economically feasible than other
alternatives. Economic viability is imperative for the efficient evaluation of possible
improvements that may be made to a system.
42
43
Chapter 9
Future Work
The methods for evaluating efficiency introduced in this work can be applied to the further
investigation of possible improvements that may be made in the waste-to-energy combustion
process, as they consider the uncertainty of the composition of waste and its energy input as well
as the effects of the formation and deposition of ash. The possibility of wider applications of these
methods should also be investigated. Recommendations for future research are as follows:
A waste-to-energy plant experiences variations in both the input and composition of fuel
over time. Therefore, further studies can be carried out that apply the methods developed
for dynamic modelling and simulation in a waste-to-energy plant so as to account for
changes in the load and heating value of the solid waste.
The high concentration of alkaline metals and chlorine present in waste fuel can lead to the
formation of deposits at low melting points [110]. This can cause an increase in corrosion
of the heat exchange tubes, leading to high maintenance costs and a reduction in the
availability of the plant. The transfer of heat to the working fluid will also be hindered,
thereby reducing the overall efficiency of the system [111]. The two modified exergy
methods introduced in this thesis can be applied further to investigate the impact of ash
deposits on the boiler and the overall process by evaluating not only the energy available
in the waste material but also the improvement potential in the boiler’s heat exchangers
and the overall process plant.
The improvement potential introduced in this study can be applied to systems other than
waste-to-energy combustion processes in order to determine the improvements that can be
made to enhance the efficiency of the processes.
44
45
Acknowledgements
Studying for this Ph.D. was like embarking on a journey. It started with a single step guided by
the almighty God and continued, with the help and contributions of the following people and
organizations, towards its completion and actualization.
My sincerest thanks are extended to my main supervisor, Prof. Tobias Richards, for all of your
support, guidance and advice. Your availability and readiness to render assistance during this
period is greatly appreciated. I am blessed to have someone like you as a supervisor, and can
proudly say that you are one in a million!
Thank you Assoc. Profs. Peter Ahlström and Anita Pettersson, my co-supervisors, for the
discussions and time we spent together. Thank you, Peter, for your introduction to advanced
thermodynamics and you, Anita, for opening my eyes to the valuable resources in solid waste.
Thank you too, my examiner Prof. Bengt-Åke Andersson for your suggestions and encouragement.
I have really learnt a lot from your practical knowledge of combustion technology and waste-to-
energy plants.
My deepest gratitude goes to Prof. and Mrs. Kayode Adekunle. Thank you both so much for the
effort you made prior to my arrival to Sweden and for the hospitality you showed when I arrived.
God will continue to bless your family.
I am indebted to The Michael Okpara University of Agriculture, Umudike, Nigeria; The Tertiary
Education Trust Fund (TeTFund) Nigeria and The University of Borås for their financial support
and for providing an enabling environment in which to conduct this research work.
Special acknowledgement is due to Sari Sarhamo for the friendly reception she gave me when I
started my studies. My appreciation extends to the following university staff at the Swedish Centre
for Resource for their support and encouragement: Prof. Mohammad Taherzadeh (Director of
Graduate School), Dr. Thomas Wahnström (Director of Studies for doctoral programme), Dr. Peter
46
Therning (Head of Department), Louise Holmgren (economist), Thomas Södergren, Susanne
Borg, Jonas Edberg, Irene Lammassaari, Jonas Andersson, Camilla Carlsson, Dr. Patrik
Lennartsson, Dr. Kamran Rousta, Dr. Päivi Ylitervo, Prof. Mikael Skrifvars, Prof. Kim Bolton,
Prof. Staffan Svensson, Dr. Akram Zamani, Dr. Lars-Erik Åmand and Dr. Tariq Bashir.
The following university lecturers are thanked for sharing their knowledge and ideas with me
during this period: Prof. Jan Nolin, Prof. Gustaf Nelhans, Prof. Christine Räisänen, Prof. Lars
Sandman, Prof. Dragu Atanasiu, Dr. Helena Francke, Dr. Magnus Lundin, Sigrid Dentler and
Bengt-Erik Larsson.
I am very grateful to the following former and current Ph.D. students at the University of Borås:
Dr. Mofuluwake Ishola, Dr. Fatima Bakare, Dr. Sunil Kumar Ramamoorthly, Dr. Abas
Mohsenzadeh, Dr. Julius Akinbomi. Dr. Karthik Rajendran, Dr. Jorge Ferreira, Dr. Behnaz
Baghaei, Dr. Kehinde Oluoti Olubukola, Dr. Farzard Moradian, Dr. Ramkumar Nair, Dr. Regina
Jijoho Patinvoh, Dr. Osagie Alex Osadolor, Dr. Pedro Ferreira de Souza Filho, Dr. Veronika
Batori, Adib, Mostafa, Konstantinos, Amir, Lukitawasa, Andrea, Madumita, Steven, Rebecca,
Pedro, David, Supriyanto, Gülru, Anette, Mohsen, Sofie, Hanieh and Danh, along with others. I
also extend my acknowledgement to the visiting Ph.D. students from other institutions, namely
Sindor, Sharareh, Shilan and Moyinoluwa. I really appreciate the time we have shared together.
Special thanks go to my friend, Dr. Martin Bohlen, who always has a smile on his face.
To my wonderful office colleagues in the neighbouring room (D414-D417), Dr. Faranak
Bazooyar, Marlen Kilberg, Ville Skrifvars and Jonas Hansson: thank you for the great times we
had together.
Thank you, Maureen Sondell, for your efforts and willingness in undertaking the language revision
of my manuscripts and thesis despite short notice and a busy schedule.
To Anna Rieck at Borås Energi och Miljö AB: thank you for helping me with data information of
the plant.
47
I am also grateful to Fr. Mike, Fr. Chigozie, Fr. Livinus and all the members of St. Sigfrids Catholic
Church in Borås. Thank you for sharing your prayer life and faith with me.
My deep gratitude goes to the families of Christian and Obioma for being wonderful and lovely
friends to my family during this PhD studies.
To my wonderful parents, the Late Chief Gilbert Ebo and Mrs Beatrice Ebo: thank you so much
for believing in me and giving me the best in life, both upbringing and education. I am extremely
grateful to my siblings, Engr. Geoffrey Eboh, Engr. Raphael Ebo, Mr. Cyril Ebo, Mrs. Nnenna
Agu and Mrs. Amarachukwu Okoyeagu, for their prayers, love and support. I am also grateful to
my in-laws, Hon. Donatus Ogbonna and his family, for their care and understanding.
Finally, my deepest gratitude goes to my beloved wife, Joy Chika. Thank you for always being
there for me. Not only have you waited patiently for me until the end of my Ph.D. programme, you
have also continued to encourage me during difficult moments. You are my valued treasure, one
that indeed cannot be bought. Thank you so much, my dearest, and remain blessed.
48
49
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Pape
r I
energies
Review
Exergy Analysis of Solid Fuel-Fired Heat and PowerPlants: A ReviewFrancis Chinweuba Eboh 1,2,*, Peter Ahlström 1 and Tobias Richards 1
1 Swedish Centre for Resource Recovery, University of Borås, 501 90 Borås, Sweden;[email protected] (P.A.); [email protected] (T.R.)
2 Department of Mechanical Engineering, Michael Okpara University of Agriculture,Umudike P.M.B 7267, Abia State, Nigeria
* Correspondence: [email protected]; Tel.: +46-33-435-5903; Fax: +46-33-435-4008
Academic Editor: Xidong WangReceived: 16 November 2016; Accepted: 19 January 2017; Published: 1 February 2017
Abstract: The growing demand for energy is particularly important to engineers with respect tohow the energy produced by heat and power plants can be used efficiently. Formerly, performanceevaluation of thermal power plants was done through energy analysis. However, the energy methoddoes not account for irreversibilities within the system. An effective method to measure andimprove efficiency of thermal power plant is exergy analysis. Exergy analysis is used to evaluate theperformance of a system and its main advantage is enhancement of the energy conversion process.It helps identify the main points of exergy destruction, the quantity and causes of this destruction,as well as show which areas in the system and components have potential for improvements.The current study is a comprehensive review of exergy analyses applied in the solid fuels heatand power sector, which includes coal, biomass and a combination of these feedstocks as fuels.The methods for the evaluation of the exergy efficiency and the exergy destruction are surveyed ineach part of the plant. The current review is expected to advance understanding of exergy analysisand its usefulness in the energy and power sectors: it will assist in the performance assessment,analysis, optimization and cost effectiveness of the design of heat and power plant systems inthese sectors.
Keywords: exergy; heat and power; solid fuels; system efficiencies
1. Introduction
The worldwide demand for, and consumption of energy and power are expected to increasein future years due to the expansion of urbanization, the rapid rate of industrialization and thecontinuous improvements being made to the standard of living [1,2]. Humankind currently uses410 × 1018 joules per annum, which is equal to the energy content of over 90,000 billion litres ofoil, i.e., commercially-traded energy [3]. Nevertheless, the rate of consumption is constrained by theavailable resources [4,5] and a consequence of energy sources being limited is that their efficientuse requires thermal processes to be optimized, with special emphasis being placed on the energyassociated with exhaust gases and other forms of waste heat [6]. The energy sector needs not only tobe effective in order to meet the increasing demands for heat and electricity from society, but also toutilize the resources that are available for their production by improving the efficiency of plants.
Normally, performance assessment of a system is carried out using the concept of the first law ofthermodynamics, which is based on the conservation of energy [1,7]. However, using energy alonein the efficiency analysis of processes is bound to lead to misconceptions, misevaluations and poordecisions [8]: it only embraces information of the inputs and outputs of the energy in the processand excludes its quality [9]. The use of second law analysis allows the quality of the energy to be
Energies 2017, 10, 165; doi:10.3390/en10020165 www.mdpi.com/journal/energies
Energies 2017, 10, 165 2 of 29
determined, and the irreversibilities quantified, as a result of the entropy that is generated and whichcauses inefficiency in the process [1]. Second law analysis is often based on the concept of “exergy”,also known as “available energy”, “availability” or “useful energy” [8]. This enables the main sourcesof loss to be identified and provides directions for improving performance within the system [7].
Exergy is the maximum amount of work that can be obtained from a stream of matter, heator work as it is brought into equilibrium with the environment [10]. The reference environment oftemperature, pressure and mixture of substances found in abundance in nature must be defined: it isgiven a zero exergy (i.e., “dead state”) [11]. Exergy analysis has been widely used in the evaluation,simulation and design of thermo-chemical and thermo-mechanical systems [12]. Its application reachesbeyond technical analysis, as it is also used in thermo-economic, environmental and sustainabilityanalyses of industrial systems [13]: exergy analysis allows for the thermodynamic assessment of energyconservation because it provides the tool for making a clear distinction between the energy lost to theenvironment and internal irreversibilities in the process [6]. It also represents quantitatively the usefulenergy, i.e., the work content of the great variety of streams (comprising mass, heat, work), that entersinto the system and accounts for the exergy destroyed during a process, which is proportional to theentropy generated [14]. This destruction of exergy, or irreversibility, is a yardstick by which losses inthe plant are determined and compared [7].
The energy in solid fuels can be converted into useful products through biological/biochemicaland thermochemical processes [15,16]. In terms of faster reaction rate and reduction in larger amountand volume of solid materials, the thermochemical processes are more efficient than the biologicalmethods [17]. The three main thermochemical conversion methods are combustion, pyrolysis andgasification [18]. The combustion process is a commercially viable option [19] and the most widelyused method for solid fuels conversion into heat and power [15,20].
In the literature, only a few papers have taken a comprehensive view of exergy analysis on thebiomass-based fuels and coal-fired heat and power plant. Saidur et al. [21] reviewed the application ofexergy analysis to different biomass fuels. Their investigations were based on biomass gasificationrather than conversion through combustion processes. Kaushik et al. [18] examined energy and exergyanalysis of coal-fired thermal power plants. Though their studies were based on thermochemicalconversion of the fuel using a combustion method, they only examined the design conditions ofexisting plants. To the best knowledge of the authors, there is no review of exergy analysis ofbiomass and coal co-fired heat and power plants. Therefore, the aim of this study is to review exergyanalysis in the heat and power sector, with respect to comparing performance, making assessments of,and suggesting improvements for, coal-fired, biomass-fired and co-fired coal and biomass heat andpower plants.
2. Exergy Analysis
The aim of exergy analysis is to detect and evaluate the thermodynamic imperfections ofa process quantitatively and indicate possible ways of improving it [22]. Thermodynamic imperfection,or irreversibility, is a function of the generation of entropy. Exergy analysis enables system designersand engineers to identify the parts with the highest entropy generation, providing them with keypoints on which to focus: they can then increase the efficiency of the system and, simultaneously,lower the negative impact exerted on the environment [23]. Achieving these two objectives involvesmaking evaluations of, and optimizing, each component in the entire system using the mass, energy,entropy and exergy flow. For a steady-state process, these balances are expressed below [23,24].
The mass balance equation can be written in the rate form of Equation (1):
∑i
mi = ∑e
me (1)
where mi and me are the mass flow rates of the fluid entering and exiting the system, respectively.
Energies 2017, 10, 165 3 of 29
The energy rate balance for a steady-state system is expressed as Equation (2):
∑i
Ei + Q = ∑e
Ee + W (2)
where the energy rate entering and exiting the system is Ei and Ee, respectively, Q is the heat rate into
the system and W is the work transfer rate performed by the system.The entropy rate balance equation is given by Equation (3):
∑i
Si + ∑j
Qj
Tj+ Sgen = ∑
eSe (3)
where S is the entropy rate of a flow and Sgen is the entropy generation rate.The exergy rate balance for a system is calculated using Equation (4):
∑i
Exi + ∑j
[1 − T0
Tj
]Qj = ∑
eExe + W + I (4)
where Qj is the heat transfer rate at temperature Tj through the boundary at position j, I is the exergydestruction rate and T0 is the temperature of the reference environment.
Rearranging the exergy balance in Equation (4), the irreversible (or exergy destruction) rate canbe expressed by Equation (5) thus:
I = ∑i
Exi − ∑e
Exe + ∑j
[1 − T0
Tj
]Qj − W (5)
Assuming there are no heat losses since the insulation of each component in the system is good, theexergy associated with the heat transfer rate in the components is zero [25], and Equation (5) becomes:
I = ∑i
Exi − ∑e
Exe − W (6)
The exergy destruction rate due to irreversibility in a system can also be given as Equation (7):
I = T0Sgen (7)
The exergy rate associated with a flowing stream of matter contains physical, chemical, kineticand potential exergy [26] according to Equation (8):
Ex = m(ex) = m(
exph + exch + exke + expe)
(8)
where exke and expe are exergy due to kinetic and potential energy, respectively, exph is the specificphysical exergy and exch is the specific chemical exergy. Assuming that the changes in velocity andelevation of the flowing stream are negligible, then exke and expe can be discarded in the calculation ofchanges in exergy. The exergy flow rate of a stream is shown in Equation (9):
Ex = m(ex) = m(
exph + exch)
(9)
Energies 2017, 10, 165 4 of 29
The specific physical exergy, exph is exergy due to the differences in the temperature and pressureof a system with respect to the reference environment [27]: it can be expressed by Equation (10) thus:
exph = (h − h0)− To(s − s0) (10)
where h0 and s0 are the specific enthalpy and the entropy respectively at the temperature of thereference environment.
The specific chemical exergy, exch depends on the chemical composition of a substance in itsparticular state, and if it is in equilibrium with the reference environment [28]. For solid fuels,the specific chemical exergy can be estimated based on the elemental compositions of the fuel [29–37].
The second law of efficiency or exergy efficiency, ηex, of any system can be defined as the ratioof the exergy transfer rate associated with the output to the exergy transfer rate associated with theinput of the system [3]. It is the best variable for evaluating the performance of a thermal system andits components [38], and is expressed here in Equation (11):
ηex =Exp
Exf
= 1 − I + ExL
Exf
(11)
where Exf represents the fuel exergy rate, while Exp and ExL represent the exergy rate of the productand the rate of exergy loss from the system. If the heat losses from the components are neglected, thenthe exergy loss is zero [38] and Equation (11) can be rewritten as Equation (12):
ηex = 1 − I
mfexf(12)
The first law efficiency or energy efficiency of a system is defined as the ratio of energy outputrate to the energy input rate to system and is calculated using Equation (13) [18].
ηe =Eo
Ei
= 1 − EL
Ei
(13)
where Eo and Ei are energy output and energy input rate respectively. EL is the rate of energy loss.
3. Cycle Analysis of a Solid Fuel Fired Power Plant Using the Exergy Method
The performance evaluation of the whole plant is done based on the components of the system.A detailed overview of the methods used for exergy analysis of each component of the solid fuel plantis given here using a modified Rankine cycle incorporated with feedwater heaters.
Generally, solid based fuels operate under thermodynamic cycles by using a working fluid invapour form for the generation of power known as the “vapour power cycle” or the Rankine cycle.This cycle consists of four processes: reversible adiabatic pumping, constant-pressure heat transferin the boiler, reversible adiabatic expansion in the turbine and constant-pressure heat transfer in thecondenser [39]. Modifications of the Rankine cycle for optimal performance lead to the formation ofthe reheat, superheat and regenerative cycles, with the addition of feed water heaters and de-aerators.A process flow diagram of an advanced, modified Rankine cycle based on a solid-fuel combined heatand power plant is shown in Figure 1 [40], and includes both the heating of feedwater and reheatingof steam.
Exergy analysis is applied to each component of the plant in order to evaluate the system’sperformance at steady state. The parts of the plant in question are: the combustion part of the boiler,heat exchangers in the boiler (HE), high pressure turbine (HPT), intermediate pressure turbine (IPT),low pressure turbine (LPT), condenser (Cond), condensate extraction pump (CEP), open feed water
Energies 2017, 10, 165 5 of 29
heater (OFWH), feed water pump 1 (FP1), closed feed water heater (CFWH) and feed water pump 2(FP2). A detailed analysis is obtained by considering the mass, energy, entropy and exergy flow rate inthe control volume of each individual system as well as the overall plant.
Figure 1. Flow diagram of solid fuel-fired heat and power plant model (modified from [40]).
3.1. Boiler
The boiler is divided into two parts: the combustor and the heat exchanger [12], as presented inFigure 1. If these are both assumed to be adiabatic, operating at steady state with negligible changesin their kinetic and potential energy, then each analysis in the boiler can be made by considering themass, energy, entropy and exergy balance using the input and output conditions of the flows.
3.1.1. Boiler Combustor (C)
The exergy balance rate in the boiler combustor is given in Equation (14):
mfexf + maexa = mhpexhp + mashexash + IC (14)
The exergy destruction rate for the boiler combustor is then calculated by Equation (15) thus:
IC = mf(hf − T0sf) + ma(ha − T0sa)− mhp
(hhp − T0shp
)− mash(hash − T0sash) (15)
The exergy efficiency is expressed as Equation (16):
ηex,C =mhpexhp
mfexf(16)
3.1.2. Boiler Heat Exchanger (HE)
The exergy balance rate for the boiler heat exchanger can be written as Equation (17):
mhpexhp + m1ex18 +(
m1 − m2
)ex3 = mfgexfg + m1ex1 +
(m1 − m2
)ex4 + IHE (17)
Energies 2017, 10, 165 6 of 29
The exergy destruction rate is:
IHE = mhp
(hhp − T0shp
)+ m1(h18 − T0s18) +
(m1 − m2
)(h3 − T0s3)
−mfg
(hfg − T0sfg
)− m1(h1 − T0s1)−
(m1 − m1
)(h4 − T0s4)
(18)
The exergy efficiency is given as:
ηex,HE =m1(ex1 − ex18) + (m1 − m2)(ex4 − ex3)
mfg
(exhp − exfg
) (19)
The overall boiler exergy efficiency is:
ηex,B =m1(ex1 − ex18) + (m1 − m2)(ex4 − ex3)
mfexf(20)
3.2. High Pressure Turbine (HPT)
The exergy balance rate for the high pressure turbine is:
m1ex1 = m2ex2 +(
m1 − m2
)ex3 + WHPT + IHPT (21)
The exergy destruction rate in the system is expressed as:
IHPT = m1(h1 − T0s1)− m2(h2 − T0s2)−(
m1 − m2
)(h3 − T0s3)− WHPT (22)
The exergy efficiency is written as follows:
ηex,HPT =WHPT
m1(ex1 − ex2)−(
m1 − m2
)ex3
(23)
3.3. Intermediate Pressure Turbine (IPT)
The exergy balance rate is:
(m1 − m2
)ex4 =
(m1 − m2
)ex5 + WIPT + IIPT (24)
The exergy destruction rate is:
IIPT =(
m1 − m2
)(h4 − T0s4)−
(m1 − m2
)(h5 − T0s5)− WIPT (25)
The exergy efficiency is:
ηex,IPT =WIPT(
m1 − m2
)(ex4 − ex5)
(26)
3.4. Low Pressure Turbine (LPT)
The exergy balance rate is:
(m1 − m2 − m6
)ex7 =
(m1 − m2 − m6
)ex8 + WLPT + ILPT (27)
Energies 2017, 10, 165 7 of 29
The exergy destruction rate is:
ILPT =(
m1 − m2 − m6
)(h7 − T0s7)−
(m1 − m2 − m6
)(h8 − T0s8)− WLPT (28)
The exergy efficiency is:
ηex =WLPT(
m1 − m2 − m6
)(ex7 − ex8)
(29)
3.5. Condenser
The exergy balance rate is:
(m1 − m2 − m6
)ex8 + m10ex10 =
(m1 − m2 − m6
)ex9 + m11ex11 + ICond (30)
The exergy destruction rate is:
ICond =(
m1 − m2 − m6
)((h8 − T0s8)− (h9 − T0s9)) + m10((h10 − T0s10)− (h11 − T0s11)) (31)
The exergy efficiency is:
ηex,Cond =m10(ex11 − ex10)(
m1 − m2 − m6
)(ex8 − ex9)
(32)
3.6. Condensate Extraction Pump (CEP)
The exergy balance rate is:
(m1 − m2 − m6
)ex9 =
(m1 − m2 − m6
)ex12 − WCEP + ICEP (33)
The exergy destruction rate is:
ICEP =(
m1 − m2 − m6
)(h9 − T0s9)−
(m1 − m2 − m6
)(h12 − T0s12) + WCEP (34)
The exergy efficiency is:
ηex,CEP =
(m1 − m2 − m6
)(ex12 − ex9)
WCEP
(35)
3.7. Open Feed Water Heater (OFWH)
The exergy balance rate is:
(m1 − m2 − m6
)ex12 + m6ex6 =
(m1 − m2
)ex13 + IOFWH (36)
The exergy destruction rate is:
IOFWH =(
m1 − m2 − m6
)(h12 − T0s12) + m6(h6 − T0s6)−
(m1 − m2
)(h13 − T0s13) (37)
Energies 2017, 10, 165 8 of 29
The exergy efficiency is:
ηex,OFWH =
(m1 − m2
)ex13(
m1 − m2 − m6
)ex12 + m6ex6
(38)
3.8. Feed Pump (FP1)
The exergy balance rate is:
(m1 − m2
)ex13 =
(m1 − m2
)ex14 − WFP1 + IFP1 (39)
The exergy destruction rate is:
IFPI =(
m1 − m2
)(h13 − T0s13)−
(m1 − m2
)(h14 − T0s14) + WFP1 (40)
The exergy efficiency is:
ηex,FB1 =
(m1 − m2
)(ex14 − ex13)
WFP1
(41)
3.9. Closed Feed Water Heater (CFWH)
The exergy balance rate is:
m2ex2 +(
m1 − m2
)ex14 = m2ex15 +
(m1 − m2
)ex17 + ICFWH (42)
The exergy destruction rate is:
ICFWH = m2((h2 − T0s2)− (h15 − T0s15)) +(
m1 − m2
)((h14 − T0s14)− (h17 − T0s17)) (43)
The exergy efficiency is:
ηex,CFWH =
(m1 − m2
)(ex17 − ex14)
m2(ex2 − ex15)(44)
3.10. Feed Pump (FP2)
The exergy balance rate is:
m2ex15 = m2ex16 − WFP2 + IFP2 (45)
The exergy destruction rate is:
IFP2 = m2(h15 − T0s15)− m2(h16 − T0s16) + WFP2 (46)
The exergy efficiency is:
ηex,FP2 =m2(ex16 − ex15)
WFP2
(47)
Energies 2017, 10, 165 9 of 29
For a combined heat and power plant, the overall exergy efficiency can be written as [41–43]:
ηex,Pl =
(wHPT + wIPT + wLPT − wCEP − wFP1 − wFP2
)+ ExQh
Exf
(48)
where ExQh is the exergy flow rate associated to the heat produced, Qh. Here, only the useful productshave been included in comparison to the exergy input. The exergy of ash and flue gas are discarded asthey do not represent a product flow.
4. Application of Exergy Analysis in Solid Fuel-Fired Heat and Power Plants
The use of exergy analysis on energy conversion processes has increased in the past years and hasincorporated studies of different types of heat and power plant systems for improving the efficiency ofexisting power plants together with developing systems and systems under design for maximizingutilization of the energy produced. In this study, we have reviewed the application of exergy analysisas an evaluator of performance in coal-fired, biomass-fired and coal-biomass co-combustion-firedpower plants.
4.1. Coal-Fired Heat and Power Plants
Coal supplies about 45% of the global electricity demand [44]. It is likely to continue as a keycomponent of the fuel mix in the generation of power even though these plants account for over 28% ofthe total global emissions of carbon dioxide [45]. In order to maximize the utility of coal used in theproduction of energy, considerable efforts need to be made to enhance the capacity and efficiency ofplants whilst simultaneously reducing their environmental impact and costs of power generation [46].Improving both the efficiency and cost effectiveness of power plants can be achieved by reducing thethermodynamic inefficiencies associated with the system that result in a reduction of the CO2 emissionper MW of electricity generated [47].
Exergy analysis has been proven to be a better way of measuring the efficiency of coal and reducingits environmental footprint by considering the effect of irreversibility in the process. A number ofstudies have been reported on the performance assessment and efficiency improvement of coal-basedpower plants using exergy analysis. Table 1 shows a summary of recent studies along with the mostimportant conclusions that can be drawn from the application of exergy analysis when evaluatingcoal-fired heat and power plants.
These results identify the boiler as being the component of the plant with the highestexergy destruction as a result of the entropy generated due to irreversible combustion reactions.Exergy destruction in the combustor section of the boiler has been attributed to the chemical reactionbetween the fuel and air, while the large temperature difference between the combustion gases and thefeedwater causes exergy destruction in the heat transfer section. The loss in the boiler in this particularcase is over 50% of the total exergy destruction. As a result of the irreversibilities identified usingexergy analysis, the exergetic efficiency is lower than the energetic efficiency, as can be seen in Table 1.The exergy efficiency of the power plants studied ranges from 17% to 38%. The results of the exergydestruction, heat loss and entropy generation in each component of a coal thermal power plant aresummarized in Table 2 [23]. The energy analysis shows that highest energy loss occurs in the condenser,whereas the actual exergy destruction is in the boiler according to exergy analysis. The results alsoshow that exergy destruction increases with increase in enropy generation. Hence, exergy analysisacounts for the entropy generated within the system, therefore, total exergy destruction is more thanthe heat loss.
Ener
gies
2017
,10,
165
10of
29
Tabl
e1.
Prev
ious
stud
ies
ofex
ergy
anal
ysis
appl
ied
toco
al-fi
red
heat
and
pow
erpl
ants
.
Plan
tCap
acit
y(M
W)
Com
bust
ion
Tech
nolo
gyPl
antO
utpu
tG
ener
atio
nC
ount
ryEx
ergy
(%)
Ener
gyEf
ficie
ncy
(%)
Aim
sM
ajor
Res
ults
Ref
.
32C
onve
ntio
nal
Elec
tric
ity
Indi
a25
.38
30.1
2
Toco
nduc
tath
erm
odyn
amic
san
alys
is,u
sing
the
desi
gnda
taof
aco
al–fi
red
pow
erpl
ant
unde
rco
nstr
ucti
on,t
oid
enti
fypo
tent
iala
reas
for
mak
ing
impr
ovem
ents
tope
rfor
man
ce.
Toin
vest
igat
eth
eef
fect
sof
vary
ing
the
oper
atin
gpa
ram
eter
son
perf
orm
ance
.
The
larg
estl
osse
soc
curr
edin
the
cond
ense
rw
hen
ener
gyan
alys
isw
asus
ed.
Whe
nex
ergy
anal
ysis
was
appl
ied,
how
ever
,the
actu
alm
ajor
loss
esw
ere
foun
din
the
boile
r,w
hich
has
the
high
est
exer
gyde
stru
ctio
n.T
his
isdu
eto
heat
bein
gtr
ansf
erre
dto
the
wor
king
fluid
,the
com
bust
ion
reac
tion
and
loss
esca
used
byem
issi
ons
offlu
ega
ses.
Incr
easi
ngth
est
eam
pres
sure
and
tem
pera
ture
,and
redu
cing
the
pres
sure
inth
est
eam
cond
ense
r,in
crea
sed
the
ener
gyan
dex
ergy
effic
ienc
ies
ofth
epl
ant.
[23]
500
Con
vent
iona
lEl
ectr
icit
yC
anad
a36
37
Toex
amin
ese
nsit
ivit
yto
reas
onab
leva
riat
ions
inde
ad-s
tate
prop
erti
esof
seve
rale
nerg
yan
dex
ergy
valu
es.T
oex
amin
eth
ere
sult
sof
ener
gyan
dex
ergy
anal
yses
ofa
com
plex
devi
ce.
The
ener
gyan
dex
ergy
valu
esw
ere
not
sign
ifica
ntly
sens
itiv
eto
reas
onab
leva
riat
ions
inde
ad-s
tate
prop
erti
es;t
hem
ain
resu
lts
ofen
ergy
and
exer
gyan
alys
esw
ere
not,
gene
rally
spea
king
,si
gnifi
cant
lyse
nsit
ive
tova
riat
ions
inth
ese
prop
erti
es.V
aria
tion
sin
the
refe
renc
ete
mpe
ratu
reco
nsid
ered
,T0,
did
nota
ffec
tthe
over
allr
esul
tsof
the
ener
gyan
dex
ergy
effic
ienc
ies
ofth
epl
ants
igni
fican
tly.
[26]
50C
onve
ntio
nal
Elec
tric
ity
Indi
a26
.95
27
Toco
nduc
tan
exer
gyan
alys
ison
the
pow
erge
nera
tion
ofU
nitV
,The
rmal
Pow
erSt
atio
n1
ofN
eyve
liLi
gnit
eC
orp.
Ltd.
(Tam
ilN
adu,
Indi
a)in
orde
rto
disc
over
the
exer
gylo
sses
inva
riou
sco
mpo
nent
sof
the
plan
t.
The
max
imum
ener
gylo
ss(3
9%)o
ccur
red
inth
eco
nden
ser
whi
leth
eto
talp
lant
exer
gyde
stru
ctio
nw
asca
lcul
ated
asbe
ing
73%
.The
max
imum
exer
gylo
ss(5
7.35
%)
occu
rred
inth
ebo
iler,
wit
h42
.73%
loss
esbe
ing
loca
ted
inth
eco
mbu
stio
n.
[27]
Ener
gies
2017
,10,
165
11of
29
Tabl
e1.
Con
t.
Plan
tCap
acit
y(M
W)
Com
bust
ion
Tech
nolo
gyPl
antO
utpu
tG
ener
atio
nC
ount
ryEx
ergy
(%)
Ener
gyEf
ficie
ncy
(%)
Aim
sM
ajor
Res
ults
Ref
.
1.5
Flui
dize
dbe
dC
ogen
erat
ion
Turk
ey20
-
Tope
rfor
man
exer
gyan
alys
isof
aco
gene
rati
onpo
wer
plan
t,lo
cate
din
Çan
kırı
,tha
tge
nera
tes
elec
tric
ity
and
stea
mus
edfo
rpr
oduc
ing
salt
.
The
high
este
xerg
yde
stru
ctio
nra
teto
okpl
ace
inth
ebo
iler,
whi
chha
d85
.89%
ofth
eto
tale
xerg
ylo
ssin
the
syst
em.
Impr
ovem
ents
toth
ede
sign
para
met
ers
(e.g
.,pr
essu
re,fl
uidi
zed
velo
city
,par
ticl
esi
zean
dge
omet
ry)a
sw
ella
sfe
edin
gth
eco
alfr
omdi
ffer
entp
oint
sin
toth
ebo
iler
shou
ldaf
fect
the
com
bust
ion
and
over
all
plan
teffi
cien
cies
posi
tive
ly.
[48]
150
Con
vent
iona
lEl
ectr
icit
yTu
rkey
35.1
937
.88
Tode
term
ine
the
effe
ctof
the
ambi
entt
empe
ratu
reon
the
irre
vers
ible
loss
esan
def
ficie
ncy
inth
eC
atal
gzi
Pow
erPl
anti
nZ
ongu
ldak
.
The
irre
vers
ibili
tyra
tes
ofth
ebo
iler
wer
ela
rger
than
for
othe
rco
mpo
nent
san
din
crea
sed
slig
htly
,tog
ethe
rw
ith
tota
lir
reve
rsib
ility
rate
,as
the
ambi
ent
tem
pera
ture
was
incr
ease
dfr
om27
8to
308
K,w
hile
that
ofth
eco
nden
ser
decr
ease
dw
ith
incr
easi
ngam
bien
tte
mpe
ratu
re.T
hebo
iler
was
the
maj
orso
urce
ofex
ergy
cons
umpt
ion
(are
sult
ofth
ech
emic
alre
acti
onbe
twee
nfu
elan
dai
r)an
dth
eref
ore
has
the
larg
estp
oten
tial
for
impr
ovem
ent.
[49]
7.7
Flui
dize
dbe
dC
ogen
erat
ion
Turk
ey23
70
Toan
alys
eth
eth
erm
odyn
amic
sof
aco
al-fi
red
fluid
ized
bed
pow
erpl
antt
osh
owth
eef
fect
sth
atex
cess
air,
stea
mpr
essu
rean
dty
peof
coal
have
onth
efir
stan
dse
cond
law
sof
effic
ienc
yin
the
ther
mal
pow
erpl
ant.
Seco
ndla
wan
alys
isre
veal
edth
atth
eFB
CC
had
the
larg
esti
rrev
ersi
bilit
y,ab
out
80.4
%of
the
syst
em’s
tota
lexe
rgy
loss
.Th
ech
emic
alre
acti
on(7
2%),
heat
tran
sfer
proc
esse
s(2
0%)a
ndph
ysic
altr
ansp
ort
(8%
)are
the
sour
ces
ofir
reve
rsib
iliti
esin
the
com
bust
ion
proc
ess
inFB
CC
.The
syst
em’s
exer
gyef
ficie
ncy
incr
ease
dw
ith
stea
mpr
essu
re,w
hile
type
sof
coal
did
nota
ffec
tthe
seco
ndla
wef
ficie
ncy.
As
the
exce
ssai
rva
lue
incr
ease
d,th
eex
ergy
and
ener
gyef
ficie
ncie
sde
crea
sed,
due
tohe
atlo
sses
bein
ghi
gher
whe
nth
eflo
wra
tes
ofth
eflu
ega
sin
crea
sed
and
com
bust
ion
tem
pera
ture
decr
ease
d:th
ese
affe
ctth
ere
acti
onra
teof
the
fuel
nega
tive
ly.
[50]
Ener
gies
2017
,10,
165
12of
29
Tabl
e1.
Con
t.
Plan
tCap
acit
y(M
W)
Com
bust
ion
Tech
nolo
gyPl
antO
utpu
tG
ener
atio
nC
ount
ryEx
ergy
(%)
Ener
gyEf
ficie
ncy
(%)
Aim
sM
ajor
Res
ults
Ref
.
-C
onve
ntio
nal
Elec
tric
ity
-34
36
Toin
vest
igat
eth
eef
fect
sof
feed
wat
erhe
ater
son
the
perf
orm
ance
ofa
coal
-fire
dpo
wer
plan
tusi
ngth
erm
odyn
amic
anal
ysis
.
For
asi
ngle
feed
wat
erhe
ater
,effi
cien
cyw
asm
axim
ized
ata
bled
stea
mte
mpe
ratu
rera
tio
of0.
4.T
heef
ficie
ncy
ofth
ecy
cle
was
high
whe
nth
ere
heat
erpr
essu
rew
as20
%–2
5%of
the
boile
rpr
essu
re.T
heex
erge
tic
loss
inth
ebo
iler
decr
ease
dw
ith
the
addi
tion
offe
edw
ater
heat
ers.
[51]
210
Con
vent
iona
lEl
ectr
icit
yIn
dia
34.5
036
.20
Toap
ply
exer
gyan
alys
isto
aco
al-b
ased
ther
mal
pow
erpl
anta
tdiff
eren
tope
rati
nglo
ads,
cond
ense
rpr
essu
res,
wit
h/w
itho
utce
rtai
nfe
edw
ater
heat
ers,
and
for
diff
eren
tgov
erno
rse
ttin
gsof
the
turb
ine
valv
es,i
.e.,
cons
tant
pres
sure
oper
atio
nor
slid
ing
pres
sure
oper
atio
n.
Red
ucin
gth
epl
antl
oad
and
incr
easi
ngth
eth
rott
leof
cont
rolv
alve
sin
crea
sed
the
irre
vers
ibili
ties
inth
ecy
cle,
whi
lst
incr
easi
ngth
eco
nden
ser’
sba
ckpr
essu
rede
crea
sed
the
exer
gyef
ficie
ncy.
Wit
hdra
wal
atth
ehi
ghpr
essu
rehe
ater
ssh
owed
ade
crea
sein
the
exer
gyef
ficie
ncy
ofth
een
tire
plan
t.T
heex
ergy
effic
ienc
yof
apa
rtlo
adop
erat
ion
impr
oved
whe
nth
em
ain
stre
ampr
essu
repr
ior
toth
etu
rbin
eva
lves
was
kept
insl
idin
gm
ode.
[52]
500
Con
vent
iona
lEl
ectr
icit
yIn
dia
31.4
734
.33
Toan
alys
ea
pulv
eriz
edco
al-fi
red
pow
erpl
anti
na
stea
dy-s
tate
cond
itio
nus
ing
ener
gyan
dex
ergy
anal
yses
.
Wit
hth
epl
anto
pera
ting
ata
capa
city
of46
0M
W,t
here
was
are
duct
ion
ofap
prox
.8.
69%
and
9.10
%,r
espe
ctiv
ely,
inth
een
ergy
and
exer
gyef
ficie
ncie
sco
mpa
red
toth
era
ting
sfo
rth
elo
adra
nge
desi
gned
.
[53]
500
Con
vent
iona
lEl
ectr
icit
yC
anad
a36
37
Toco
mpa
reco
alan
dnu
clea
rel
ectr
icge
nera
ting
stat
ions
ther
mod
ynam
ical
ly,u
sing
ener
gyan
dex
ergy
anal
ysis
.
Inth
eco
al-fi
red
plan
t,67
%an
d33
%of
the
exer
gyco
nsum
edw
asdu
eto
com
bust
ion
and
heat
tran
sfer
resp
ecti
vely
,In
the
nucl
ear
pow
erpl
ant,
5%,0
.9%
,0.1
%an
d94
%of
the
exer
gyde
stro
yed
was
due
toth
ebo
iler,
mod
erat
orco
oler
,he
avy-
wat
erpu
mp
and
reac
tor,
resp
ecti
vely
.
[54]
Ener
gies
2017
,10,
165
13of
29
Tabl
e1.
Con
t.
Plan
tCap
acit
y(M
W)
Com
bust
ion
Tech
nolo
gyPl
antO
utpu
tG
ener
atio
nC
ount
ryEx
ergy
(%)
Ener
gyEf
ficie
ncy
(%)
Aim
sM
ajor
Res
ults
Ref
.
3×
210
Con
vent
iona
lEl
ectr
icit
yTu
rkey
31.9
537
.01
Tom
ake
aco
mpa
rati
vean
alys
isof
the
perf
orm
ance
ofni
neco
alth
erm
alpo
wer
plan
tsfr
omen
erge
tic
and
exer
geti
cas
pect
s.
The
plan
twit
ha
capa
city
of32
0M
Wha
dth
ehi
ghes
texe
rget
icpe
rfor
man
ce:
the
exer
gyef
ficie
ncy
ofa
boile
rw
ith
aci
rcul
atin
gbe
dco
mbu
stor
had
the
high
estv
alue
ofal
lpla
ntbo
ilers
.Boi
lers
are
vita
lcom
pone
nts
beca
use
they
have
the
high
este
xerg
ylo
sses
ina
plan
t:th
eysh
ould
ther
efor
ebe
inve
stig
ated
soth
atth
eov
eral
lexe
rget
icpe
rfor
man
cem
aybe
enha
nced
.
[55]
4×
150
Con
vent
iona
lEl
ectr
icit
yTu
rkey
31.5
038
.03
2×
150
Con
vent
iona
lEl
ectr
icit
yTu
rkey
35.1
937
.88
3×
157
Con
vent
iona
lEl
ectr
icit
yTu
rkey
28.5
537
.19
4×
360
Con
vent
iona
lEl
ectr
icit
yTu
rkey
32.4
642
.64
210
Con
vent
iona
lEl
ectr
icit
yTu
rkey
35.4
937
.63
6×
165
Con
vent
iona
lEl
ectr
icit
yTu
rkey
32.3
536
.08
5×
160.
9C
onve
ntio
nal
Elec
tric
ity
Turk
ey33
.09
38.4
4
2×
160
Cir
cula
ting
fluid
ized
bed
Elec
tric
ity
Turk
ey37
.88
42.1
2
3×
210
Con
vent
iona
lEl
ectr
icit
yTu
rkey
31.9
537
.01
Tode
term
ine
the
mos
tco
nven
ient
poin
tofe
xtra
ctio
nof
ener
gyfo
rus
ein
dist
rict
heat
ing/
cool
ing
inth
eco
nven
tion
alco
al-fi
red
Yata
gan
Ther
mal
Pow
erPl
ant,
usin
gth
erm
odyn
amic
anal
ysis
toex
amin
eth
een
erge
tic
and
exer
geti
cpe
rfor
man
ces.
The
mos
tcon
veni
entp
oint
for
extr
acti
ngst
eam
inth
epl
anta
naly
sed
was
foun
dto
beth
elo
w-p
ress
ure
turb
ine
inle
tsta
ge.
[41]
500
Con
vent
iona
lEl
ectr
icit
yC
anad
a36
37
Toex
amin
eth
eef
fect
ofin
crea
sing
the
rehe
atpr
essu
reon
the
irre
vers
ibili
tyra
tean
dex
ergy
effic
ienc
yin
aco
al-fi
red
stea
mpo
wer
plan
t.
The
irre
vers
ibili
tyra
teas
soci
ated
wit
hhe
attr
ansf
erin
the
stea
mge
nera
tor
decr
ease
das
the
rehe
atpr
essu
rein
crea
sed.
How
ever
,the
over
all-
plan
texe
rgy
effic
ienc
yde
crea
sed
due
toth
ela
rge
decr
ease
inth
epo
wer
outp
utof
the
shaf
t.Th
ede
crea
sein
the
plan
t’sth
erm
alan
dex
ergy
effic
ienc
ies
over
the
rang
eof
rehe
atpr
essu
res
cons
ider
edw
asne
arly
9.3%
.
[56]
Ener
gies
2017
,10,
165
14of
29
Tabl
e1.
Con
t.
Plan
tCap
acit
y(M
W)
Com
bust
ion
Tech
nolo
gyPl
antO
utpu
tG
ener
atio
nC
ount
ryEx
ergy
(%)
Ener
gyEf
ficie
ncy
(%)
Aim
sM
ajor
Res
ults
Ref
.
32.5
Con
vent
iona
lEl
ectr
icit
yIn
dia
17.8
-
Toan
alys
eth
epe
rfor
man
ceof
aco
al-fi
red
stok
erpo
wer
plan
tusi
ngex
ergy
anal
ysis
.To
inve
stig
ate
the
effe
cts
ofva
ryin
gth
eop
erat
ing
tem
pera
ture
sof
the
boile
ras
wel
las
the
refe
renc
ete
mpe
ratu
rest
ate.
The
boile
rha
dth
ehi
ghes
texe
rgy
dest
ruct
ion
rate
,wit
h77
%of
the
tota
lexe
rgy
loss
bein
gdu
eto
flue
gas
emis
sion
s,flu
ega
ste
mpe
ratu
re,
com
bust
ion
reac
tion
san
dhe
attr
ansf
erto
the
stea
m.T
heef
ficie
ncy
ofth
epo
wer
plan
tinc
reas
edfr
om18
%to
42%
whe
nth
ete
mpe
ratu
reof
the
exit
ing
stea
min
crea
sed
from
723
Kto
793
K.V
aryi
ngth
ere
fere
nce
stat
ete
mpe
ratu
reha
dno
sign
ifica
ntim
pact
onth
epl
ant’s
over
allp
erfo
rman
ce.
[57]
250
Con
vent
iona
lEl
ectr
icit
yBa
ngla
desh
30.7
8-
Toin
vest
igat
ea
coal
-bas
edth
erm
alpl
anto
pera
ting
atsu
b-cr
itic
alst
eam
cond
itio
nsus
ing
ther
mod
ynam
icpe
rfor
man
cecr
iter
ia.
The
max
imum
exer
gylo
sses
occu
rred
inth
ebo
iler:
the
larg
eex
ergy
loss
was
mai
nly
due
toth
eco
mbu
stio
nre
acti
onan
dth
ehi
ghte
mpe
ratu
redi
ffer
ence
betw
een
the
com
bust
ion
gas
and
the
stea
m.
[58]
63C
ircu
lati
ngflu
idiz
edbe
dEl
ectr
icit
yIn
dia
29.2
931
.15
Toes
tabl
ish
the
ener
gyan
dex
ergy
flow
sof
each
com
pone
ntin
the
coal
-bas
edci
rcul
atin
gflu
idiz
edbe
dbo
iler
inth
eTu
tico
rin
Pow
erPl
anti
nor
der
toid
entif
yth
em
ajor
area
ofex
ergy
loss
.
74%
ofth
eto
tale
xerg
ylo
ssoc
curr
edin
the
furn
ace
ofth
ebo
iler
syst
em;
54.1
%of
the
loss
was
loca
ted
inth
efu
rnac
e’s
com
bust
ion
cham
ber.
[59]
4×
400
Con
vent
iona
lEl
ectr
icit
ySa
udi
Ara
bia
35.7
7-
Toev
alua
teth
ede
sign
and
perf
orm
ance
ofth
eex
isti
ngG
hazl
anPo
wer
Plan
tusi
ngth
eex
ergy
conc
ept.
The
maj
oren
ergy
loss
esw
ere
due
tohe
atre
ject
ion
inth
eco
nden
ser
and
stac
kga
ses,
whi
leth
ehi
ghes
texe
rgy
loss
esof
70.6
%of
the
tota
llos
soc
curr
edin
the
boile
r.
[60]
Ener
gies
2017
,10,
165
15of
29
Tabl
e1.
Con
t.
Plan
tCap
acit
y(M
W)
Com
bust
ion
Tech
nolo
gyPl
antO
utpu
tG
ener
atio
nC
ount
ryEx
ergy
(%)
Ener
gyEf
ficie
ncy
(%)
Aim
sM
ajor
Res
ults
Ref
.
7.7
Flui
dize
dbe
dC
ogen
erat
ion
Turk
ey23
70
Toap
ply
conv
enti
onal
and
adva
nced
exer
gyan
alys
isto
aflu
idiz
edbe
dco
alco
mbu
stio
n(F
BCC
)and
heat
reco
very
stea
mge
nera
tor
(HR
SG)i
na
text
ilepl
ant.
Ato
tale
xerg
yde
stru
ctio
nof
5104
kWoc
curr
edin
the
syst
em,t
hem
ajor
part
ofw
hich
(428
5kW
)was
inth
eFB
CC
.Th
eco
nven
tiona
lexe
rgy
effic
ienc
ies
inth
eFB
CC
and
HR
SGw
ere
44.2
%an
d46
.2%
,res
pect
ivel
y,an
d53
.1%
and
48.1
%,r
espe
ctiv
ely,
for
adva
nced
exer
gyef
ficie
ncie
s.
[61]
145
200
300
Con
vent
iona
lC
ogen
erat
ion
Chi
na29
.132
.227
.3
58.2
46.2
73
Toin
vest
igat
eth
em
ost
impo
rtan
tope
rati
ngpa
ram
eter
saf
fect
ing
the
ener
geti
can
dex
erge
tic
effic
ienc
ies,
and
thei
rin
fluen
ceon
the
perf
orm
ance
ofth
ree
diff
eren
tcoa
l-fir
edco
mbi
ned
pow
er(C
HP)
plan
tsun
der
vari
ous
oper
atio
nalc
ondi
tion
sin
the
dist
rict
heat
ing
(DH
)sys
tem
.
The
extr
actio
nflo
wra
tean
dex
trac
tion
pres
sure
wer
eth
em
osti
mpo
rtan
tpa
ram
eter
sof
the
ener
getic
and
exer
getic
effic
ienc
ies,
resp
ectiv
ely,
inth
eth
ree
pow
erpl
ants
.Whe
nth
eex
trac
tion
ratio
incr
ease
d,th
een
erge
ticef
ficie
ncy
incr
ease
d,w
here
asth
eex
erge
ticef
ficie
ncy
decr
ease
d.A
high
extr
actio
nra
tioan
da
low
extr
actio
npr
essu
rega
veth
ebe
stpe
rfor
man
cein
the
CH
P.A
high
erex
trac
tion
pres
sure
led
toa
high
erhe
atde
liver
y.
[42]
280
Con
vent
iona
lEl
ectr
icit
yA
ustr
alia
--
Toco
nduc
tan
exer
gyan
alys
isof
aco
al-fi
red
pow
erpl
anti
nce
ntra
lQue
ensl
and.
The
high
este
xerg
yde
stru
ctio
noc
curr
edin
the
boile
r,w
hich
had
81%
ofth
eto
tale
xerg
yde
stru
ctio
nin
the
plan
t.Th
isdi
ffer
sto
the
ener
gyba
lanc
e,w
hich
show
edth
atm
osto
fthe
ener
gylo
ssoc
curr
edin
the
cond
ense
r,w
here
69%
ofth
eto
talw
aslo
st.
The
exer
gylo
ssin
the
boile
rw
asa
resu
ltof
(i)its
inte
rnal
loss
,(ii)
the
loss
inits
blow
dow
nst
ream
and
(iii)
the
heat
loss
caus
edby
the
stre
amof
flue
gas:
the
grea
test
exer
gylo
ssoc
curr
edin
the
boile
r´s
inte
rnal
heat
tran
sfer
arra
ngem
ent.
Ast
eam
boile
rha
sa
grea
tpot
entia
lfor
impr
ovin
gth
eov
eral
leff
icie
ncy
ofa
plan
t.
[62]
Energies 2017, 10, 165 16 of 29
Table 2. Results from the analysis of a coal-fired thermal plant [23].
Components Exergy Destruction (kW) Heat Loss (kW) Entropy Generation (kW/K)
Boiler 73,046 12,663 3312.0Turbine 6403 3242 17.2
ACC (air cooled condenser) 1622 33 3.3Deaerator 886 71 1.4LP heater 552 336 2.4HP heater 759 65 2.7
Boiler feed pump 375 140 0.0Generator 550 656 0.9
Total 84,193 50,456 3339.9
Whilst the reference temperature does not have a noticeable effect on the energy efficiency [23],it does affect the exergy efficiency slightly, as shown in Table 3. This indicates that the surroundings ofthe system affect its performance when exergy analysis is used. Even though variations in the referencetemperature, T0, do not affect the overall exergy results significantly, it is important in determining theoptimal operation condition in a given plant design [26]. An increase in the ambient temperature hasa greater effect on the condenser compared to other components, as indicated in Table 4 [49].
Table 3. Variation in energy and exergy efficiencies at different reference temperatures [23].
Temperature (K) Exergy Efficiency (%) Energy Efficiency (%)
273 25.3970 30.12283 25.3920 30.12293 25.3884 30.12303 25.3850 30.12313 25.3806 30.12323 25.3760 30.12
Table 4. The exergy rate of fuel and irreversibility rates of a power plant, kW, at different referencetemperatures [49].
Reference Temperature 278 K 283 K 288 K 293 K 298 K 303 K 308 K
Fuel exergy rate 473,500 473,500 473,500 473,500 473,500 473,500 473,500Irreversibility rate of boiler 262,520 262,561 268,602 271,643 274,684 277,725 280,766
Irreversibility rate of turbine 35,594 35,941 36,288 36,636 36,983 37,330 37,678Irreversibility rate of condenser 9330 7982 6871 4607 2186 960 373
Irreversibility rate of feed water heaters 5256 5314 5371 5428 5486 5543 5601Irreversibility rate of pumps 1014 1028 1042 1056 1070 1084 1098
Irreversibility rate of pipe 1418 1393 1367 1342 1317 1291 1266Total Irreversibility rate 315,132 317,218 319,542 320,711 321,725 323,934 326,780
Taniguchi et al. [63] conducted exergy analyses of coal combustion processes with air temperaturesentering the combustion chamber higher than the ambient temperature. They found that an increase inthe temperature of the combustion air increases exergy efficiency. The decrease in the amount of excessair reduces flue gas losses and improves the combustion temperature [48]. Feedwater heaters can beinstalled to decrease the temperature difference between the flue gases and the working fluid [51];both of these measures decrease the irreversibilities in the boiler. Operation of a power plant at fullload has been shown to increase the combustion efficiency of the system and the exergy efficiency ofthe plant [52,53], indicating that power plants operating at their rated capacity are more economicalthan when operating at part loads [23]. The performance of the boiler system and the exergy efficiencyincrease with an increase in the steam pressure and temperature and number of feed water heaters,but a decrease in pressure in the condenser and reheater [23,51,52], while utilization of the rejected
Energies 2017, 10, 165 17 of 29
heat from the condensers as employed in the cogeneration systems improves the overall efficiency ofthe system [54].
The adoption of fluidized bed combustion firing technologies has been suggested as a means ofimproving the performance of energy conversion systems since (i) their heating surfaces located inthe combustion chamber have high heat transfer rates and (ii) their combustion efficiency is superiorto conventional firing systems [55]. Moreover, a fluidized bed boiler has the capacity of burning fuelmixtures with widely differing characteristics. Its low combustion temperature minimizes NOx, andthe usage of adsorbent in the bed permits the capture of sulphur [64].
4.2. Heat and Power Plants Fired by Biomass-Based Fuels
Biomass energy is derived from plant and animal material, such as wood and wood waste,agricultural crops and their waste by-products, solid municipal refuse, animal offal, waste from foodprocessing units, aquatic plants and algae [65]. The resource known as biomass can be consideredas being renewable material in which the energy of sunlight is stored in the form of chemical bonds;when the bonds between adjacent carbon, hydrogen and oxygen molecules are broken by digestion,combustion or decomposition, these substances can release their stored chemical energy [66].
The reduction in the use of coal fuels, and the need to find alternatives to fossil fuels in order todecrease CO2 emissions, have attracted more interest in using biomass fuels as the energy carrier sincebiomass is perceived as being a carbon-neutral source [67,68]: biomass is thus regarded a suitable sourceof energy [69]. However, the overall efficiencies of biomass-fired power plants are relatively low [19,70].Only a few papers in the literature have discussed exergy analysis applied to biomass-based heat andpower plants—these are summarized in Table 5.
Li et al. [67] used conventional exergy analysis to find the sources of irreversibilities and to identifyexergy destruction in the various different components of the biomass boiler. They also used advancedexergy analysis to provide comprehensive information about the avoidable exergy destruction for eachcomponent, as well as for the whole system. Their results showed that a combustion chamber witha higher degree of heat absorption has a higher exergy in the specified boiler components and that,in a biomass boiler system, the combustion process is where most of the exergy destruction that isavoidable can be found.
Kamate and Gangavati [71] applied exergy analysis to two types of steam turbines to examine theeffective utilization of cogeneration power plants in the sugar industry. They found that the efficiencyof the plant using a non-condensing steam turbine (back pressure steam turbine) with energy andexergy efficiencies of 0.863 and 0.307 respectively, was higher than that of a plant using an extraction(condensing) steam turbine with energy and exergy efficiencies of 0.682 and 0.260 respectively, becausethe former does not reject heat in the condensation process. However, when a greater amount ofelectricity is needed, the latter is preferred.
The generation of entropy occurs mainly in the combustion process, which promptedBaloyi et al. [72] to examine the change in its rate as a function of the air to fuel (AF) ratio in anadiabatic combustor, using wood as the source of fuel. They showed that the entropy generation ratereaches a minimum at an AF of 4.9 and equivalence ratio of 1.64.
The performances of biomass multi-generation and cogeneration power plants have also beenevaluated. Soltani et al. [28] investigated a biomass multi-generation energy system that produceselectricity, steam, hot water, district heating and timber heating: significant increases in both theenergy and exergy efficiencies were observed in the biomass multi-generation systems compared toconventional systems. A fuel energy savings ratio of 8.2% was reported by Kamate and Gangavati [43]for a biomass cogeneration plant over the generation of heat and power in two separate plants.
Ener
gies
2017
,10,
165
18of
29
Tabl
e5.
Prev
ious
stud
ies
ofex
ergy
anal
ysis
appl
ied
tobi
omas
s-fir
edhe
atan
dpo
wer
plan
ts.
Plan
tCap
acit
y(M
W)
Com
bust
ion
Tech
nolo
gyPl
antO
utpu
tG
ener
atio
nC
ount
ryEx
ergy
Effic
ienc
y(%
)En
ergy
Effic
ienc
y(%
)A
ims
Maj
orR
esul
tsR
ef.
-C
onve
ntio
nal
Mul
ti-ge
nera
tion
-25
60
Tom
odel
and
eval
uate
a(s
awdu
st)b
iom
ass-
fired
mul
ti-g
ener
atio
nen
ergy
syst
emus
ing
ener
gyan
dex
ergy
anal
ysis
.
The
biom
ass
com
bust
orw
asth
elo
cati
onof
the
mai
nex
ergy
dest
ruct
ion
inth
esy
stem
due
toir
reve
rsib
lech
emic
alre
acti
ons
(com
bust
ion)
and
heat
tran
sfer
acro
sste
mpe
ratu
redi
ffer
ence
sbe
twee
nth
ein
puta
ndou
tput
stre
ams
inth
ehe
atex
chan
ger.
The
biom
ass
inpu
trat
eha
sa
sign
ifica
ntef
fect
onth
ehe
atav
aila
ble
for
dist
rict
heat
ing
and
elec
tric
ity
gene
rati
on:
whi
lsti
tinc
reas
edth
eex
ergy
effic
ienc
yof
the
over
allm
ulti
-gen
erat
ion
syst
em,i
tde
crea
sed
the
ener
gyef
ficie
ncy
slig
htly
.
[28]
-C
onve
ntio
nal
--
--
Toes
tabl
ish
ath
eore
tica
lfr
amew
ork
for
the
exer
gyan
alys
isan
dad
vanc
edex
ergy
anal
ysis
ofa
biom
ass
boile
r.
The
com
bust
ion
proc
ess
dom
inat
edth
eex
ergy
dest
ruct
ion
inth
em
ain
com
pone
nts
ofa
biom
ass
boile
rin
conv
enti
onal
and
adva
nced
exer
gyan
alys
is.T
hein
crea
sein
biom
ass
moi
stur
ere
duce
dth
ead
iaba
tic
flam
ete
mpe
ratu
re,
decr
ease
dth
eto
talb
oile
rex
ergy
effic
ienc
yan
dde
crea
sed
the
air-
fuel
rati
o.
[67]
4.5
Con
vent
iona
lEl
ectr
icit
yIn
dia
16.8
918
.25
Toco
nduc
tan
exer
gyan
alys
isof
abi
omas
s-ba
sed
stea
mpl
anti
nK
arem
pudi
.
The
boile
rha
dth
ehi
ghes
texe
rgy
dest
ruct
ion,
49.1
7%of
the
tota
lam
ount
,du
eto
irre
vers
ibili
tyas
soci
ated
wit
hch
emic
alre
acti
ons.
[19]
-C
onve
ntio
nal
Cog
ener
atio
nIn
dia
*30.
7**
26.0
*BPS
T**
ECST
*86.
3**
68.2
*BPS
T**
ECST
Toev
alua
te,a
ndm
ake
anov
eral
lass
essm
ento
f,a
baga
sse-
base
dco
gene
rati
onpl
anti
nth
esu
gar
indu
stry
usin
gba
ckpr
essu
rean
dan
extr
acti
onco
nden
sing
stre
amtu
rbin
ew
ith
aca
paci
tyof
2500
tonn
esof
suga
rca
nepe
rda
y.
The
back
-pre
ssur
est
eam
turb
ine
(BPS
T)w
asth
em
oste
ffec
tive
confi
gura
tion
from
anov
eral
lper
spec
tive
butt
heex
trac
tion
cond
ensi
ngst
eam
plan
t(EC
ST)c
anpr
oduc
em
ore
pow
er.T
hebo
iler
was
the
leas
teffi
cien
tcom
pone
ntan
dw
asth
esi
teof
the
maj
orpa
rtof
the
exer
gyde
stru
ctio
n,bu
tinc
reas
ing
both
the
stea
min
let
pres
sure
and
tem
pera
ture
decr
ease
dir
reve
rsib
ility
inth
epl
ant’s
com
pone
nts.
[71]
-Fl
uidi
zed
bed
--
--
Toan
alys
eth
eir
reve
rsib
ilitie
sge
nera
ted
duri
ngth
eco
mbu
stio
nof
woo
din
anad
iaba
tic
com
bust
or.
The
rate
ofen
trop
yge
nera
tion
was
enti
rely
due
toth
eco
mbu
stio
npr
oces
s;th
eir
reve
rsib
iliti
esge
nera
ted
reac
hed
am
inim
umat
anai
r-fu
elm
ass
rati
oof
4.9
inan
adia
bati
cco
mbu
stor
.
[72]
Ener
gies
2017
,10,
165
19of
29
Tabl
e5.
Con
t.
Plan
tCap
acit
y(M
W)
Com
bust
ion
Tech
nolo
gyPl
antO
utpu
tG
ener
atio
nC
ount
ryEx
ergy
Effic
ienc
y(%
)En
ergy
Effic
ienc
y(%
)A
ims
Maj
orR
esul
tsR
ef.
44C
onve
ntio
nal
Cog
ener
atio
nIn
dia
2565
Toev
alua
teba
gass
e-ba
sed
coge
nera
tion
ofpo
wer
base
don
asu
gar
fact
ory
inBe
lgau
mw
ith
aca
paci
tyof
10,0
00to
nsof
suga
rca
nepe
rda
y(T
CD
)usi
ngen
ergy
and
exer
gyan
alys
is.
The
maj
orex
ergy
dest
ruct
ion
was
foun
din
the
boile
r,w
here
71%
ofth
efu
elex
ergy
inpu
twas
dest
roye
d.En
ergy
loss
esoc
curr
edm
ainl
yin
the
boile
rex
haus
tand
cond
ense
r,w
here
35M
Wan
d27
MW
wer
elo
stto
the
envi
ronm
ent,
resp
ecti
vely
.Th
epl
ant’s
fuel
ener
gysa
ving
sra
tio
for
the
co-g
ener
atio
npl
anti
s8.
2%ov
erse
para
tege
nera
tion
.
[43]
-C
onve
ntio
nal
Cog
ener
atio
nN
orw
ay17
.340
.6
Toca
lcul
ate
the
seco
ndla
wef
ficie
ncy
ofa
mun
icip
also
lidw
aste
com
bine
dhe
atan
dpo
wer
plan
tloc
ated
inBe
rgen
usin
gdi
ffer
ent
met
hods
tode
term
ine
the
chem
ical
exer
gyof
the
fuel
.
The
diff
eren
tmet
hods
used
show
com
para
ble
resu
lts.
The
seco
ndla
wef
ficie
ncy
was
17.3
%fo
rth
elo
cal
surr
ound
ing
tem
pera
ture
,the
ener
gyut
iliza
tion
was
40.6
%an
dth
eR
1ef
ficie
ncy
was
0.56
8.Fo
cusi
ngon
the
prod
ucti
onof
elec
tric
ity
from
was
teca
ngi
vela
rger
incr
ease
sin
exer
gyre
cove
ryan
dex
ergy
effic
ienc
yth
anin
crea
sing
the
deliv
ery
ofdi
stri
ctor
proc
ess
heat
.
[73]
--
--
--
Toan
alys
ecr
itic
ally
,an
dca
lcul
ate
corr
ectl
y,th
eef
ficie
ncy
ofen
ergy
reco
vere
dfr
omw
aste
inci
nera
tion
inth
ene
ww
aste
fram
ewor
kdi
rect
ive.
Toco
mpa
reth
een
ergy
reco
very
effic
ienc
yto
the
mor
esc
ient
ifica
lly-b
ased
appr
oach
ofex
ergy
effic
ienc
y.
The
aver
age
ener
gyre
cove
ryef
ficie
ncie
sca
lcul
ated
for
CH
Ppl
ants
,pla
nts
prod
ucin
gm
ainl
yel
ectr
icit
yan
dpl
ants
only
prod
ucin
ghe
atw
ere
0.71
,0.4
9an
d0.
64,r
espe
ctiv
ely,
whi
lstt
heav
erag
eex
ergy
effic
ienc
ies
for
thes
epl
ants
wer
e20
.9%
,19.
4%an
d18
.8%
,res
pect
ivel
y.Th
eav
erag
een
ergy
reco
very
effic
ienc
yof
WTE
plan
tsis
high
erin
nort
hern
Euro
peth
anin
sout
hern
,as
are
sult
ofth
eco
gene
rati
onte
chno
logy
that
ism
ostl
yus
edth
ere.
The
ener
gyre
cove
ryef
ficie
ncy
ina
WTE
plan
tdoe
sno
ttak
ein
toac
coun
tth
eef
fect
ofth
epl
ant’s
size
and
the
influ
ence
ofcl
imat
eco
ndit
ions
.Th
eex
ergy
effic
ienc
yis
are
liabl
em
easu
reof
the
calc
ulat
ion
ofef
ficie
ncy
ofen
ergy
reco
very
from
was
tein
cine
rati
on.
[74]
Energies 2017, 10, 165 20 of 29
Different methods have been applied to calculate the efficiency of the incineration of municipalsolid waste. Solheimslid et al. [73] used the chemical exergy of solid biomass by employing correlations,the chemical exergy obtained from the combustion equation and the absolute entropy to determine theexergy efficiency of municipal waste in a combined heat and power plant, and found both results to bein good agreement. Grosso et al. [74] examined the energy recovery efficiency, reported in the WasteFrame Directive (Directive 2008/98/EC), which accounts for the production of both power and heat.According to the directive, the energy recovery efficiency must be equal to, or exceed, 0.60 for wasteincineration plants to be classified as energy recovery, rather than waste disposal, units. They analysedand compared the energy recovery efficiency to the exergy efficiency in the form of energy recoverycriteria for different types of waste incineration plants in Europe, and found out that only the exergyefficiency can be considered a reliable measure.
4.3. Biomass and Coal Co-Fired Heat and Power Plant
Co-firing biomass in coal-fired boilers is regarded as being the most cost-effective approach forutilising biomass to generate power [75] because it requires little initial investment: the combustiontechnologies used in biomass co-firing plants are similar to those used in existing coal-fired plants [76].Three different methods are used in biomass co-firing technology: direct, indirect and parallel co-firing.In the first method, biomass is fed directly into a boiler furnace with coal whilst the second entailsa combination of gasification and combustion: the biomass is gasified and the product gas is fedinto a boiler furnace containing the coal. The third method involves the biomass being burnt ina separate boiler to generate steam, which is then used in a power plant together with coal [77].Selecting the appropriate co-firing option depends on the type of biomass available and site-specificfactors, such as the types of coal handling equipment used and the arrangement of the coal firingsystems installed [78,79].
Biomass co-fired with coal in traditional coal-fired boilers presents one combination of utilisingfossil and renewable energy that derives the greatest benefit from both types of fuel; it leadsto an effective reduction in CO2 and SOx emissions, and often NOx emissions too. It representsan attractive alternative for reducing emissions of greenhouse gas from coal-fired boilers [80].Coal-biomass co-firing prevents the concentration of chlorine, which can otherwise result in theformation of harmful alkaline and chlorine compounds on the heat transfer surfaces in boilers [81].Progress has been made over the past years in developing the co-ultilization of biomass fuels incoal-fired boiler plants [82]. Exergy analysis can nevertheless be used to evaluate performance inorder to identify both the magnitude and the locations of imperfections in the process, with the aim ofimproving the efficiency of the plant. Reports pertaining to exergy analyses of biomass co-combustionprocesses are very few and far between: Table 6 shows a summary of previous work performed inthis field.
Biomass co-combustion is considered as a measure for reducing CO2 emissions. However, theexergy losses due to irreversibility from biomass co-firing are larger than for coal-based power plants.This irrervisibility has led to decreases in the exergy efficiencies of both the boiler and the overallco-combustion plant [83]: the gas exiting the furnace has a lower temperature due to a reduction in theexergy input to the plant. Applying biomass co-firing to a fluidized bed shows that the velocity of thefluidized bed does not influence the exergy efficiency [84].
The Soma coal thermal power plant in Turkey was modified to operate as both a direct andparallel co-firing biomass plant; performance evaluation shows that biomass parallel combustionperforms better, from both technical and environmental aspects, than direct co-firing which suffersfrom problems of corrosion and fouling in the boiler [25]. As a result of the direct contact that occursbetween biomass and coal in the direct co-firing method, the alkali metals and chlorine from thebiomass reduced the melting temperature of the ash: the result was slagging at the furnace walls of theboiler and a possible decrease in the efficiency of the plant [85].
Ener
gies
2017
,10,
165
21of
29
Tabl
e6.
Prev
ious
stud
ies
ofex
ergy
anal
ysis
appl
ied
toco
al-b
iom
ass
co-c
ombu
stio
nhe
atan
dpo
wer
plan
ts
Plan
tCap
acit
y(M
W)
Com
bust
ion
Tech
nolo
gyPl
antO
utpu
tG
ener
atio
nC
ount
ryEx
ergy
Effic
ienc
y(%
)En
ergy
Effic
ienc
y(%
)A
ims
Maj
orR
esul
tsR
ef.
165
Con
vent
iona
lEl
ectr
icit
yTu
rkey
29.0
435
.91
Toin
vest
igat
eth
ete
chni
cala
nden
viro
nmen
talf
easi
bilit
yof
dire
ctan
dpa
ralle
lco-
firin
gof
biom
ass
*w
ith
Som
aLi
gnit
eC
orp.
Ltd.
inth
eSo
ma
Ther
mal
Pow
erPl
ant,
usin
gex
ergy
anal
ysis
.*co
rnco
bs,c
otto
ngi
nan
dol
ive
pits
.
Both
the
dire
ctan
dpa
ralle
lco-
firin
gof
biom
ass
decr
ease
dth
eco
nsum
ptio
nra
teof
ligni
tean
dre
duce
dth
epl
ant’s
emis
sion
sof
CO
2,SO
2an
ddu
stsi
gnifi
cant
ly.T
hela
rges
texe
rgy
dest
ruct
ion
occu
rred
inth
ebo
iler.
Para
llelc
o-fir
ing
offe
red
bett
erte
chni
cal
and
envi
ronm
enta
lper
form
ance
sth
andi
rect
co-fi
ring
.
[25]
-C
onve
ntio
nal
Elec
tric
ity
-32
.26
-
Toco
nduc
tan
exer
gyan
alys
isof
abi
omas
s*
co-fi
red
base
dco
nven
tion
alpu
lver
ized
coal
(bit
umin
ous
and
ligni
te)p
ower
plan
t.*
chic
ken
litte
r,pi
nesa
wdu
st,r
efus
e-de
rive
dfu
elan
dri
cehu
sks.
The
larg
este
xerg
yde
stru
ctio
noc
curr
edin
the
boile
r,du
eto
chem
ical
reac
tion
san
dhe
attr
ansf
erac
ross
ala
rge
tem
pera
ture
diff
eren
cebe
twee
nth
epr
oduc
tgas
and
the
feed
wat
er,w
ithth
eco
mbu
stor
havi
ngth
ehi
ghes
tdeg
ree
ofde
stru
ctio
n.Th
eir
reve
rsib
ility
rate
sof
the
plan
tdec
reas
edas
the
cont
ento
fbi
omas
sin
the
fuel
blen
din
crea
sed.
How
ever
,the
exer
gyef
ficie
ncie
sof
the
boile
ran
dth
eov
eral
lpla
ntde
crea
sed
asth
eco
-firi
ngin
crea
sed.
Alt
houg
hbi
omas
sco
-firi
ngis
nota
dvan
tage
ous
from
ath
erm
odyn
amic
pers
pect
ive,
ithe
lps
redu
ceen
viro
nmen
tale
mis
sion
san
den
hanc
esth
efin
ance
sof
the
plan
t.
[83]
1.0
Flui
dize
dbe
dEl
ectr
icit
y-
32.9
-
Toap
ply
the
exer
gym
etho
dto
the
nine
expe
rim
enta
lres
ults
obta
ined
from
the
pilo
tpla
nt,
mod
elle
don
bubb
ling
fluid
ized
bed
co-c
ombu
stio
n,us
ing
biom
ass
*an
dlo
wgr
ade
Span
ish
coal
.*pi
nech
ips,
i.e.,
woo
dw
aste
.
The
exer
gyde
stro
yed
rang
edfr
om48
.4%
to56
.2%
ofth
eex
ergy
inpu
t,w
ith
high
esti
rrev
ersi
bilit
yfo
und
inth
eco
mbu
stio
npr
oces
s.Th
epe
rfor
man
ceof
the
plan
tmay
beim
prov
edby
redu
cing
the
exit
tem
pera
ture
ofth
eflu
ega
sby
the
addi
tion
ofa
heat
exch
ange
r;he
atlo
ssto
the
envi
ronm
entc
anbe
redu
ced
byin
sula
ting
the
com
bust
ion
cham
ber.
[84]
Energies 2017, 10, 165 22 of 29
5. Discussion
The performance assessment of energy from solid fuels used for generation of heat and power hasbeen reviewed. An effective utilization of this energy in the heat and power plant is needed: as the fuelconversion efficiencies investigated are low. The use of energy efficiency to evaluate the performanceof the system is not adequate as the energy method does not identify degradation of the energy qualityduring the energy conversion processes. As a result of this inaccuracy, the energy efficiencies arehigher than the exergy efficiencies.
The difference between the energy and exergy efficiencies is observed in the heat and power plant,Figure 2, while little variation is seen in the power plant, Figure 3, where the data used is collectedfrom Tables 1, 5 and 6. The produced heat, often distributed as water around 100 ◦C has a low energyquality (low exergy) but represents rather high energy content.
Figure 2. Variation of exergy and energy efficiency in different combined heat and power plants.
Figure 3. Variation of exergy and energy efficiency in different power plants.
Energies 2017, 10, 165 23 of 29
The performance of the whole plant is based on the individual components of the system.Therefore, identification of the component with highest inefficiencies is the first step for performanceimprovement of the overall plant. According to the energy analysis, the major energy losses in a powerplant are due to the heat rejection in the condenser as a result of the large enthalpy difference betweenthe turbine and the condenser: here, second law analysis shows that less than 6% of the total exergy lossstems from the condenser while as much as 69% of the total energy loss is found in this component [62].From the exergy analysis, the highest degree of exergy destruction occurs in the boiler (combustionand heat transfer) with over 50% of the total irreversibility in the plant. Figure 4 shows the effect ofboiler efficiency on the performance of the overall plant, where the plant data is taken from Tables 1,5 and 6. The result indicates that an increase in the boiler efficiency will increase the overall exergyefficiency of the plant.
Figure 4. Effect of the boiler efficiency on the overall plant exergy efficiency.
The performance evaluations of the solid fuel-fired heat and power plants reviewed, showsin general, that the coal-fired plant has highest exergy efficiency compared with the other solidfuels. This is as a result of higher operating temperature and pressure. However, the CO2 emissionsassociated with the system have an impact on the environment as a greenhouse gas. The emissions canbe reduced by integrating the plant with carbon capture and storage [86]. But adopting this technologymeans extra cost and more energy is consumed during the process, which leads to reduction in theefficiency of the plant [47].
The biomass-based fuels on the other hand account for about 14% of the energy utilize in theworld [87]. It remains the main source of energy for more than half of the world’s population [88].Although, biomass has a lower efficiency than coal; it is a suitable and renewable energy option thatprovides clean gas fuels presently and in the future [89,90].
Co-combustion of biomass and coal could decrease the consumption rate of coal as well as reducethe environmental impact from coal-fired plant. Biomass contains only a small amount of nitrogenand sulphur, which will reduce NO2 and SO2 emissions associated with coal [91]. Co-firing also giveshigher exergy efficiency than the biomass-based plant. However, co-firing of biomass in the existingcoal-fired plant decreases the boiler and overall exergy efficiency due to increased moisture content inthe biomass, which reduces the furnace exit gas temperature [83]. Moreover, it increases corrosion andash deposition in the system, and if co-utilization of biomass fuel in coal-fired plant is not carefullydesigned, it will involve risk of power outages [80].
Energies 2017, 10, 165 24 of 29
Different improvement measures have been suggested by the past studies in order to reduce exergydestruction. Though excess air is needed for complete combustion, the amount should be minimizedbecause an increase in excess air will reduce the adiabatic flame temperature and decrease the exergyefficiency of the boiler [67] as well as the overall exergy efficiency of the plant [53]. Installation offeedwater heaters will decrease the temperature difference of the flue gas and feedwater, and willreduce irreversibilities encountered in the boiler heat exchanger. The decrease in pressure of thecondenser as well as increase in steam pressure and temperature will reduce exergy destruction andincrease the overall system performance. However, increase in the temperature is limited by the boilertube’s oxidation temperature and allowable stress [67]. Moreover, the benefit of higher revenue asa result of increase in performance of the plant due to the increase in temperature and installation offeedwater heaters should be balanced against the increase in the capital cost to ensure that the pay-backperiod on the investment is favourable [23]. A plant operating at its full capacity is shown to be moreeconomical and with higher exergy efficiency than those operating at part loads [52]. Because atfull capacity, the heat absorbed in the combustion chamber will increase together with the efficiencyof the boiler. However, this may not always be true for the combined heat and power plant: here,extraction ratio had a significant influence on the performance of the plant. As the plant with thesmallest extraction ratio will have the highest exergy efficiency and lowest energy efficiency [42].
The use of advanced exergy-based method for evaluation of inefficiencies in the thermalconversion systems should be recommended. This method accounts for the avoidable and unavoidableexergy destruction associated with the plant and interaction between the components. The unavoidablepart of exergy destruction cannot be improved, even using the best possible solution with availabletechnology [44], as a result of limitation in the design specifications of the plant. The efforts toimprove the plant should then be concentrated on the avoidable part so that the real thermodynamicinefficiencies and their causes can be identified [92].
6. Conclusions
Exergy analysis is a reliable method that can be used for the design, optimization, performanceevaluation and calculation of efficiency of a solid fuel-fired heat and power plant. The exergetic methodenables the main sources of loss to be identified, quantifies the irreversibilities that result from theentropy generated and provides direction for improving performance in the system. The application ofexergy analysis should be extended to biomass-based and biomass co-combustion fired power plantsso that important improvements can be made, because limited research work has been carried out inthese sectors. Turkey and India are the two major countries where exergy analysis has been applied tosolid fuel power plants, the majority of which are coal-fired. The results of the present review indicatethat extensive research should focus on the combustion and heat transfer processes in boilers in orderto optimise the performance of solid fuel-fired heat and power plants.
Acknowledgments: Financial support from University of Borås, Sweden and TETFund Nigeria through MichaelOkpara University of Agriculture, Umudike, Abia State, Nigeria is greatly appreciated.
Author Contributions: Francis Chinweuba Eboh is responsible for the literature survey and writing of themanuscript. Peter Ahlström and Tobias Richards supervised the research work.
Conflicts of Interest: The authors declare no conflict of interest.
Nomenclature
E energy rate (kW)Ex exergy rate (kW)ex specific exergy (kJ/kg)h specific enthalpy (kJ/kg)I irreversibility rate or exergy destruction rate (kW)
Energies 2017, 10, 165 25 of 29
m mass flow rate (kg/s)Q heat transfer rate (kW)S entropy rate (kW/K)s specific entropy (kJ/kgK)T temperature (K)W work transfer rate (kW)Subscriptsa airf fuelfg flue gashp hot productL lostp productpl plante exito outgen generationi input0 reference environment or dead stateSuperscriptsCh chemicalKe kinetic energyPe potential energyPh physicalAbbreviationsB boilerC combustorCEP condensate extraction pumpCFWH closed feed water heaterCHP combined heat and powerCond condenserFP feed water pumpHE heat exchangerHPT high pressure turbineIPT intermediate pressure turbineLPT low pressure turbineOFWH open feed water heaterHP hot productWTP waste-to-energyGreek letterη efficiency
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Introduction
The quantities of solid wastes increase as the population continues to grow throughout the world, whereas the available space for disposal decreases. If this waste is not properly treated and handled, it will pollute the air, land, ground water, and soil as well as has a negative impact on the hygienic conditions of the people [1]. A waste- to- energy (WTE) plant is one of the most robust and effective posttreatment options to decrease the volume of produced waste, reduce greenhouse gas emissions, and utilize the energy content in nonrecyclable waste for the production of electricity and heat, thereby reducing the dependence on fossil fuel.
For the efficient design of energy conversion processes, the chemical exergy and energy content of the fuel are basic properties to be considered to estimate the maximum
available energy entering the system for performance analysis and optimization of the entire process. This can be done by detecting and evaluating quantitatively the thermody-namic imperfection (exergy loss) of the process under consideration, its main sources of loss and possible ways of improving such process can be indicated [2]. Estimating the chemical exergy of fuel is an important step when performing exergy analysis [3] in waste- to- energy plants. Exergy analysis is a method that uses the conservation of mass and energy together with the second law of ther-modynamics. This analysis method is useful to achieve a more efficient energy- resource use because it enables the locations, types, and magnitudes of losses to be identified and to determine meaningful efficiencies [4].
However, because many solid fuels have unknown struc-tures and chemical compositions, their exergy values cannot be calculated directly because of the lack of standard
MODELING AND ANALYSIS
Estimating the specific chemical exergy of municipal solid wasteFrancis Chinweuba Eboh1,2, Peter Ahlström1 & Tobias Richards1
1Swedish Centre for Resource Recovery, University of Borås, 501 90 Borås, Sweden2Department of Mechanical Engineering, Michael Okpara University of Agriculture, Umudike, Abia, Nigeria
© 2016 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
KeywordsHigher heating value, municipal solid waste, specific chemical exergy, standard entropy, statistical model
CorrespondenceFrancis Chinweuba Eboh, Swedish Centre for Resource Recovery, University of Borås, 501 90 Borås, Sweden. E-mail: [email protected]
Funding InformationTETfund Academic Staff Training and Development, Nigeria.
Received: 13 January 2016; Revised: 21 April 2016; Accepted: 22 April 2016
Energy Science and Engineering 2016; 4(3): 217–231
doi: 10.1002/ese3.121
Abstract
A new model for predicting the specific chemical exergy of municipal solid waste (MSW) is presented; the model is based on the content of carbon, hy-drogen, oxygen, nitrogen, sulfur, and chlorine on a dry ash- free basis (daf). The proposed model was obtained from estimations of the higher heating value (HHV) and standard entropy of MSW using statistical analysis. The ultimate analysis of 56 different parts of MSW was used for the derivation of the HHV expression. In addition, 30 extra parts were used for validation. One hundred and seventeen relevant organic substances that represented the main constituents in MSW were used for derivation of the standard entropy of solid waste. The substances were divided into different waste fractions, and the standard entropies of each waste fraction and for the complete mixture were calculated. The specific chemical exergy of inorganic matter in the waste was also investigated by con-sidering the inorganic compounds in the ash. However, as a result of the ex-tremely low value calculated, the exergy of inorganic matter was ignored. The results obtained from the HHV model show a good correlation with the measured values and are comparable with other recent and previous models. The correla-tion of the standard entropy of the complete waste mixture is less accurate than the correlations of each individual waste fraction. However, the correlations give similar results for the specific chemical exergy, indicating that HHV has a greater impact when estimating the specific exergy of solid waste than entropy.
218 © 2016 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.
F. C. Eboh et al.Estimating the Specific Chemical Exergy of MSW
absolute entropy values [5]. Many models for the predic-tion and estimation of the chemical exergy of carbon- based fuels with complex bond interactions and unknown ther-modynamics properties have been proposed based on the characteristics of the known homogeneous organic sub-stances in the fuel. The first attempt was performed by Rant [6], involving the formulation of a semiempirical model to evaluate the availability (exergy) content of a structurally complicated material species. In that model, the chemical exergy of a fuel is evaluated from the com-putation of pure organic substances of known absolute entropies. Rant evaluated the ratios of the estimated chemi-cal exergies and the higher heating values for seven gases and 12 liquid organic substances. Szargut and Styrylska [7] improved Rant′s correlation by considering the chemi-cal composition of the fuels. They obtained correlation formulas to express the dependence between the ratio of the standard chemical exergy to the lower heating value using mass ratios of hydrogen, oxygen, nitrogen and sulfur to carbon that describe the chemical composition of the fuel. Due to lack of thermodynamic data, sulfur was not considered in their model for solid fuels, and their cor-relations were theoretically limited to Szargut′s reference environmental (R.E) model.
Using a model for estimating the thermodynamic prop-erties of coal, char, tar, and ash, Eisermann et al. [8] approximated the standard entropy of coal by comparing the behavior of the standard entropies of a number of aliphatic and aromatic hydrocarbons as a function of several elemental ratios: H/(C + N), O/(C + N), N/(C + N) and S/(C + N). Shieh and Fan [9] estimated the specific chemical exergy of a structurally complicated material by adopting the concepts of the dead (or reference) state and the properties of the constituents in the material based on the first and second laws of thermodynamics. It was assumed that the entropy of a fuel is equal to the entropies of its constituent elements. This assumption is not accurate in many cases. Ikumi et al. [10] developed a method for estimating the entropies of coals based on the mole ratios of hydrogen, oxygen, nitrogen, and sulfur elements to the carbon element. Bilgen and Kaygusuz [11] used the entropy correlation proposed by Eisermann et al. [8] to improve the Shieh and Fan [9] model for the calculation of the chemical exergy of coals, and Stepanov [5] applied the entropy model developed by Ikumi et al. [10] to modify Shieh and Fan [9] to calculate the exer-gies of coke- oven gases of different metallurgical mills. These models are limited to coal fuels only because their constituent organic compounds have been derived from the standard entropies of the relevant organic substances of coals.
Song et al. [3] developed a model based on Shieh and Fan [9] to estimate the specific entropy of the organic
matter in biomass used for the exergy calculations. Although their model showed a high accuracy and was simpler than the Szagut and Styrylska’s correlation, it has a limited application, as it is only applicable to biomass. Song et al. [12] also proposed a model for estimating the entropy of solid fuels and then extended the Shieh and Fan [9] model using the major organic constituents of solid fuel for the prediction of the specific chemical exergy of solid fuels. However, they combined the higher heating value derived on a dry basis (db) with values of the standard entropy obtained based on a dry ash- free basis (daf) for the estimation of the chemical exergy. Furthermore, their model cannot be used for estimating the chemical exergy of substances containing elements other than C, H, O, N, and S and for combustible materials, such as certain categories of leather, plastic, and rubber, that are part of municipal solid waste. To the author’s knowledge, no model has been found in the literature that is derived for predicting the chemical exergy of MSW.
The objective of this work is to propose a model for calculating the specific chemical exergy of MSW contain-ing the C, H, O, N, S, and Cl from its elemental com-positions on a dry ash- free basis.
Derivation of the Estimated Model
Municipal Solid Waste consists of a complex, heterogene-ous mixture of organic and inorganic substances. The organic elements in MSW are mainly C, H, O, N, S, and Cl, which can be obtained from ultimate analysis, whereas the inorganics are commonly Si, Ca, K, P, Al, Mg, Fe, S, Na, Zn, Cu, Mn, and Cr, from which their oxides can be obtained from ash analysis data. Previous reports have shown that the influence of inorganic matters on the exergy value can be neglected in solid fuel as a result of their relatively small value [3, 12].
The standard chemical exergy of a substance that is not present in the environment can be evaluated by con-sidering a reaction of the substance with other substances for which the chemical exergies are known [13]. The exact calculation of the chemical exergy of a material with complicated structures is difficult [14]; as a result, the standard chemical exergy of the substance in the environ-ment is not readily available.
Standard chemical exergy of a substance
The chemical exergy of a substance is equal to the maxi-mum amount of work that can be obtained from the substance by taking it to chemical equilibrium with the reference environment [15]. The standard exergy of a substance can be evaluated by considering an idealized reaction of the substance with other substances (generally
219© 2016 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.
Estimating the Specific Chemical Exergy of MSWF. C. Eboh et al.
reference substances) of known chemical exergies [16]. The known chemical exergies can be obtain from the table of standard chemical exergy based on Szargut’s R.E model (Model II), as shown in Table 1. With considera-tion of the reversible reaction for chemical formation of a compound, Szargut et al. [2] expressed the standard chemical exergy of elements or compounds as
where ΔGo
f, ni, and bo
chi represent the standard Gibbs energy
of formation, the mole fraction of component i in the mixture, and the standard chemical exergy of the con-stituent element i, respectively.
Calculation of the specific chemical exergy of municipal solid waste
For simplicity, suppose 1 kg of MSW (daf), expressed as CmHnNpOqCrSt, undergoes complete combustion at a standard state for the steady condition to produce carbon dioxide, water, nitrogen, hydrogen chloride, and sulfur dioxide as follows:
All substances are assumed to enter and exit at the refer-ence temperature, T0 = 298.15 K, and reference pressure, P0 = 101.325 kPa. The subscripts m, n, p, q, r, and t are the numbers of atoms of C, H, N, O, Cl, and S, respec-tively, in kmol/kg MSW or the molal compositions per kg of MSW expressed as:
where the elements in Equations (3–8) are expressed in wt% (daf). For the steady state, under the standard con-dition, the energy balance of the reaction in Equation (2) is given by
The entropy balance is expressed as
where W and Q are the work and heat transfer, respectively. Sgen is the entropy generated by the irreversibility in the reaction, and s0 and h0 represent the standard entropy and enthalpy, respectively.
Eliminating the heat transfer Q between Equation (9) and (10) gives the following:
(1)bo
ch= ΔGo
f+∑
inib
o
chi(kJ∕mol)
(2)
CmHnNpOqClrSt +(
m+ t−q
2+
n− r
4
)O2
→ mCO2 +(
n− r
2
)H2O+
P
2N2 + rHCl+ tSO2
(3)m=0.01C
12.011kmol/kg or
10C12.011
mol/kg
(4)n=0.01H
1.008kmol/kg or
10H1.008
mol/kg
(5)p=0.01N
14.007kmol/kg or
10N14.007
mol/kg
(6)q=0.01O
15.999kmol/kg or
10O15.999
mol/kg
(7)r=0.01Cl
35.45kmol/kg or
10Cl35.45
mol/kg
(8)t=0.01S
32.066kmol/kg or
10S32.066
mol/kg
(9)W =Q+h0
MSW+(
m+ t−q
2+
n− r
4
)h0
O2
−mh0
CO2
−(
n− r
2
)h0
H2O−
p
2h0
N2
− rh0
HCl− th0
SO2
(10)0 =
Q
To
+ s0
MSW+(
m+ t−q
2+
n− r
4
)s0
O2
−ms0
CO2
−(
n− r
2
)s0
H2O−
p
2s0
N2
− rs0
HCl− ts0
SO2
+Sgen
Table 1. Standard chemical exergy and standard entropies of various compounds.
Substance e0 (kJ/mol) s0 (kJ/mol K)
CO2 19.87 0.214H2Ol 0.95 0.070O2 3.97 0.205N2 0.72 0.192SO2 310.93 0.248SiO2 1.636 0.041HCl 85.5 0.187CaO 129.881 0.038K2O 412.544 0.102P2O5 377.155 0.117Al2O5 4.479 0.051MgO 62.417 0.027Fe2O3 17.656 0.087SO3 242.003 0.257Na2O 296.32 0.075MnO 122.390 0.060ZnO 37.080 0.042Cr 538.610 0.024Pb 226.940 0.065As 477.040 0.035Cd 290.920 0.052Cl 163.940 0.166
l, liquid phase.Source: [3, 17, 18].
220 © 2016 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.
F. C. Eboh et al.Estimating the Specific Chemical Exergy of MSW
The maximum work, Wmax, will occur when there is no irreversibility in the system. Hence, Equation (11) can be expressed as
or
where ΔHo
r represents the heat of reaction of the combus-
tion process, which is equal to the negative higher heating value [9], that is
then
Assume that the reaction in Equation (2) at 298.15 K and 101.325 kPa is an adiabatic process with no irrevers-ibility. The exergy balance equation, in absence of changes in the kinetic and potential energy for reacting systems, is given by
where e represents the specific chemical exergy. Substituting Equation (15) into Equation (16), the specific chemical exergy of MSW (daf), eMSW, is presented as
or
where e0 is the standard exergy in kJ/mol and s0 is the standard entropy in kJ/mol K, as tabulated in Table 1. s0
MSW is the standard entropy of municipal solid waste,
in kJ/K kg (daf), and HHVMSW is the higher heating value (HHV) of MSW, in kJ/kg (daf). The specific chemi-cal exergy of MSW can be calculated once the standard chemical exergies of CO2, H2O(l), N2, O2, SO2, and HCl; the higher heating value; and the absolute entropies are known.
Estimating the higher heating value of municipal solid waste
In the absence of a measured value, the HHV of fuel can be estimated from their elemental composition [19, 20]. In this study, the HHV estimate was performed by considering 56 data points and 30 data points of MSW samples for the derivation and validation of the
(11)
W =
[h0
MSW+(
m+ t−q
2+
n− r
4
)h0
O2
−hs0
CO2
−(
n− r
2
)h0
H2O−
p
2h0
N2
− rh0
HCl− th0
SO2
]
−To
[s0
MSW+(
m+ t−q
2+
n− r
4
)s0
O2
−hs0
CO2
−(
n− r
2
)s0
H2O−
p
2s0
N2
− rs0
HCl− ts0
SO2
]−ToSgen
(12)
Wmax
=
[h0
MSW+(
m+ t−q
2+
n− r
4
)h0
O2
−mh0
CO2
−(
n− r
2
)h0
H2O−
p
2h0
N2
− rh0
HCl− th0
SO2
]
−To
[s0
MSW+(
m+ t−q
2+
n− r
4
)s0
O2
−ms0
CO2
−(
n− r
2
)s0
H2O−
p
2s0
N2
− rs0
HCl− ts0
SO2
]
(13)
Wmax
= −ΔHo
r−To
[s0
MSW+(
m+ t−q
2+
n− r
4
)s0
O2
−ms0
CO2
−(
n− r
2
)s0
H2O−
p
2s0
N2
− rs0
HCl− ts0
SO2
]
(14)ΔHo
r= −HHV
(15)
Wmax
=HHV−To
[s
msw+(
m+ t−q
2+
n− r
4
)s0
O2
−ms0
CO2
−(
n− r
2
)s0
H2O−
p
2s0
N2
− rs0
HCl− ts0
SO2
]
(16)
0 = −Wmax
+eMSW
+(
m+ t−q
2+
n− r
4
)e0
O2
−me0
CO2
−(
n− r
2
)e0
H2O−
p
2e0
N2
− re0
HCl− te0
SO2
(17)
eMSW
=HHV−To
[s0
MSW+(
m+ t−q
2+
n− r
4
)s0
O2
−ms0
CO2
−(
n− r
2
)s0
H2O−
p
2s0
N2
− rs0
HCl− ts0
SO2
]+me0
CO2
+(
n− r
2
)e0
H2O+
p
2e0
N2
+ re0
HCl
+e0
SO2
−(
m+ t−q
2+
n− r
4
)e0
O2
(18)
eMSW =m
[ (e0
CO2+Tos
0
CO2
)−(
e0
O2+Tos
0
O2
)]
+n
2
[ (e0
H2O+Tos
0
H2O
)−
1
2
(e0
O2+Tos
0
O2
)]
+q
2
(e0
O2+Tos
0
O2
)+
p
2
(e0
N2+Tos
0
N2
)
+ t
[ (e0
SO2+Tos
0
SO2
)−(
e0
O2+Tos
0
O2
)]
−r
2
[ (e0
H2O+Tos
0
H2O
)−
1
2
(e0
O2+Tos
0
O2
)
−2(
e0
HCl+Tos
0
HCl
) ]+HHVMSW −Tos
0
MSW(kJ∕kg)
221© 2016 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.
Estimating the Specific Chemical Exergy of MSWF. C. Eboh et al.
correlation, respectively; in addition, the chemical com-position and HHV of each sample was collected from the published literature and presented in Tables A1 and A2 (Appendix 1). These data cover six categories of combustible MSW fractions, namely, food, wood, paper, textiles, plastics, and rubber waste [21, 22]. For the selection of a suitable model, 9 assumed algebraic expres-sions from previous work based on the correlation of the HHV and ultimate analysis of solid fuel (daf) were used, as shown in Table 2. Using regression analysis based on the generalized method of least squares [19] on the 56 data points, the constant terms of these alge-braic expressions were evaluated. The correlation that has the least error and highest coefficient of determina-tion, as described in Selection of the best correlation, was selected. The newly estimated correlation was compared with the experimental values of HHV and the results of previous models collected from the open literature, for further validation.
Estimating the standard entropy of municipal solid waste
Municipal solid waste (MSW) contains mainly organic polymers in plastics, wood, paper, textile, rubber, and food waste. The entropies of these polymers in the organic waste are estimated or evaluated by the entropies of their organic monomers structures as there is no significant difference between the entropies of the solid organic monomers and their polymers [24]. The difference ranges from 0.1 to 12.5% (Table 3).
The standard entropy of MSW was derived from organic substances with known standard entropies. In this work, 117 samples of organic compounds relevant to MSW were collected from the published literature [3, 8, 10, 12, 17, 24] and tabulated in Table A3 (Appendix 1). The data points where selected based on the molecular struc-tures of the organic substances that are associated or
linked with the formation of larger molecular structure network of municipal solid waste. The organic com-pounds were grouped into the six categories of waste fractions, as previously used for the higher heating value, namely: food, plastic, textile, rubber, wood, and paper. This was accomplished by considering the molecular structures of the organic substances that can be found in each of the molecular structures of the waste frac-tions. For wood, it contains three major chemical com-ponents: cellulose, hemicelluloses, and lignin [25]. Each of the chemical structure of the wood constituents [26, 27] was studied and organic compounds (monomers) that can be made or found from these structures are selected. In the food, the main structural elements iden-tified are proteins, carbohydrate and lipids [28]. The molecular structures of these food components [29, 30] were also investigated and organic monomers that are linked with the structure are selected. The same method was carried on chemical structures of plastic [31], textile [32, 33], and rubber [34] materials with identifications of biologically important molecules which form the building structure of their polymers. Based on the abso-lute entropies and elemental compositions of the selected organic substances, a first- order polynomial correlation
Table 2. Assumed correlations used for the selection of the proposed model for HHV (daf).
S. No. Assumed expression Criteria for selection Reference
1. HHV = aC + bH + cO + dN + eS + fCl Assuming fuel HHV to be a linear function of it constituents. Current model2. HHV = aC + bH + cO + dN + eS Based on Gumz’s criteria [19]3. HHV = a0 + bH + cO + dN + eS + fCl Based on Chang’s criteria [23]4. HHV = aC + b (H − O/8) + eS Based on Dulong’s criteria [19]5. HHV = aC + bH + cO + eS Based on modified version of Dulong’s criteria [19]6. HHV = a0 + aC + bH + cO2 Based on Seyler’s criteria [19]7. HHV = a (C − (3/8) O) + b (3/8) O) + c (H − (1/6) O) + eS Based on Steuer’s criteria [19]8. HHV = a (C − 0.75 (O/2)) + b (H − 0.125 (O/2)) + eS Based Sumegi’s criteria [19]9. HHV = aC + bH + c ((N + O − 1)/8) + eS Dulong- Berthelot‘s criteria [19]
where C, H, O, N, S, and Cl represents carbon, hydrogen, oxygen, nitrogen, sulfur, and chlorine, respectively, in % by mass on a dry ash free basis. a0, a, b, c, d, e, and f are constants of correlation.
Table 3. Standard entropies of some solid organic polymers and monomers at 298.15 K.
S. No. Substance S0 (J/mol K)
1. C6H11NO 173.21(C6H11NO)n 173.0
2 C4H4O4 157.2(C4H4O4)n 151.4
3 C15H10N2O2 332.5(C15H10N2O2)n 294
4 C13H24O2 401.9(C13H24O2)n 351.6
Source: [24].
222 © 2016 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.
F. C. Eboh et al.Estimating the Specific Chemical Exergy of MSW
was derived statistically for the standard entropy of the waste fractions and the mixture.
Selection of the best correlation
Three statistical parameters were used as evaluating parameters for both HHV and the standard entropy of MSW, which are computed as follows:
where Zest and Zexp denote the estimated and experimental values, respectively. Zexp is the experimental average value. AAE is the average error of a correlation. A smaller error of correlation will occur when AAE is low, which indicates higher accuracy. ABE denotes the average bias error of correlation. A positive value of ABE indicates an overall overestimation, whereas a negative value implies an overall underestimation. The smaller the absolute value of ABE, the smaller the bias of correlation. R2 is used as a com-prehensive parameter to measure the accuracy of the model. A higher R2 value means a better estimation and fitting [20]. These three parameters are the important statistical criteria and are primarily employed to assess correlations [3, 12, 19].
Specific chemical exergy of inorganic matter in municipal solid waste
Inorganic substances of waste materials are contained in the ash and obtained from complete combustion of solid fuel; ash is mainly contained in various metallic oxides and has a high thermal stability [35, 36]. The specific chemical exergy of inorganic matter in kJ/kg MSW was calculated from the major ash compositions data in Table 4 from a stoker- type incinerator [37] as follows [3, 12, 36]:
where ni represents number of moles of the component in inorganic matter, in mol/kg. e0
ioc and xi are the standard
chemical exergy and mole fraction of components i in inorganic matter, respectively. R is the universal gas con-stant, 0.0083145 kJ/mol K, and A is the ash content of MSW in wt%.
Results and Discussion
Correlation based on the higher heating value
For the higher heating value of MSW, the correlation derived that showed the minimum error with a higher accuracy among the nine assumed correlations used in Table 2 was expressed as
The results of the validation of the derived model and the comparison with published correlations using the experi-mental values of 30 samples of MSW in the different catego-ries of food, wood, plastic, textile, rubber, and paper waste are shown in Table 5 and represented in Figures 1–5. Figures 1, 2, and 4 show the best correlation with experi-mental data (highest coefficient of determination), repre-senting the model developed in this work, model by Channiwala and Parikh [19] and Dulong’s correlation. However, the proposed model shows significantly better esti-mations when considering the errors (AAE and ABE) com-pared to the other models. This is not surprising, as these models have been derived from mixed solid fuel and coal. Figure 3 shows a correlation proposed by Sheng and Azevedo [20]. Although the correlation has a good coefficient of determination (R2 = 0.92), it has a higher error and
(19)
Average absolute error (AAE) =1
n
n∑
i=1
|||||
Zest −Zexp
Zexp
|||||×100%
(20)Average bias error (ABE) =1
n
n∑
i=1
Zest −Zexp
Zexp
×100%
(21)
Coefficient of determination (R2) = 1−
n∑
i=1
(Zest −Zexp
)2
(Zexp − Zexp
)2
(22)eioc = 0.01A(∑
nixie0
ioc+RTo
∑nixilnxi
)(kJ/kg)
(23)HHV = 0.364C+0.863H−0.075O
+0.028N−1.633S+0.062Cl (MJ∕kg)
35.8%≤C≤86.1%,4.1%≤H≤13.9%,0.0%
≤O≤54.9%,0.0%≤N≤20.3%,0.0%
≤S≤2.7%,0.0%≤Cl≤56.4%,
13.0 MJ∕kg≤HHV≤43.2 MJ∕kg.
Table 4. Chemical composition of MSW ashes.
Component Bottom ash, BA (wt%) Fly ash, FA (wt%)
SiO2 37.8 2.47CaO 20.79 44.5K2O 0.85 3.01P2O5 3.63 0.26Al2O3 13.4 0.55MgO 2.91 0.57Fe2O3 7.46 0.32SO3 1.01 1.61Na2O 5.38 4.39ZnO 0.52 2.25CuO 0.51 0.096MnO 0.17 0.04Cr 0.63 0.008Pb 0.22 0.51As 0.021 0.062Cd 0.0003 0.003Cl 3.51 35.15
Source: [37].
223© 2016 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.
Estimating the Specific Chemical Exergy of MSWF. C. Eboh et al.
underestimated the HHV. In addition, the correlation is lim-ited to biomass. The correlation proposed by Chang (Table 5 and Fig. 5) has a considerable accuracy, with a coefficient of determination of R2 = 0.93. Although this correlation was derived from MSW, it overestimated the correlation and has a higher error value when compared with the present model.
Standard entropy of municipal solid fuel
For the prediction of the standard entropy of the organic substance in MSW, a correlation in the form of the
first- order polynomial was used. The five correlations derived for estimating the standard entropy of waste fractions and the mixture of waste were expressed as follows:
For Plastic waste
(24)s0
pl= 0.0087C+0.0753H+0.0134O
+0.0077N+0.0084Cl (kJ∕K kg)
10.3%≤C≤94.7%,0.0%≤H≤14.3%,0.0%≤O≤54.2%,
0.0%≤N≤66.7%.
Table 5. Derived correlation compared with previous models.
S No. Name Correlation (MJ/kg) Application AAE (%) ABE (%) R2 Reference
1. Proposed Model
HHV = 0.364C + 0.863H − 0.075O + 0.028N − 1.633S + 0.062Cl
MSW 5.738 0.032 0.95 Current model
2. Channiwala and Parikh
HHV* = 0.3491C* + 1.1783H* + 0.1005S* − 0.1034O* − 0.0151N* − 0.0211A*
Mixed waste
6.456 2.254 0.95 [19]
3. Sheng and Azevedo
HHV* = −1.3675 + 0.3137C* + 0.7009H* + 0.0318 (100 − C* − H* − A*)
Biomass 9.657 −3.650 0.92 [20]
4. Dulong HHV = 0.3383C + 1.443 (H − (O/8)) + 0.0942S
Coal 11.822 −4.832 0.95 [19]
5. Chang HHV = 35.8368 + 0.7523H − 0.2674S − 0.4654O − 0.3814Cl − 0.2802N
MSW 7.234 3.067 0.93 [19]
(*) shows the correlations obtained in % by mass on a dry basis, whereas the others are on dry ash- free basis.
Figure 1. Comparison between the experimental and the estimated HHV by the developed correlation.
Figure 2. Comparison between the experimental and the estimated HHV by the Channiwala and Parikh [19] correlation.
Figure 3. Comparison between the experimental and the estimated HHV by the Sheng and Azevedo [20] correlation.
Figure 4. Comparison between the experimental and the estimated HHV by Dulong’s correlation.
224 © 2016 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.
F. C. Eboh et al.Estimating the Specific Chemical Exergy of MSW
With ABE, AAE, and R2 of 0.722, 7.314, and 0.7674, respectively.
For Textile/Rubber waste
With ABE, AAE, and R2 of 0.714, 6.476, and 0.5457, respectively.
For Wood/Paper waste
With ABE, AAE, and R2 of 0.329, 5.215, and 0.728, respectively.
For Food waste
With ABE, AAE, and R2 of 0.414, 5.886, and 0.6922, respectively.
For Mixed waste
with ABE, AAE, and R2 of 1.118, 8.293, and 0.5414, respectively.
Comparing the five equations obtained, the results show that the standard entropy correlations for each waste frac-tion in MSW are more accurate than the standard entropy correlation for the waste mixture. This is as a result of complicated mixture, heterogeneous molecule structure and variation in municipal solid waste chemical composi-tions and properties. Nevertheless, because the standard entropy of plastic, textile/Rubber, wood/paper, food, and waste mixture gave similar average values for the specific exergy of the MSW estimation of 24,359, 24,364, 24,426, 24,393, and 24,387 (kJ/kg), respectively, the correlation of the standard entropy of the waste mixture can be used for the derivation of exergy.
Specific chemical exergy of municipal solid fuel (daf) and specific chemical exergy of ash
By substituting Equation (3)–(8), (23), and (24)–(28) into Equation (18), along with the standard chemical exergy data from Table 1, the specific chemical exergy of solid waste on a dry ash- free basis can be expressed as follows:
For Plastic waste:
For Textile/Rubber waste:
For Wood/Paper waste
For Food waste
For mixed waste
The minimum, maximum, and average specific exergy values of municipal solid waste calculated were 17,602, 43,396, and 24,387 in (kJ/kg), respectively. Although Equation (33) slightly underestimated the specific chemical exergy calculated by Equations (29) and (30), that is, an ABE of −0.139 and −0.113, respectively, and slightly
(25)s0
tr= 0.0097C+0.0635H+0.0128O+0.0136N
+0.0165S (kJ∕K kg)
15.8%≤C≤95.1%,3.0%≤H≤9.7%,0.0%≤O≤55.2%,
0.0%≤N≤66.7%,0.0%≤S≤42.1%
(26)
s0
wp= 0.0162C+0.0116H+0.0081O+0.00691Cl (kJ∕K kg)
26.7%≤C≤77.8%,0.4%≤H≤7.7%,5.1%≤O≤71.1%,
0.0%≤Cl≤66.3%
(27)
s0
fo= 0.0065C+0.0808H+0.0127O
+0.0101N+0.0100S (kJ∕K kg)
19.2%≤C≤92.3%,1.4%≤H≤14.1%,0.0%≤O
≤59.7%,0.0%≤N≤51.9%,0.0%≤S≤34.0%
(28)
s0
msw= 0.0101C+0.0630H+0.0106O+0.0108N
+0.0155S+0.0084Cl (kJ∕K kg)
10.3%≤C≤95.1%,0.00%≤H≤14.3%,0.0%≤O≤71.1%,0.0%
≤N≤66.7%,0.0%≤S≤42.1%,0.0%≤Cl≤89.7%,
(29)eP = 376.879C+787.351H−58.654O+46.398N
−1533.261S+100.981Cl (kJ∕kg)
(30)e
TR= 376.580C+790.869H−58.475O+44.639N
−1538.180S+98.566Cl (kJ∕kg)
(31)e
WP= 374.642C+806.343H−57.074O+48.693N
−1533.261S+101.425Cl (kJ∕kg)
(32)Food e
F= 377.535C+785.711H−58.446O+45.682N
−1536.242S+103.486Cl (kJ∕kg)
(33)e
msw= 376.461C+791.018H−57.819O+45.473N
−1536.242S+100.981Cl (kJ∕kg)
Figure 5. Comparison between the experimental and the estimated HHV by Chang’s correlation.
225© 2016 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.
Estimating the Specific Chemical Exergy of MSWF. C. Eboh et al.
overestimated the exergy estimated by Equations (31) and (32), that is, an ABE of 0.179 and 0.009, respectively, when compared, the coefficient of determination shows that Equations (29–32) are similar to Equation (33) (i.e., a value of 1 was achieved in all cases). This result indi-cates that Equation (33) can be used to estimate the specific exergy of municipal solid waste, that is, the HHV has more impact on the exergy.
The overall average ratio of the specific exergy of MSW developed with the higher heating value was obtained as 1.036, showing that the value of exergy is slightly higher than the HHV. The ratio of exergies to heating values obtained in this work is similarly when compared with Szargut and Styrylska [7] model with ratio of 1.047. As their methods were commonly used for evaluating the chemical exergy of solid fuels. This result indicates that the present model is reliable and accurate. However, the slight variation in the ratios is due to different types of fuel used.
The specific exergies of inorganic matter in MSW cal-culated from Equation (22) using the chemical ash com-position data in Table 4 are 0.86 and 1.79 kJ/kg for bottom ash and fly ash, respectively. These values are very small when compared with the average specific chemical exergy values, 24,387 kJ/kg of MSW (daf) estimated, demonstrat-ing that the specific chemical exergy of inorganic matter can be neglected.
Conclusions
Following the evaluations of the previous equations for estimating the specific chemical exergy of solid fuels, the present proposed models in this study were found to be more accurate when using municipal solid waste as a fuel. All other methods have either ignored the inclusion of chlorine from the elemental compositions of waste or have used other solid fuels with a limited amount of MSW. In this work, a simple method for estimating the specific exergy of municipal solid waste on (daf) from their ultimate analysis based on HHV, standard entropy, and exergy equation of reaction was proposed.
The higher heating values of the estimated MSW showed a good correlation and a higher accuracy compared with previous models. It is calculated as
The standard entropy of the estimated waste mixture has a rather low accuracy when compared with the waste fractions. However, the standard entropy can be used for the estimation of the specific chemical exergy of a solid, as it showed a similar result with the standard entropy of waste fractions; the standard entropy is expressed as
This result indicates that a higher heating value has more impact on the derivation of the specific chemical exergy of solid waste than entropy. In other words, the specific exergy of MSW mainly depends on the values of HHV.
Due to very low calculated values of specific chemical exergy of inorganic matter in MSW, the specific chemical exergy developed in this work is equal to the specific chemical exergy of the organic matter in MSW and is presented as
The results obtained demonstrate that the specific chemical exergy is always slightly higher than the highest heating value, indicating the validity and accuracy of the model.
The present correlation can be accepted for estimating the specific chemical exergy of MSW using the elemental compositions of the fuel within the range specified based on a dry ash- free basis. The model is applicable for the efficient modeling of a combustion system in a waste- to- energy plant.
Acknowledgments
The authors acknowledge the Nigerian Government and Michael Okpara University of Agriculture Umudike, Abia State, Nigeria, for supporting this work through TETfund Academic Staff Training and Development.
Conflict of Interest
None declared.
Nomenclature
AAE average absolute errorA ash content in the waste (%)ABE average bias errorE chemical exergy (kJ)e specific chemical exergy (kJ/kg) or (kJ/mol)FC fixed carbon (%)G Gibbs energy (kJ/kg) or (kJ/mol)H enthalpy (kJ/kg)HHV higher heating value (kJ/kg)MSW municipal solid wasteP pressure (kpa)R2 coefficient of determination
HHV= 0.364C+0.863H−0.075O+0.028N
−1.633S+0.062Cl (MJ∕kg)
s0
msw= 0.0101C+0.0630H+0.0106O+0.0108N
+0.0155S+0.0084Cl (kJ∕K kg).
Emsw = 376.461C+791.018H−57.819O
+45.473N−1536.242S+100.981Cl (kJ∕kg).
226 © 2016 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.
F. C. Eboh et al.Estimating the Specific Chemical Exergy of MSW
S entropy (kJ/K)Sgen entropy generateds specific entropy (kJ/kg K) or (kJ/mol K)T Temperature (K)V volatile matter (%)
Subscripts
ba bottom ashdaf dry ash-free basisest estimateexp experimentfa fly ashf formationfo foodioc inorganic compoundmax maximummsw municipal solid waste or mixed solid wasteo standard statepl plasticR reactiontr textile/rubberwp wood/paper
Superscripts
0 reference state
Greek Symbols
Δ change∑ summation
References
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waste incineration. World Bank Technical Guidance
Report. Washington D.C.
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F. C. Eboh et al.Estimating the Specific Chemical Exergy of MSW
Appendix
Table A1. Chemical characteristics of MSW (daf) used for derivation.
MSW groups, Subgroup and variety
Proximate analysis (wt %) Ultimate analysis (wt %)HHV (MJ/kg) ReferenceA V FC C H O N S Cl
Food waste 1. Flour – – – 42.78 6.19 48.39 2.48 0.15 – 18.157 [21] 2. Rice 0.40 84.42 15.18 45.97 6.35 45.74 1.67 0.25 – 18.213 [22] 3. Peanut shell – – – 53.6 6.70 38.3 1.20 0.20 – 20.598 [21] 4. Pea – – – 42.13 5.88 48.62 3.14 0.22 – 16.533 [21] 5. Scallion – – – 48.12 6.27 41.74 3.09 0.78 – 18.042 [21] 6. Potato 3.15 79.52 17.33 44.41 5.33 47.82 1.81 0.64 – 17.656 [22] 7. Spinach 15.97 65.26 18.77 47.58 6.48 43.93 1.57 0.43 – 20.326 [22] 8. Celery 14.58 65.36 20.06 38.46 6.16 54.52 0.21 0.65 – 15.886 [22] 9. Pakchoi 18.44 63.97 17.59 43.37 5.93 48.64 1.25 0.81 – 23.173 [22]10. Tangerine peel 2.91 76.49 20.6 48.74 5.92 43.83 1.43 0.08 – 19.024 [22]11. Banana peel 10.85 64.38 24.77 35.8 4.79 54.93 4.37 0.10 – 18.385 [22]12. Orange peel 2.44 76.27 21.29 43.93 5.64 48.93 1.34 0.07 0.08 18.550 [21]13. Rib – – – 52.92 8.83 25.63 2.29 0.32 – 17.277 [21]14. Fish bone 39.82 56.25 3.93 63.87 8.01 19.08 8.39 0.64 – 26.245 [21]15. Food waste 6.1 82.11 18.00 51.54 7.14 37.06 3.13 0.21 0.92 21.619 [38]Wood waste16. Poplar wood 7.54 73.85 18.61 51.36 5.86 41.00 1.52 0.22 – 20.009 [22]17. Poplar leaf 15.69 68.74 15.57 49.54 5.24 43.30 1.32 0.59 – 19.986 [22]18. Chinar leaf 9.23 69.74 21.03 52.95 4.88 40.51 1.01 0.65 – 21.064 [22]19. Gingko leaf 11.62 73.19 15.19 41.35 5.54 50.88 1.36 0.87 – 17.289 [22]20. Pine wood 0.95 83.5 15.54 50.51 5.95 43.39 0.11 0.03 – 19.834 [21]21. Sawdust 0.42 81 18.58 49.42 7.26 42.92 0.39 0.01 – 21.267 [21]22. Wood 1.00 81.62 18.38 50.10 6.16 43.47 0.17 0.02 0.07 19.697 [38]23. Wood chips 1.95 82.66 15.4 49.54 6.21 44.06 0.12 0.04 0.03 19.544 [21]24. Bamboo 0.69 81.03 18.27 50.46 6.32 42.73 0.22 0.1 0.16 19.716 [21]25. Leaves 8.92 73.7 17.38 47.25 5.57 46.26 0.19 0.73 – 18.882 [21]26. Pine needles – – – 52.57 6.3 40.44 0.54 0.16 – 20.843 [21]27. King grass 7.44 74.12 18.43 46.91 5.89 46.3 0.7 0.21 – 19.428 [21]Paper waste28. Blank printing paper
10.69 79.33 9.98 45.12 5.31 48.91 0.38 0.28 – 15.127 [22]
29. Tissue paper 0.52 90.47 9.01 45.18 6.13 48.32 0.25 0.11 – 17.340 [22]30. Newspaper 8.07 79.54 12.39 48.01 5.71 45.86 0.33 0.09 – 18.666 [22]31. Magazine 29.49 62.44 8.07 41.04 8.99 49.15 0.42 0.4 – 16.771 [21]32. Writing paper – – – 43.66 5.84 50.16 0.16 0.18 – 13.69 [21]33. Cardboard 5.27 81.75 12.97 46.09 5.36 48.02 0.32 0.21 – 18.239 [21]34. Carton 7.22 83.95 8.82 48.97 6.14 44.52 0.21 0.16 – 18.430 [21]35. Printing paper 9.70 82.83 17.17 47.51 5.98 46.25 0.14 0.03 0.09 18.051 [38]36. Packaging paper 12.2 85.88 14.12 46.92 5.92 46.74 0.22 0.09 0.10 17.654 [38]Textile37. Absorbent cotton gauze
0.14 94.85 5.01 46.74 5.69 47.23 0.27 0.08 – 14.664 [22]
38. Cotton cloth 3.09 78.71 18.21 56.49 5.87 33.3 3.52 0.18 0.65 14.664 [21]39. Wool 1.24 84.76 14.00 60.07 4.24 31.48 2.65 1.55 – 21.183 [21]40. Acrylic fiber 0.14 75.25 24.61 66.78 5.2 7.31 20.26 0.45 – 29.812 [21]41. Chemical fiber – – – 48.09 7.16 34.06 9.43 1.26 – 21.959 [21]42. Polyester taffeta 0.44 90.63 8.93 60.1 4.5 35.11 0.28 0.01 – 22.178 [21]43. Terylene 0.49 88.6 10.91 62.16 4.14 33.12 0.29 0.28 – 20.963 [22]44. Textiles 1.40 82.86 17.14 52.54 6.19 39.26 1.76 0.20 1.42 21.197 [38]Plastics waste45. PS 0.04 99.57 0.39 86.06 6.27 1.93 5.73 – – 38.946 [22]46. LDPE – 99.98 0.02 85.98 11.20 2.61 0.21 – – 46.480 [22]47. HDPE 0.18 99.57 0.25 85.35 12.70 1.90 0.05 0.14 – 46.444 [22]
229© 2016 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.
Estimating the Specific Chemical Exergy of MSWF. C. Eboh et al.
Table A2. Chemical characteristics of MSW (daf) used for validation.
MSW groups, Subgroup and variety
Proximate analysis (wt %) Ultimate analysis (wt %)HHV (MJ/kg) ReferenceA V FC C H O N S Cl
Food waste1. Rice 0.42 87.74 11.84 44.2 5.73 48.75 1.20 0.1 0.02 18.048 [21]2. Potato – – – 42.09 6.5 49.06 2.12 0.23 – 16.912 [21]3. Orange peel 2.91 76.49 20.6 48.74 5.92 43.72 1.43 0.19 – 19.024 [21]4. Rib 38.22 61.56 0.23 51.61 6.38 31.91 9.48 0.69 – 22.716 [21]Wood waste5. Wood 0.82 81.64 17.54 48.35 6.62 44.7 0.04 0.29 – 20.868 [21]6. Wood chips 3.45 81.5 15.05 49.03 5.69 44.98 0.22 0.07 – 19.255 [21]7. Wooden chopsticks 2.18 83.45 14.37 48.79 5.16 45.7 0.3 0.04 – 19.355 [39]8. Bamboo 1.79 81.36 16.84 51.42 6.01 41.92 0.36 0.29 – 19.974 [40]9. Leaves 9.43 74.32 16.25 47.18 5.61 46.35 0.18 0.68 – 20.278 [21]10. King grass – – – 48.37 6.30 44.58 0.49 0.25 – 22.127 [21]Paper waste11. Newspaper 5.43 85.04 9.53 45.24 7.17 47.1 0.25 0.23 – 17.204 [21]12. Printing paper 12.3 87.65 0.04 44.93 4.55 50.43 0.09 – – 16.233 [21]13. Cardboard – – – 46.71 5.31 47.35 0.32 0.32 – 18.367 [21]14. Toilet paper 0.52 90.47 9.01 45.18 6.13 48.32 0.25 0.11 – 17.337 [21]15. Paper food cartons 6.93 – – 48.07 6.55 45.04 0.16 0.17 – 18.137 [41]16. Magazine stock 29.26 – – 46.55 6.56 46.44 0.16 0.30 – 17.967 [41]17. Plastic- coated paper 2.77 – – 44.53 6.35 46.80 0.19 0.08 – 17.556 [41]Textile18. Cotton cloth 1.52 84.53 13.95 46.51 5.8 46.98 0.43 0.28 – 17.699 [22]19. Cotton 1.45 86.7 11.85 46.19 6.12 47.07 0.54 0.08 – 17.500 [21]20. Wool – – – 58.53 6.48 18.23 15.12 1.65 – 23.632 [22]21. Shoe heel and sole 30.09 – – 76.13 10.14 11.10 0.72 1.92 – 36.676 [41]22. leather 10.1 – – 66.74 8.90 12.79 11.12 0.44 – 22.892 [41]23. Upholstery 2.80 – – 48.46 6.28 44.86 0.31 0.10 – 17.891 [41]Plastics waste24. PE 0.15 99.85 – 85.45 14.32 – 0.16 0.07 – 46.388 [42]25. PP 0.16 99.84 – 84.3 14.44 1.05 0.18 0.03 – 45.842 [21]26. PVC 0.04 95.16 4.8 38.75 5.21 – 0.22 – 55.82 22.575 [21]27. Polyurethane 4.38 87.29 8.32 66.17 6.55 18.46 6.26 0.02 2.53 27.300 [41]28. Plastic film 6.72 – – 72.05 10.42 16.96 0.49 0.08 – 34.519 [41]Rubber waste29. Rubber 15.38 65.26 19.36 89.18 8.54 – 1.23 1.05 – 39.473 [21]30. Tire 19.27 63.11 17.61 88.56 8.52 0.88 0.75 1.29 – 37.364 [21]
All the proximate, ultimate analysis data and HHV on dry basis are converted to dry ash- free basis. Also all HHV are converted to MJ/kg.
MSW groups, Subgroup and variety
Proximate analysis (wt %) Ultimate analysis (wt %)HHV (MJ/kg) ReferenceA V FC C H O N S Cl
48. PVC – 94.93 5.07 38.34 4.47 – 0.23 0.61 56.35 20.830 [22]49. PET 0.09 90.44 9.47 63.01 4.27 32.69 0.04 – – 23.111 [22]50. PE 0.15 99.85 – 85.45 14.32 – 0.16 0.07 – 46.388 [21]51. PP 0.02 99.97 0.01 85.41 12.51 1.85 0.23 – – 46.248 [21]52. Packaging plastic 3.90 95.21 4.79 75.75 9.78 12.00 0.35 0.03 2.08 26.951 [38]53. Other plastic 1.30 99.09 0.91 84.90 9.63 0.97 3.35 0.03 1.11 41.135 [38]Rubber waste54. Rubber 8.36 84.77 6.86 77.72 10.12 7.42 0 2.66 2.08 25.474 [21]55. Tire 25.70 68.05 6.25 79.19 8.45 11.38 0.69 0.28 – 35.654 [21]Other combustibles56. Other combustibles
20.40 90.83 9.17 70.48 8.79 17.53 1.63 0.83 0.74 32.161 [38]
All the proximate, ultimate analysis data and HHV on dry basis are converted to dry ash- free basis. Also all HHV are converted to MJ/kg.
Table A1. Continued.
230 © 2016 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.
F. C. Eboh et al.Estimating the Specific Chemical Exergy of MSW
Name Formula S0 (kJ/kg K)
55. Naphthalene C10H8 1.30656. Succinic acidic C4H6O4 1.41757. Cyanuric acid C3H3N3O3 1.94758. Acetamide C2H5NO 1.84059. Durene C10H14 1.16660. Hexamethlenetetramine C6H12N4 1.11661. Triphenylene C18H12 1.27362. Hydroquinone C6H602 1.18263. Melamine C3H6N6 1.25164. Phthalic acid C8H6O4 1.21565. Phathalic anhydide C8H4O3 1.40566. Triethylenediamine C6H12N2 1.94767. 4,4′- diphenylmethane diisocyanate
C15H10N2O2 1.329
68. Polyisocyanurate (C15H10N2O2)n 1.17569. Tridecanolactone C13H24O2 1.89370. Polytridecanolactone (C13H24O2)n 1.65671. Polyvinylidene chloride (C2H2Cl2)n 0.89472. Polyvinyl chloride (C2H3Cl)n 1.04273. poly(1- butene), isotactic (C4H8)n 1.83674. Polystyrene (C8H8)n 1.294Textile75. 3- Nitrobenzoic acid C7H5NO4 1.22776. 1,2- Diphenylethene C14H12 1.3977. Adipic acid C6H10O4 1.50478. 2- Methlnaphthalene C11H10 1.54779. Acenaphthene C12H10 1.22580. Anthracene C14H10 1.16481. 1,4- Benzoquinone C6H4O2 1.50682. Diphenylamine C12H11N 1.66683. Pyrene C16H10 1.11284. Thiourea CH4N2S 1.52385. Ammonium thiocyanate CH4N2S 1.84286. 3- Nitroaniline C6H6N2O2 1.27687. Resorcinol C6H6O2 1.34188. Triphenylmethane C19H16 1.27789. Triphenylmethanol C19H16O 1.26590. Isoquinoline C9H7N 1.32491. Acridine C13H9N 1.16192. 2- Nitrobenzoic acid C7H5O4N 1.24793. 1,3- Phenylenediamine C6H8N2 1.42994. Dicyanodiamide C2H4N4 1.53895. ε- Caprolactam C6H11NO 1.53196. Poly- ε- Caprolactam (C6H11NO)n 1.52997. Polyglycolide (C4H4O4)n 1.304Wood98. L-Sorbose C6H12O6 1.22699. o- Cresol C7H8O 1.530100. Oxalic acid C2H2O4 1.220101. p- Cresol C7H8O 1.547102. Sucrose C12H22O11 1.052103. D-Mannitol C6H14O6 1.309104. Pentachlorophenol C6HCl5O 0.946105. Galactose C6H12O6 1.14106. Phenol C6H6O 1.53107. 2- Hydroxybenzoic acid C7H6O3 1.29108. Glucose C6H12O6 1.161
Table A3. Standard entropies at 298.15K of organic compounds rele-vant to MSW.
Name Formula S0 (kJ/kg K)
Food1. Allantoin C4H6N4O3 1.2332. Alloxan C4H2N2O4 1.3143. Arginine C6H14N4O2 1.4394. Asparagine C4H8N2O3 1.3225. Aspartic acid C4H7NO4 1.2796. Citric acid C6H8O7 1.3127. Creatine C4H9N3O2 1.4458. Cystine C6H12N2O4S2 1.3479. D-Glutamic acid C5H9NO4 1.23010. L-Lactic acid C3H6O3 1.57911. L-Phenylalanine C9H11NO2 1.29312. L-Proline C5H9NO2 1.42513. Maleic acid C4H4O4 1.37314. Malic acid C4H6O5 1.19915. Methionine C5H11NO2S 1.55216. Phenanthrene C14H14 1.20717. Trytophan C11H12N2O2 1.22918. Tyrosine C9H11NO3 1.18119. Uric acid C5H4N4O3 1.03020. Valine C5H11NO2 1.52721. Xanthine C5H4N4O2 1.05922. Stearic acid C18H36O2 1.53123. Taurine C2H7NO3S 1.23124. Urea CH4N2O 1.74225. Hexadecanoic acid C16H32O2 1.76426. Adenine C5H5N5 1.11827. Creatinine C4H7ON3 1.48328. L-Serine C3H7O3N 1.41929. L-Glutamine C5H10O3N2 1.33530. DL-Alanyl glycine C5H10O3N2 1.46031. Glycylglycine C4H8N2O3 1.43832. Alanine C3H7NO2 1.45033. Cysteine C3H7NO2S 1.40234. Dimethyl sulfone C2H6O2S 1.50935. D-Lactic acid C3H6O3 1.59336. Fumaric acid C4H4O4 1.44737. Guanine C5H5N5O 1.06138. Gycine C2H5NO2 1.37939. Isoleucine C6H13NO2 1.58640. Leucine C6H13NO2 1.58641. L-Glutamic acid C5H9NO4 1.27942. 1- Hexadecanol C16H34O 1.86443. Hypoxanthine C5H4ON4 1.07044. Glycolide C4H4O4 1.354Plastic45. 1,3,5- Trioxane C3H6O3 1.47646. Benzophenone C13H10O 1.34647. Biphenyl C12H10 1.35848. Hexachloroethane C2Cl6 1.00249. Diphenyl carbonate C13H10O3 1.30050. Diphenyl ether C12H10O 1.37251. Diphenylcarbinol C13H12O 1.33052. Polypropylene, isotatic (C3H6)n 1.66253. Polypropylene, syndiotic (C3H6)n 1.79854. Pyromellitic dianhydride C10H2O6 1.087
Table A3. Continued,
231© 2016 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.
Estimating the Specific Chemical Exergy of MSWF. C. Eboh et al.
Table A3. Continued,
Name Formula S0 (kJ/kg K)
109. Xylose C5H10O5 0.956110. 3- Hydroxybenzoic acid C7H6O3 1.281111. 4- Hydroxybenzoic acid C7H6O3 1.272112. Benzoic acid C7H6O2 1.372113. Catechol C6H6O2 1.364114. Lactose C12H22O11 1.128115. o- Hydroxybenzoic acid C7H6O3 1.29116. o- Hydroxybenzoic acid C7H6O3 1.281117. o- Hydroxybenzoic acid C7H6O3 1.272
Source: [3, 8, 10, 12, 17, 24].
Pape
r III
Contents lists available at ScienceDirect
Case Studies in Thermal Engineering
journal homepage: www.elsevier.com/locate/csite
Evaluating improvements in a waste-to-energy combined heat andpower plantFrancis Chinweuba Eboh∗, Peter Ahlström, Tobias RichardsSwedish Centre for Resource Recovery, University of Borås, 501 90, Borås, Sweden
A R T I C L E I N F O
Keywords:Theoretical processExergy efficiencyFlue gas condensationMunicipal solid-waste fired plantImprovement potentialGasification-combustion process
A B S T R A C T
Evaluation of different alternatives for enhancement in a waste combustion process enablesadequate decisions to be made for improving its efficiency. Exergy analysis has been shown be aneffective tool in assessing the overall efficiency of a system. However, the conventional exergymethod does not provide information of the improvements possible in a real process. The purposeof this paper is to evaluate state-of-the art techniques applied in a municipal solid-waste firedheat and power plant. The base case plant is evaluated first; the results are then used to decideupon which technical modifications should be introduced and they are thereafter evaluated. Amodified exergy-based method is used to discover the improvement potential of both the in-dividual components and the overall base case plant. The results indicate that 64% of exergydestruction in the overall process can theoretically be improved. The various modifications se-lected involve changing the bed material, using a gasifier followed by a gas boiler and in-corporating a more durable material into the boiler walls. In addition, changing the heatingmedium of the incoming air (from steam to flue gas) along with a reduction in the stack tem-perature and the integration of flue gas condensation were considered for utilizing the exergy inthe flue gases. The modification involving gasifier, gas boiler and flue gas condensation proved tobe the best option, with the highest exergy efficiency increment of 21%.
1. Introduction
Energy resources are essential for the social and economic development of all nations. A rise in the energy demand is inevitable asthe populations of the world increase with improved lifestyle and industrial development [1]. An adequate management of energyresources and protection of the global environment are vital to achieve sustainable economic development and thereby alleviatepoverty, improve human conditions and preserve biological systems. Enhancing the efficient use of energy resources promotessustainable development because it reduces the environmental and economic costs of expanding energy services [2]. Furthermore,the improvement in the energy efficiency of a process is important for the advancement of energy production. It is the most cost-effective method of abating CO2 emissions, which is the main greenhouse gas that contributes to global warming [3]. Moreover, itreduces the cost of producing heat and power, thus decreasing the cost of energy to consumers, improves the quality of the en-vironment and the standard of living, upholds a stronger economy and secures the source of energy [4].
Waste-to-energy technologies have helped reduce the amount of waste being dumped in landfill sites and in converting non-recyclable waste materials into useful energy resources in the form of heat and electricity [5]. However, the efficiency of energyconversion in solid-waste plants is low when compared with other solid fuels, such as coal and biomass [6] due to the low steam
https://doi.org/10.1016/j.csite.2019.100476Received 6 May 2019; Received in revised form 29 May 2019; Accepted 30 May 2019
∗ Corresponding author.E-mail addresses: [email protected] (F.C. Eboh), [email protected] (P. Ahlström), [email protected] (T. Richards).
properties used in order to prevent high corrosion rates [7]. Exergy analysis has been shown to be an effective tool in furthering thegoal of attaining a more efficient use of energy resources [8]. Its aim is to identify the locations and magnitudes of thermodynamicirreversibilities in a process. Exergy analysis explicitly takes the effects of the surroundings into account: it provides a more realisticpicture of improvement potentials compared to a pure energy analysis. On the other hand, the definition of the state of the sur-roundings is not always unambiguous, leaving some uncertainties in the analysis. The term “exergy” was proposed by Zoran Rant,who used it to develop a model for the chemical exergy of a fuel material that was structurally complicated [9]. Szargut and Styrylska[10] improved Rant's model by considering the chemical composition of the fuels and obtained a correlation between the ratio of thechemical exergy and the lower heating value. Bejan et al. [11] investigated the application of the exergy method in thermal process ina cogeneration system including gas turbine and heat-recovery steam generator. They found that combustion chamber was thecomponent with highest thermodynamic inefficiency and it can be reduced by preheating the combustion air and reducing the air-fuel ratio. Reguagadda et al. [12] performed an exergy analysis of a coal-fired power plant; their investigations showed that thegreatest exergy destruction occurred in the boiler due to heat transfer to the working fluid, flue gas losses and the combustionreaction. Taniguchi et al. [13], who used an exergy method to evaluate the temperature level of air combustion in a coal combustionprocess, confirmed that using air that was warmer than the ambient temperature enhanced the exergy efficiency of the system.Srinivas et al. [14] analysed the steam power cycle with feedwater heaters from an exergy perspective. They found that the tem-perature difference between the working fluid and the flue gas could be decreased by the installation of feedwater heaters, whichhelps to reduce the entropy generated in the boiler. Kamate and Gangavati [15] applied an exergy method to a co-generation plantbased on bagasse to compare the performance of two types of steam turbine. They found that the efficiency of the plant was higherwhen a non-condensing (back pressure) steam turbine was used rather than an extraction (condensing) steam turbine, due to the non-rejection of heat in the condensation process in the former. The latter is, however, preferred as it produces more electricity. Sol-heimslid et al. [5] performed an exergy analysis of a municipal solid-waste combined heat and power plant located in Bergen,Norway. They compared different methods to calculate the chemical exergy of the waste; their investigations showed the methods tobe in good agreement. The exergy efficiency of the plant was calculated to be 17.3%. Grosso et al. [16] found that exergy analysis wasa more reliable measure of performance criteria in waste incineration plants in Europe than the energy recovery efficiency analysisproposed in the Waste Frame Directive (Directive 2008/98/EC of the European Parliament and Council on waste and repealingcertain directives).
To the best knowledge of the authors, no research work has been done on the process improvement evaluations and comparisonsof state-of-the art techniques applicable in waste-to-energy facilities using an exergy method. Therefore, the aim of this paper is toevaluate improvements that can be made in a municipal heat and power plant fired by solid waste, considering the most recentdevelopment in this technology.
2. Evaluation of efficiency improvement method
2.1. Exergy analysis
Exergy analysis is used for performance evaluation, identifying exergy destructions in the process based on the exergy input andoutput of the system. For the steady-state process, assuming that the changes in kinetic and potential energy in this particular systemcan be neglected, the exergy destruction rate for the overall systems can be determined from the exergy rate balance and given inEquation (1) thus:
= +Ex Ex Ex 1 TT
Q WDi
io
oj
0
jj
(1)
where Exi and Exo are the input and output, respectively, of the system's exergy rate,Qj is the heat transfer rate at Position j throughthe boundary at temperature Tj and W is the net work transfer rate across the boundary of the system.
In the case of two separate fluid streams interacting (such as in boiler heat exchangers, condensers, air preheaters and feedwaterheaters), the exergy destruction rates are obtained from Equation (2), assuming no heat loss to the surroundings, as follows:
= + +Ex (m ex m ex ) (m ex m ex )Di
h,i h,i c,i c,io
h,o h,o c,o c,o(2)
Where mh and mc are the mass flow rate of the hot and cold stream, respectively.The process exergy efficiency, ex, is expressed as Equation (3):
= + =Ex WEx
(Exergy available)(Exergy input)ex
Q net
i i
a
i
h
(3)
where ExQh is the exergy flow rate associated with the production of district heating. The exergy input rate is given as shown inEquation (4):
= m exEx .f chi (4)
Where exch is the specific exergy of the fuel. For a solid-waste fuel, such as municipal solid waste, the specific chemical exergy can becalculated by Equation (5): this is taken from the model developed by Eboh et al. [17] and is based on the elemental composition of
F.C. Eboh, et al.
waste fuel.
= + + +ex C H O N S Cl376.461 791.018 57.819 45.473 1536.242 100.981ch (5)
Although a conventional method of exergy analysis to a particular process identifies exergy destructions in a system it does not,however, consider either the constraints in the conversion method or the impact of each individual component.
Further development of exergy-based analysis introduced the concept of the “exergy improvement potential” of industrial pro-cesses, which was proposed by van Gool [18] and is expressed as in Equation (6).
=IP (1 )Exex rpk , D ,rpk (6)
It has been applied for evaluations in the energy system in UK [19], in a coal thermal plant [20] and for solar energy [21]. In thismethod, the improvement potential relates the inefficiency experienced in a system with its exergy efficiency. Nevertheless, theimprovement is limited to the current performance of the specific real process system without taking any future development in thesystem into consideration. The method does not compare the specified process with its theoretical process for available advancementand relative progression in the system.
Recent improvement made to exergy analysis has led to the development of advanced exergy analysis [22], which involvesdividing the destruction of exergy into two parts: avoidable and unavoidable. Avoidable exergy destruction is defined as the irre-versibility that can be prevented through performance enhancement, whilst unavoidable exergy destruction occurs as a result ofphysical, technology and economic constraints. Here, the improvement potential lies in the former, i.e. avoidable, part of exergydestruction. The avoidable exergy destruction rate for a component, k, is obtained from Equation (7).
=E E ED,kAV
D,k D,kUN
(7)
This method has been applied to a gas turbine co-generating system [22], in a combined cycle power plant [23], fluidized bedboiler [24] and geothermal power plant [25]. The unavoidable exergy destruction rate is determined by selecting the most importantthermodynamic parameters of the studied component to give its maximum achievable efficiency [22]. Though this method comparesthe real process with an advanced process, their efficiency improvement is limited to technological constraints. Moreover, efficiencylimited by technology is not predictable and may change over time for a given process [26] as a result of subjective decisions [22].Hence, a modified exergy-base improvement evaluation method is introduced.
In this study, the improvement potential of a process is determined by comparing the exergy destructions of the real process withthe equivalent theoretical process. The theoretical process is defined as the conditions when the thermodynamic limits and maximumperformance of the real process have been reached. The maximum performance is achieved by optimizing the entire system and usingthe parameters of each component of the process plant that give its maximum efficiency. No considerations are taken regarding costand material properties in the theoretical process. Although it is not anticipated that technological enhancements will reach theirtheoretical limits, the latter do, however, provide information of the progress that is possible and the improvements that are neededin the former. The conventional exergy method is used to investigate the exergy destructions in the components and the entiresystem, while the improvement potential introduced in this study is applied to assess the possible future enhancement of the com-ponents when compared with their theoretical processes.
The process exergy efficiency, given in Equation (3), is normally used to compare the useful output with the required input of aparticular system, even though it does not provide a benchmark for process improvement. The improvement potential (IP) istherefore introduced here: it compares the exergy destructions of the real process with that of the theoretical process, therebyproviding a more realistic description of the changes that are possible with respect to the constraints of the conversion pathwayselected. Furthermore, the theoretical limit of maximum efficiency is not subjected to change over time for a given process, unlike thelimits of technology efficiency evaluated by previous researchers.
The improvement potential, IPk, shows the improvement that is possible to attain in a component of the system: it links the exergyefficiency to the total exergy destruction. The higher the improvement potential value of a component, the greater the level ofimprovement required. The improvement potential associated with a particular kth component of a process can be calculated usingEquation (8). It is a modified exergy-based method developed by Tsatsaronis and Park [22]. In this study, the theoretical process isintroduced.
=IP Ex(1 )
Exex tp
ex tpk D ,rp
,
,P ,rpk k
(8)
where ExD ,rpk and ExP ,rpk are respectively, the exergy destruction and product exergy of a particular component in the real process.1 ex tp
ex tp
,
,is the exergy destruction per unit of product exergy under theoretical conditions.
The improvement potential relative, IPr,k , compares the improvement potential achieved in the component with the total exergydestruction. Expressing the improvement potential relative to total exergy destruction gives Equation (9) as:
=IP IPExk
r,kk
D ,rpk (9)
F.C. Eboh, et al.
3. Process plant
3.1. The case study (real process)
Two variations of a process plant are used in this work: the case (i.e. the real process) and the theoretical study. The case studyprocess is based on the design parameters of a heat and power plant fired by solid waste that is currently under construction. Theplant has a fuel energy input of 100 MWth. The waste fuel used in the process has a lower heating value of 11.6MJ/kg as received anda moisture content of 33.1 wt-% [27]; a chemical analysis of the fuel on a dry basis (db) is presented as (C: 46.2); (H: 6.1); (O: 28.03);(N: 1.1); (S: 0.2); (Cl: 0.47) and (Ash: 17.9) [28].
A flow diagram of the process in the plant used in the current study can be seen in Fig. 1. There are two air heaters (using steam), aboiler with a combustion section and a heat exchanger section, a turbine, a condenser, a condensate pump, a feed-water pump, adeaerator and a feed-water heater. A flue gas recirculation process is employed in the plant to reduce the temperature of thecombustion chamber, with the gas being discharged later through the chimney stack. The isentropic efficiency of both the turbine andthe pump were selected from the typical range of 70–90% and 75–85%, respectively, for the real process plant [29].
3.2. The theoretical process
The theoretical process, in contrast to the real case, is not limited by technological conditions such as physical and economicalconstraints. It gives the highest efficiency of the process; even though its efficiency cannot be achieved in practice it does, however,provide a benchmark or target for the design of the process [26]. Here, the greatest improvement in the plant is achieved byoptimizing the entire system, using the parameters of the component that give the greatest efficiency. In the boiler combustor, thetemperature is taken as being the adiabatic flame temperature of the waste fuel, i.e. 1677 °C, operating under stoichiometric airconditions. In the boiler heat exchanger and other heat exchangers of the plant, a pressure drop of zero and a minimum temperaturedifference of 0.1 °C is assumed. An isentropic efficiency of 100% is assumed for the pumps and the steam turbine. The assumptions inthe theoretical process are based on the minimum exergy destruction of the component [30]. Optimization of the system is performedusing an Aspen Plus software simulator and by considering each of two variables: extraction pressures and steam pressure. Here, onevariable is adjusted while the other is kept constant until the maximum value is achieved. This procedure is repeated for each variableand iterated until the overall maximum efficiency of the plant is reached. The flue gas recirculation process was not considered,however, as it reduces the maximum temperature in the combustion zone and thus increases the destruction of exergy.
Modelling and simulation of the case study and theoretical processes of the heat and power plant fired by solid waste wereperformed with Aspen Plus (Advanced System for Process Engineering Plus). The Peng-Robinson property model (PR-BM) was chosenfor the estimation of the flue gas because it contains conventional components, namely N2, O2, H2O and CO2, at atmospheric pressureand high temperature regions; the IAPWS-95 property method was used to model the properties of water and steam [31].
4. Results and discussion
4.1. Improvement potential and evaluations
A method for improving the potential of a system has been developed so that the enhancement of the process can be evaluatedefficiently. It has been applied to the energy conversion process of a municipal heat and power plant fired by solid waste whilst underconstruction. Here, the exergy destructions of the case study process plant is compared with the theoretical process.
Fig. 1. Process flow diagram of the municipal heat and power plant fired by solid waste, modelled in Aspen Plus.
F.C. Eboh, et al.
Table 1 shows the results of the evaluations made of improvements in performance conducted on the components of the casestudy process plant. The theoretical efficiency was achieved at the optimal values of 37, 2.3 and 1.32 bar for the first, second andthird extraction pressures, respectively. The hypothetical component was introduced in order to convert the flue gases emitted fromthe stack into the environmental condition. The overall exergy efficiencies of the case study and theoretical processes are 25% and56%, respectively.
The exergy efficiencies of the processes determined from Equation (3) and presented in Table 1 were found by using the con-ventional method of comparing the available exergy with the input exergy. The method used for the system analysis of the case studyprocess shows that significant improvement should focus primarily on the boiler, followed by the steam turbine (as shown in Table 1)because of the high destruction of exergy in these components. The boiler has been identified as the component with the largestexergy destruction, which is due to irreversible combustion reactions: this is in agreement with previous exergy efficiency evaluationsof a thermal power plant [8]. Although the conventional exergy method identifies the components and processes with the highestexergy destruction it does not, however, account for the relative efficiency that determines the maximum possible improvement of aparticular component in the system.
The exergy efficiency of the case study process was therefore compared with the theoretical process in Equations (8) and (9) forefficient performance evaluation of the energy conversion processes. Table 1 shows that the boiler is the component with the highestimprovement potential. The method for improving potential that is developed in this work substantiates the fact that this componentshould be targeted in the quest to improve the overall performance of the system, as examined in the conventional exergy method. Inaddition, the present study investigated improvement that may be possible by determining the maximum efficiency of the compo-nents. For example, in the case study process plant investigated, the efficiency improvement of the boiler will never exceed 62% dueto constraints in the combustion of fuel. It indicates that even though this component has the largest exergy destruction of 66MW,64% of improvement can theoretically be achieved. In the overall process plant, on the other hand, 53% of the total exergy de-struction can be improved in the boiler.
The improvement potential relative to the total exergy destruction in the case study process plant using the method developed inthis study, along with the van Gool [18] and Tsatsaronis and Park [22] methods (avoidable exergy destruction) all identified theboiler as the components with the highest improvement potential, with 53%, 53% and 36% respectively (Fig. 2). Furthermore, thethree methods agree that over 80% of total improvement potential should be in the boiler.
Though the improvement potential calculated for the boiler, using this method is similar to van Gool method. However, van Goolmethod does not identify the maximum theoretical conditions that limit the process efficiency. The Tsatsaronis and Park [22] method
Table 1Evaluations of the performance of the case study (CS) and theoretical processes (TP).
CS Process TP Process IPk IPr,k
ex (%) Ex (D kW) Ex (P kW) ex (%) (kW)
Boiler 37 66148 38569 62 42019 5.3·10−1
Steam turbine 77 5558 18716 100 5558 7.0·10−2
Condenser 66 3989 7815 68 227 2.9·10−3
Condensate pump 72 5 12 100 5 5.9·10−5
Feed-water pump 74 103 291 95 89 1.1·10−3
Feed-water heater 94 15 187 96 7 8.8·10−5
De-aerator 87 209 109 93 201 2.5·10−3
Primary steam air heater 48 851 774 54 204 2.6·10−3
Secondary steam air heater 74 109 315 75 3 3.2·10−5
Hypothetical component 0 2075 2075 100 2075 2.6·10−2
Overall plant 25 79062 68864 56 42382 6.1·10−1
Fig. 2. Comparison of the improvement potential relative to total exergy destruction, IPr,k of the. Method Developed (DM) with the van Gool (vGM)and Tsatsaronis and Park methods (TPM).
F.C. Eboh, et al.
showed a lower improvement potential than the current method as a result of the technological constraints their method employs.Technological limitations are subjected to change over time for a given process, whereas the method developed here is based ontheoretical limits that are fixed for a given process [26].
4.2. Different efficiency improvement methods
The boiler was identified as having highest improvement potential in the base case plant, as shown in Table 1. Different mod-ification methods were therefore applied to this component for efficiency enhancement, i.e. changing the bed material, convertingthe waste boiler into a gas boiler and using Inconel, which is a corrosion-resistant material in the boiler walls. In addition, flue gascondensation and changing the air heating medium (from steam to flue gas) to reduce the stack temperature, were also considered inthe quest to better utilize the exergy in the stack. The efficiency enhancement evaluation of the WTE plant producing only electricitywas also examined and compared with that of the combined heat and power plant. The design parameters of the base plant and thevariables in the different modifications made are shown in Table 2; in both cases, waste was used as the fuel with the same energyinput of 100MW.
Modification 1 (M1) involves reducing the excess air from 39% to 11% and assumes that the bed material in the combustionchamber is changed to ilmenite (FeTiO3), an oxygen-carrying metal oxide. The bed material has the ability to absorb, release anddistribute oxygen uniformly in the boiler furnace. Less air is therefore required to reduce the amounts of carbon monoxide andunreacted hydrocarbons. The use of this material has been investigated and applied to a CFB boiler/gasifier reactor at ChalmersUniversity of Technology, Gothenburg, by Thunman et al. [32].
Modification 2 (M2) incorporates the integration of flue gas condensation. Here, the temperature in the stack is cooled from160 °C to 110 °C and a portion of the energy in the flue gas outside the system boundary is recovered in the heat exchange for use asdistrict heating. Flue gas at 160 °C is first condensed below the dew point temperature of about 50 °C for the separation of watervapour. It is then reheated to 110 °C before being discharged via the stack. A flow diagram of this modification is shown in Fig. 3.
In Modification 3 (M3), the temperature and pressure of the steam in the case study process are increased from 420° to 440 °C and50 bar–130 bar, respectively (Fig. 4). In addition, an intermediate reheater is integrated into the system. It reheats the wet steam afterthe first turbine extraction (14 bar) from 180 °C to 320 °C. The high steam parameters are those used in the waste-to-energy plant ofAfval Energie Bedrijf, Amsterdam [33]. Here, the furnace membrane walls are protected by Inconel, a corrosion-resistant material foruse in high temperature applications.
Modification 4 is a combination of Modifications 1, 2 and 3.Modification 5 (M5), as shown in Fig. 5, integrates waste gasification with a gas boiler and is used in the waste gasification plant
in Lahti, Finland [34]. The waste is gasified at about 900 °C and then cooled to 400 °C before being subjected to the gas cleaningprocess. The energy from the waste heat is used for evaporating part of the water from the economizer. The product gas is combustedin a gas boiler operating with a steam temperature and pressure of 540 °C and 121 bar, respectively.
Whilst Modification 6 (M6) has the same structure and operating variables as Modification 5, it also incorporates a flue gascondensation (FGC) process.
In Modification 7 (M7), the two air heaters in the base plant were removed and replaced by a high-pressure feedwater heater anda new air heater. Here, the temperature of the flue gas in the stack was deceased from 160 °C to 130 °C. The air heater, which wasintegrated into the system after the economizer, was heated by flue gas (Fig. 6). It should be noted that both the base plant andModifications 1–6 use steam to preheat the air entering the combustion chamber.
Table 2The parameters of the base case plant and the different improvement modifications made.
Variables Unit BP M1 M2 M3 M4 M5 M6 M7
Baseplant
Excess airreduction
Flue gasCondensation(FGC)
High steamparameter+ reheater
M1+M2+M3 Wastegasification+ gas boiler
M5+FGC Changing themedium for pre-heating air
Energy input MW 100 100 100 100 100 100 100 100Extraction press.
HPTbar 10 10 14 14 10 10 10 10
Extraction press. IPT bar 5 5 5 5 5 5 5 5Extraction press. LPT bar 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5Flue gas
recirculation% 20 20 20 20 20 20 20 20
Excess air % 39 39 39 39 5 5 39 39Stack temperature o C 160 160 160 160 160 160 130 130Steam temperature o C 420 420 440 440 540 540 420 420Steam pressure bar 50 50 130 130 121 121 50 50Reheat steam temp. o C – – 320 320 – – – –Reheat steam press. bar – – 14 14 – – – –
F.C. Eboh, et al.
Fig. 3. Flow diagram of the case study process with a flue gas condenser, modelled in Aspen Plus.
Fig. 4. Flow diagram of the case study process with a steam reheater, modelled in Aspen Plus.
Fig. 5. Flow diagram of the waste gasification process with a gas boiler, modelled in Aspen Plus.
F.C. Eboh, et al.
4.3. Comparison of the methods used to improve efficiency with the case process plant
Table 3 presents the generation of electricity and the production of district heating, together with the energy and exergy effi-ciencies, for the case study process plant and its modifications. The results show that reducing excess air (i.e. Modification 1)increases the exergy and the energy efficiencies by 0.9% and 1.6%, respectively, for the overall plant and 0.4% and 1.4% in the boiler,respectively, when compared with the case study process. This is due to a decrease in the loss of flue gas (the amount of gas isreduced) and that less steam is extracted in the turbine to preheat the incoming air. As a result, more heat is transferred to the water/steam in the boiler heat exchanger sections, which increases the production of both electricity and district heat.
The introduction of flue gas condensation in Modification 2 decreases the exergy loss from the flue gas to the surroundings from2.1MW to 1.5MW. Here, 30% of the exergy content in the flue gas was utilized and used for district heat. This includes both theactual heat of condensation but also the net decrease in the temperature of the flue gas stack, which was cooled from 160 °C to 50 °Cand reheated after condensation to 110 °C. The greatest amount of district heating is produced here, yielding an increase of 4.3% and11.4% in overall exergy and energy efficiencies, respectively. Although this modification did not have any effect on the production ofelectricity, the electricity demand of the plant may, however, increase due to the large pressure drop witnessed during the con-densation process.
Modifications 3–6 have the highest electrical generation and exergy efficiencies because of the high steam temperatures andpressures used in their respective processes. Modifications 3 and 5 showed the lowest production of district heat and Modifications 2,4 and 6 showed the highest, which was due to the integrated flue gas condensation process. In addition, the greatest reduction ofexergy loss in the flue gas, of about 38%, was noted in Modification 4: this was a result of combining flue gas condensation andreducing the amount of excess air. Modification 7 enhances the production of district heating without integrating condensation of theflue gas. It also helps to reduce the loss of flue gas in the stack by decreasing the temperature from 160 °C to 130 °C: the temperaturemust be sufficiently high to avoid low-temperature corrosion.
Furthermore, although Table 3 shows that Modification 3 does not change the energy efficiency of the boiler and the overallprocess, exergy efficiency increments of 8% and 9% were nevertheless observed in the respective processes. This confirms that theenergy method is not a reliable tool for evaluating a system.
Fig. 6. Flow diagram of the case study process with a flue gas air heater and a high-pressure feed-water heater, modelled in Aspen Plus.
Table 3Evaluation of the improvement in efficiency in the waste-to-energy combined heat and power plant.
Parameter Unit BP M1 M2 M3 M4 M5 M6 M7
Base plant Excess airreduction
Flue gasconden-sation
High steamparameterplus reheater
M1+M2+M3 Wastegasification plusgasboiler
M5+flue gasconden-sation
Changing themedium for pre-heating air
Electricityproduction
MW 18.72 18.81 18.72 21.73 21.73 24.16 24.16 18.52
District heatingproduction
MW 58.97 60.07 67.49 55.90 66.40 58.38 61.53 60.84
Boiler exergy eff. % 36.8 37.0 36.8 39.8 41.1 50.5 50.5 37.3Boiler energy eff. % 81.8 83.0 81.8 81.8 82.2 85.0 85.0 83.0Exergy efficiency % 25.2 25.5 26.3 27.4 28.8 30.1 30.5 25.3Energy efficiency % 77.0 78.5 86.1 77.0 87.4 81.8 84.9 79.0Exergy loss MW 2.1 2.0 1.5 2.1 1.3 1.6 1.4 1.6
F.C. Eboh, et al.
The improvement in efficiency for the electricity production only is shown in Table 4. Here, the flue gas condensation process isnot considered as this does not increase the production of power. In order to accomplish this process, the condensing pressure afterthe steam turbine was reduced from 1 bar to 0.08 bar (representing a temperature in the condenser of slightly above 40 °C). Com-parison of the base case plant and the different improvement modifications (Table 4) shows that Modifications 3 and 5, with thehighest steam temperatures and pressures, have not only the highest production of electricity but also the greatest energy and exergyefficiencies.
5. Conclusions
Different methods of improving the efficiency of a heat and power plant fired by solid waste have been investigated and eval-uated. They are based on the component with the highest improvement potential, which compares the exergy destructions of theplant with its theoretical process in order to identify the parts in which improvements may be made, as well as their significance. Theanalysis made in this study identifies the maximum limits for improving the efficiency of the system. It was found that 64% of thetotal exergy destruction in the case study process can be improved. The boiler was identified as being the component with the greatestpotential for making improvements to the plant, with a theoretical efficiency of 62%. Constraints in the combustion process, how-ever, mean that 53% of the improvement possible in the overall process plant can be achieved theoretically in this component. Basedon the component with the highest potential for improvement, the different methods that were investigated showed Modifications 2,4 and 6, involving flue gas condensation to be the best options for enhancing the efficiency of the district heating process in acombined heat and power plant. Modification 7, which involves changing of air heating medium from steam to flue gas is the bestmethod for the production of heat without flue gas condensation. Modifications 3 and 5 with reheating process and waste gasificationwere found to be the best for the production of electricity only, with exergy efficiency of 26% and 28%, respectively.
Conflicts of interest
The authors declare no conflict of interest.
Acknowledgements
The financial support from the Government of Nigeria through the Tertiary Education Trust Fund is highly appreciated.
Nomenclature
Ex exergy rate (kW)m mass flow rate (kg/s)Q heat transfer rate (kW)W work transfer rate (kW)Subscripts
a availablec coldD destructionf flowh hoti inputk component of a processo outputrp real processtp theoretical process
Table 4Evaluation of the improvement in efficiency for the production of electricity only.
Parameter Unit BP M1 M3 M5 M7
Baseplant
Excess airreduction
High steam parameter plusreheater
Waste gasification plus gasboiler
Changing the medium forpre-heating air
Electricity production MW 24.7 25.0 27.5 30.1 24.7Exergy efficiency % 23.5 23.7 25.8 28.4 23.5Exergy efficiency increment % – 0.8 10 21 –Energy efficiency % 24.3 24.5 26.7 29.4 24.3Energy efficiency increment – 0.8 10 21 –
F.C. Eboh, et al.
Superscripts
AV availablech chemicalUN unavailableAbbreviations
BC boiler combustorBP base plantC carbonCl chlorineCOND condenserCP condensate pumpCS case studyDRT deaeratorECO economizerEVA evaporatorFG flue gasFGAH flue gas air heaterFGC flue gas condenserFWH feed-water heaterFWP feed-water pumpH hydrogenHPT high-pressure turbineHTR heaterIP improvement potentialIPT intermediate-pressure turbineLTP low-pressure turbineM modificationMIX mixerMSW municipal solid wasteN nitrogenO oxygenP pressurePSAH primary steam air heaterRH reheaterS sulphurSEP separatorSH superheaterSPLIT splitterSSAH secondary steam air heaterSTDRUM steam drumSW steam and waterTAE thermodynamics achievement efficiencyTP theoretical processT temperatureW waterGreek letters
efficiencychange
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r IV
Economic evaluation of improvements in a waste-to-energy combinedheat and power plant
Francis Chinweuba Eboh ⇑, Bengt-Åke Andersson, Tobias RichardsSwedish Centre for Resource Recovery, University of Borås, 501 90 Borås, Sweden
a r t i c l e i n f o
Article history:Received 26 April 2019Revised 26 August 2019Accepted 7 September 2019
Keywords:Waste-to-energy plantEfficiency improvementEconomic viabilityCost of improvement
a b s t r a c t
Improving the efficiency of waste-to-energy combined heat and power plants increases their productionof both electricity and heat. Economic evaluation of such improvements enables adequate decisions to bemade between the various alternatives with respect to economic viability of the plant. In this study, thecost and profitability of different modifications to improve efficiency in a waste-to-energy plant are con-sidered: these include the re-arrangement of air heaters, the introduction of a reheater, flue gas conden-sation (FGC) and an integrated gasification-combustion process. The base case and the modifications areevaluated and compared when operating either as a combined heat and power plant or as a power plant.Modelling, simulation and cost estimations were performed with the Aspen Plus software. Although theintegrated gasification-combustion technology with FGC has the highest exergy efficiency, its higher cap-ital cost is greater than all of the other alternatives. Modification 6, which involves both re-arrangementand changing the air heating medium has the lowest capital cost with respect to enhancing exergy effi-ciency. Modifications 1 and 7, involving FGC, are the best alternatives for the capital cost per total unit ofrevenue generated. These modifications not only provides the highest heat production but also the high-est net present value (NPV). The base case and the modifications investigated all have positive NPV, indi-cating that a waste-to-energy combined heat and power plant is an attractive investment. However, anincrease of about 122% in the gate fees would be required for a system with only electricity production tobe profitable.
� 2019 Elsevier Ltd. All rights reserved.
1. Introduction
The current increase being experienced in the generation ofwaste endangers human health and the environment. The abilityto manage large quantities of waste is one of the greatest chal-lenges facing the present and future generations (World EnergyCouncil, 2016). One possible solution is to minimise waste by reus-ing or recycling large fractions of waste materials (European Union,2008). A suitable approach for treating undesired end productsremaining after recycling is the energy recovery method(Solheimslid et al., 2015; World Energy Council, 2016).
Utilization of energy from waste helps in treating non-reusableand non-recyclable waste as well as converting the valuable energyresource into electricity and heat (World Energy Council, 2016).The technology used for recovering energy from waste employsnot only combustion but also gasification, pyrolysis and anaerobicdigestion. Of these, the combustion process is used the most
widely for treating waste materials of different types and sizes(Astrup et al., 2015; Burnley et al., 2011).
Waste combustion technology is well established in many Euro-pean countries (Grosso et al., 2010). Sweden, for instance, hasabout 34 waste-to-energy combustion plants and recovers moreenergy from waste per capita than any other country in Europe(Avfall Sverige, 2018). The capacity of the waste combustion plantsin Sweden is, in fact, greater than the amount of combustible wasteproduced in the country: in 2017, a total of 6,150,150 tonnes ofindustrial and household waste were treated and converted intomore than 18.3 TWh of energy, of which 2.2 TWh was for electric-ity and 16.1 TWh for heating (Avfall Sverige, 2018).
Recovering energy via waste combustion technology hasreduced the volume and mass of solid waste by 90% and 70%,respectively (Cheng and Hu, 2010; Menikpura et al., 2016). How-ever, its electrical efficiency is generally low when compared withother combustion plants as a result of low steam properties: this,on the other hand, prevents surface corrosion on the heat exchan-ger tubes in the boiler (Ionescu et al., 2013; Malkow, 2004) causedmainly by the concentration of alkaline chlorides in the flue gases(Lee et al., 2007). The steam temperature and pressure of a
https://doi.org/10.1016/j.wasman.2019.09.0080956-053X/� 2019 Elsevier Ltd. All rights reserved.
⇑ Corresponding author.E-mail addresses: [email protected] (F.C. Eboh), bengt-ake.
[email protected] (B. Andersson), [email protected] (T. Richards).
Waste Management 100 (2019) 75–83
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waste-to-energy plant are therefore often limited to 400 �C and40 bar, respectively (Lombardi et al., 2015). Furthermore, waste-to-energy technology is capital intensive (Leme et al., 2014) dueto high financial investments and high maintenance and operatingcosts (Menikpura et al., 2016). The investment cost is about threetimes higher than for a woodchip CHP and four times higher thanfor a pulverized coal power plant (Taherzadeh and Richards, 2016).
Enhancing energy efficiency may help in reducing costs,increasing energy conversion and minimising the environmentalimpact of combustion. The exergy method has been shown to bean efficient tool for evaluating the efficiency of recovering energyfrom the combustion of waste. Grosso et al. (2010) analysed theuse of the energy efficiency factor, R1, within the waste framedirective along with the exergy efficiency as performance criteriaof waste-to-energy plants in Europe. Their results showed thatthe exergy method is a reliable way of assessing efficiency, as theR1 formula does not account for changes in the size of the plantor climate conditions. Solheimslid et al. (2015) evaluated the effi-ciency of a combined heat and power plant fired by municipal solidwaste in Bergen, Norway, using different methods to calculate thechemical exergy of the solid waste; the results obtained from thedifferent methods used in their investigations are in goodagreement.
Possible measures for improving the design of waste combus-tion plants have been examined by several researchers. Lee et al.(2007), for example, examined the use of different corrosion-resistant alloys as cladding for the boiler tubes that could with-stand high steam temperatures, thus enabling an increment inthe properties of the steam in the superheater. However, thecorrosion-resistance materials need to be evaluated and balancedwith respect to cost-effectiveness. A net electricity efficiency ofabove 30% was achieved with energy from a waste plant in Amster-dam (the Netherlands) when the boiler operated at a steam tem-perature of 440 �C and pressure of 130 bar (Gohlke, 2009; Mureret al., 2011). Here, the plant was incorporated with a steam rehea-ter, had an excess air ratio of 1.4 and a condensate pressure of0.03 bar. The boiler tubes were protected with Inconel; more heatwas recovered from the boiler’s heat exchangers by cooling the fluegas exit temperature from 180 �C to 130 �C. Main and Maghon(2010) examined different improvement measures that wereapplicable for enhancing the efficiency of modern energy fromwaste (EFW) facilities located at Hameln/Germany, Arhus/Den-mark, Heringen/Germany, Naples/Italy and Ruedersdorf (Berlin)Germany. The improvement methods evaluated were comparedto the waste combustion technology of the base plant, operatingat steam conditions of 40 bar and 400 �C, a flue gas temperatureof 190 �C and an excess air level of 60%. They observed that reduc-ing the excess air to 39% in the EFW at Hameln/Germany; reducingin the flue gas temperature at the boiler outlet from 180 �C to100 �C using heat exchangers in the EFW at Arhus/Denmark; intro-ducing an external superheater using auxiliary fuels at 520 �C and90 bar in the EFW at Heringen/Germany; increasing the steamparameters to 500 �C and 90 bar in the EFW at Naples/Italy andoperating a boiler with an intermediate reheater in the EFW atRuedersdorf (Berlin)/Germany increased the energy efficiency ofthe base process plant by 1.1%, 6.8%, 12.6%, 14.6% and 13.5%,respectively. However, when compared with the base plant, noincrease was observed in the boiler efficiency for the changes madein the EFW plants at Naples/Italy and Ruedersdorf (Berlin)/Germany, showing that there is no room for significant improve-ment to be made when the energy method is used for efficiencyevaluation. It agrees with the statement that energy efficiency isbound to lead to misconception, misevaluations and poordecision-making (Gaggioli and Wepfer, 1980). It does not accountfor entropy generated within the system, providing only informa-tion of inputs and outputs of energy in the process and excluding
its quality (Luis, 2013). Further improvement in the energy recov-ery from waste can also be realized through preheating the com-bustion air and water, using the low temperature streams in theplant or flue gas at the boiler (Lombardi et al., 2015).
The recirculation of flue gas has been shown to enhance energyrecovery from waste by improving homogeneity and mixing thegases to provide a more efficient combustion (Liuzzo et al., 2007;Murer et al., 2011). While examining the effect of flue gas recircu-lation (FGR) of a municipal solid waste fired plant, Liuzzo et al.(2007) noticed that when FGR was used as the secondary air inthe boiler, it not only reduced the formation of NOx in the fluegas but also increased the energy recovery of the overall systemby 3%.
A further measure of efficiency improvement is the applicationof a combined heat and power process (co-generation) in thewaste-to energy plant. Here, energy from a waste plant is suppliedto a district heating system via a condensing heat exchanger with afeed temperature in the range of 75 �C to 110 �C, while the returntemperature varies between 40 �C and 55 �C (Gohlke, 2009). Theenergy efficiency of waste combustion typically ranges between20 and 30% for electricity production only, whereas about 85%can be reached in the combined heat and power plant (Ryu andShin, 2013). Sweden has a well-developed district heating system(Gohlke and Martin, 2007) that enables the recovery of moreenergy per ton of waste combusted, with more than 82% ofwaste-to-energy plants producing both electricity and heat(Avfall Sverige, 2018).
The effect that pre-treating waste before combustion has onenergy recovery was studied by Consonni et al. (2005a). Theyinvestigated strategies of using municipal solid waste to recoverenergy in a waste-to-energy plant involving the direct combustionof waste without pretreatment, subjecting it to light mechanicaltreatment and converting it into refuse-derived fuel. They foundthat whilst pre-treating the waste increases its heating value mar-ginally it does, however, reduce the net production of electricitydue to the loss of combustible materials. Consonni et al. (2005b)examined further the environmental impact and cost implicationsof the four strategies. Their observations showed that treatingwaste before it is used in a waste-to-energy plant is neither envi-ronmentally nor economically beneficial. Cimpan and Wenzel(2013) compared the energy savings of pre-treating waste materialusing mechanical treatment and mechanical biological treatmentwith direct combustion in a waste-to-energy plant, and found thatdirect combustion without pre-treatment achieved the highestenergy savings.
Although the different improvement methods in the recovery ofenergy from waste as reported by past researchers will enhancethe efficiency of the process, their cost implications and profitabil-ity were not addressed. Moreover, most of the waste-to-energyplants investigated produce only electricity and were evaluatedbased on the energy efficiency method. Therefore, the aim of thisstudy is to investigate different improvement options in a wastecombustions process, as well as their cost and economic viability.The specific objectives are: (i) to evaluate the exergy efficiency ofa waste-to-energy combined heat and power plant, (ii) to investi-gate possible improvements in this sector, (iii) to evaluate an eco-nomic analysis of such improvements and (iv) to compareimprovements that could be made in the combined heat andpower plant with electricity production only.
2. Methodology
The cost of improving efficiency was evaluated by comparingthe ratio of cost increment and the exergy efficiency enhancementof each modification with the base case plant. Seven modifications
76 F.C. Eboh et al. /Waste Management 100 (2019) 75–83
of the base case process plant were considered, and involved there-arrangement of air heaters along with changing the heatingmedium; reheating; flue gas condensation and an integrated gasi-fication and combustion. A sensitivity analysis was performed inorder to examine the effects of uncertainties in the price of theincome generated and the operating costs estimated. In addition,a profitability assessment of each improvement method was madeto ascertain their economic viability. Furthermore, the modifica-tions were evaluated and compared with the case study processplant operating either as a combined heat and power (CHP) plantor as a power plant. The conditions of the base plant and the var-ious modifications are shown in Table 1. In all cases, municipalsolid waste together with industrial waste corresponding to anenergy input of 100 MW was used. The seven modifications andthe base case process were modelled and simulated using AspenPlus V9.
The base case plant (BC) is a municipal heat and power grateboiler fired by solid waste currently under construction. The pro-cess flow diagram of the plant and descriptions of its equipmentas modelled in Aspen Plus are shown in Fig. 1 and Table 2, respec-tively. The stack temperature is 160 �C and 20% flue gas recircula-tion is employed in the boiler. The full system comprises acondensate pump, a feed-water pump, a boiler with a combustionpart that includes the boiler tubes and a heat exchanger part (evap-orator, superheater and economizer), a feed-water heater, a de-aerator and two air heaters (using steam). The solid waste fuelused in the process, obtained from the city of Borås in Sweden, iscomprised of 70% industrial waste and 30% municipal solid waste.The former is composed of wood, paper and plastics; the latter hasan average composition of food waste (24.61%), paper packaging(19.26%), plastic packaging (11.10%), cardboard (1.87%), metalpackaging (2.76%), glass packaging (3.58%), diapers and tissues(20.62%), combustible (12.73%), electronic waste (0.43%), haz-ardous waste (0.46%) and other materials (2.58%) (Moghadamand Karimkhani, 2010). The solid waste has a lower heating valueof 11.6 MJ/kg as received, with a moisture content of 33.1 wt-%(Pettersson et al., 2013); a chemical analysis of the waste fuel inweight percentage, calculated on a dry basis (db) is reported as(C: 46.2); (H: 6.1); (O: 28.03); (N: 1.1); (S: 0.2); (Cl: 0.47) and(Ash: 17.9) (Jones et al., 2013).
Modification 1 (M 1), as shown in Fig. 1, has the same designand operating parameters as in the base case plant, except forthe addition of flue gas condensation (FGC). The flue gas, at a stacktemperature of 160 �C, is cooled down to 50 �C, which is below thedew point temperature. It is reheated thereafter to 110 �C, in orderto avoid condensation and low temperature corrosion.
In Modification 2 (M2), the temperature and pressure of thesteam in the case study process are increased from 420 �C to440 �C and 50 bar to 130 bar, respectively. In addition, an interme-diate reheater is integrated into the system, which reheats the wetsteam after the first turbine extraction (14 bar) from 180 �C to320 �C. The high steam parameters are those used in the waste-to-energy plant of Afval Energie Bedrijf, Amsterdam (Murer et al.,2011). Here, the furnace membrane walls are protected by Inconel,a corrosion-resistant material suitable for use in high temperatureapplications.
Modification 3 (M3), which is similar to Modification 2 (M2),has flue gas condensation integrated to utilise the exergy other-wise lost to the surroundings.
Modification 4 (M4), represented in Fig. 1, involves the integra-tion of waste gasification with the waste boiler, as has been appliedin the waste gasification plant in Lahti, Finland (Taherzadeh andRichards, 2016). Solid waste was first gasified at a temperature of900 �C to produce combustible gases, which were then cooleddown to 400 �C prior to the gas-cleaning process. The cleaned gaswas then combusted in a gas boiler for the production of electricityand heat. The gasifier used air as the gasifying medium, while thecombustion section of the plant operated at a steam temperatureand pressure of 540 �C and 121 bar, respectively.
Modification 5 (M5) has the same process configuration asModification 4, with the addition of flue gas condensation (FGC).
Modification 6 (M6) is similar to the base case plant. The two airheaters have been removed and a high-pressure feed-water heaterand a new air heater added instead. The air heater was integratedinto the system after the economizer and heated by the flue gas.The stack temperature, which was 160 �C in the base case plant,was reduced to 130 �C.
Modification 7 (M7) is similar to Modification 6 but incorpo-rates flue gas condensation in order to utilise the exergy lost tothe surroundings (Fig. 1).
2.1. Evaluation of efficiency
Exergy analysis was used to evaluate the improvement in effi-ciency in the process plant based on the exergy input and outputof the system. The input exergy of the waste stream was calculatedfrom the elemental composition of the waste fuel using the modeldeveloped by (Eboh et al., 2016). The exergy efficiency of the pro-cess was calculated using Eq. (1):
gex ¼_ExQh
þ _WnetPi_Exi
¼P
a Exergy availableð ÞPi Exergy inputð Þ ð1Þ
Table 1The parameters of the base case plant and the different improvement modifications made.
Variables Unit BC M1 M2 M3 M4 M5 M6 M7Basecase plant
Flue gasCondensation(FGC)
High steamparameter+reheater
M2 + FGC Wastegasification+ gas boiler
M4 + FGC Changing themedium forpre- heating air
M6 + FGC
Energy input MW 100 100 100 100 100 100 100 100Extraction press. HPT bar 10 10 14 14 10 10 10 10Extraction press. IPT bar 5 5 5 5 5 5 5 5Extraction press. LPT bar 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5Flue gas recirculation % 20 20 20 20 20 20 20 20Excess air % 39 39 39 39 5 5 39 39Stack temperature �C 160 110 160 110 160 110 130 110Steam temperature �C 420 420 440 440 540 540 420 420Steam pressure Bar 50 50 130 130 121 121 50 50Reheat steam temp. �C – – 320 320 – – – –Reheat steam press. bar – – 14 14 – – – –
Source: BC is from design data of a waste plant under construction in Sweden; M2 and M3 are modified from the operation conditions of Afval Energie Bedrijf, Amsterdam(Murer et al., 2011); M4 and M5 are modified from the operation conditions of a waste gasification plant in Lahti, Finland (Taherzadeh and Richards, 2016).
F.C. Eboh et al. /Waste Management 100 (2019) 75–83 77
where _ExQhis the exergy flow rate associated with the production of
district heat, _Wnet is the net work output rate and _Exi is the exergyrate input to the system.
2.2. Evaluation of costs and finances
The various methods pertaining to the improvement of the effi-ciency of the base case study investigated were compared with the
Fig. 1. Process flow diagram of the base case and modified waste-to-energy CHP plant, modelled in Aspen Plus. The dotted areas show the new equipment in the modificationcompared to the base plant. The equipment symbols are described in Table 3.
78 F.C. Eboh et al. /Waste Management 100 (2019) 75–83
capital cost involved in the improvement so that accurate decisionscould be made. The costs and financial analyses were calculatedusing the Aspen process economic analyser. This is one of the mostsophisticated software used in the industry for estimating costs(Towler et al., 2013a): using a detailed method of cost estimation,it evaluates the cost of process design by examining the costs of thecomponents that constitute the system. It involves estimating thecost of the equipment purchased based on the sum of the cost ofthe material, i.e. labour and overhead costs as well as the profitmade by the manufacturer. The cost of the equipment installedwas estimated from the bulk materials and labour requirements.The information in the software was based on the cost data pro-vided by the vendor in 2015.
The profitability analysis was carried out using the investmentparameters listed in Table 3.
The information provided in the table was used to calculate theoperation and maintenance costs, the net present value (NPV) andthe internal rate of return (IRR), based on the assumption that theeconomic life of the process plant is 20 years. Also, the analysisassumes a salvage value of zero. As industrial plants continue tofunction for many years after the end of their economic life(Towler et al., 2013b), the salvage value determines the estimatedworth of the capital cost of a project at the end of its useful life andis used to calculate the annual depreciation. The straight-linedepreciation method is employed here, as it is the simplest andmost commonly used (Towler et al., 2013b). It is determined bysubtracting the salvage value from the capital cost, and then divid-ing the result by the economic life of the project. The main incomeconsidered here is the revenue generated from electricity, districtheat and gate fees. A utilization factor of 91% was presumed, whichcorresponds to the plant having 8000 operating hours/year. Therevenue prices and most of the assumptions made were based onwaste combustion plants in Sweden (Taherzadeh and Richards,2016).
2.3. The cost of improving efficiency
The cost of improving efficiency compares the increase in capi-tal costs with the enhancement of efficiency resulting from the dif-ferent modifications. It is calculated here as the ratio of theincrement in the capital cost to the increment in the exergy effi-ciency. The best option for improvement is selected when the frac-tion is at the lowest value: this is the option with the lowest capitalcost for enhancing the efficiency of the system.
2.4. Sensitivity analysis
The price of electricity, district heating, gate fees and operatingcosts are based on estimations and can change with time. A sensi-tivity analysis was carried out to ascertain the uncertainty of theseprices on the economic viability of process plants. The productionprices, operating costs, and the income from the process plantswere therefore varied from the range of �40% to +40% of the baseprice. For electricity production only, price variations of 0–140%were used in order to determine the economic feasibility of thesystem. The range of price variations were chosen so as to coverthe break-even point. The sensitivity analysis was made by adjust-ing one parameter only (an income or a production cost) whilekeeping the others constant.
3. Results and discussion
3.1. The cost of improving efficiency
The enhancements made to improve the efficiency of the pro-cess were compared with the capital cost and profit of the systemin order to ascertain its economic viability.
The base case waste combustion plant, which has an exergyefficiency of 25% and a capital investment cost of $ 176 million,was improved by considering the seven different modificationsdescribed in this work. The capital investment cost for 27 ton/hwaste input used in this study is comparable with investment costestimated to be between $ 145 and $ 207 million for a capacity of25 to 35 ton/h fuel input as reported by Taherzadeh and Richards(2016) for the cost of waste-to-energy plant in part of the Europe.
Modification 6, which involves not only re-arrangement butalso changing the air heating medium (from steam to flue gas),does not have a significant effect on the efficiency increment ofthe base plant. It is, however, the best option for improvementwith regards to the lowest capital cost per unit increase in effi-ciency (Fig. 2). The result is a 0.6% decrease in the capital cost ofthe base case process plant. It is also the second-best alternativefor the lowest capital cost per total revenue earned.
Modifications 1 and 7, which incorporate flue gas condensation,are the second-best options for efficiency improvement when com-pared with the costs involved and the two best alternatives for thecapital cost per total unit of revenue generated (Fig. 2). This can beattributed to the flue gas condensation component integrated intothe systems, which enhances the production of heat and increasesthe overall efficiency of the base case by 4%, as seen in bothmethods.
Modifications 4 and 5, which include waste gasification, havethe highest exergy efficiency, namely 30.1% and 30.5%, respec-tively. The improvements in efficiency experienced in these modi-fications are the result of increasing the steam conditions to 540 �Cand 121 bar from the 420 �C and 50 bar of the base case. However,the capital investment cost per increase in efficiency is higher thanfor the other alternatives, as shown in Fig. 2. Here, the improve-ment methods are not favourable when the capital cost of theenhancement is considered. This is attributed to a huge differencein the capital cost compared to the base case, although it should benoted that this refers only to the cost associated with an increase inefficiency and says nothing about the total revenue. In the case ofthe flue gas condensation process experienced in Modification 5,the cost of efficiency increment can be decreased by 6%.
Modifications 2 and 3, both of which have a reheater, are thenext improvement methods with a high capital investment costper efficiency increase. Nevertheless, these two methods helpeliminate the moisture in the steam that causes erosion of the tur-bine blades, and would thus also reduce the maintenance cost of
Table 3The investment parameters used in the economic analysis of waste-to-energy CHPplants.
Name units value
Cost indexa 2015 in USDTax Rate* %/y 22Interest rate* %/y 6Economic life of the plantb y 20Depreciation methodc Straight-lineWorking capitalc %/y 15Operating hoursa h/y 8000Ash treatmentd USD/ton 55District heating network & support costd USD/kWh 0.045Flue gas treatmente USD/ton 14Waste pretreatmentd USD/ton 30District heating costd USD/kWh 0.09Electricity costd USD/kWh 0.05Gate feesd USD/ton 55
Source: aAspenTech (2012); bEuropean Commission (2017); cTowler et al. (2013b);dTaherzadeh and Richards (2016); eBosmans et al. (2013); *Communication withplant operator.
F.C. Eboh et al. /Waste Management 100 (2019) 75–83 79
the turbine. A reduction in the cost of enhancing efficiency of about29% can be achieved using Modification 3, which employs flue gascondensation.
3.2. Economic evaluations
The net present value (NPV) and internal rate of return (IRR) ofthe process plants investigated are presented in Fig. 3. It showsthat Modifications 1 and 7 have the highest NPV and IRR, withan increase of 30% and 12%, respectively, when compared withthe base case process plant, indicating that these are more eco-nomically viable than the other improvement options. Flue gascondensation incorporated into the systems increases the heat thatcan be produced, which has a significant impact on the overallincome (Fig. 4). These two modification processes have the highestamount of heat production.
Modification 6 is the next efficiency improvement alternativewith 7% and 3% increments, respectively, in the NPV and IRR ofthe base case. These are also attributed to the high production ofheat in this system, which contributes significantly to the total rev-enue generated. It is the best method for heat production when theflue gas condensation is not integrated in the process plant (Fig. 4).The IRR of 12.21% calculated in Modification 6 is in agreement with12.21% and 12.22% obtained by Udomsri et al. (2010) and Zhaoet al. (2016) respectively for a waste combustion plant of similararrangement.
In Modifications 2, 4 and 5, reductions in the NPV and IRR(Fig. 3) were observed, i.e. the base case process plant is seen tobe more economically viable, and hence they are not the bestoptions for efficiency enhancement of the base case. However, apositive value of the NPV in the different improvement methodsshows that they are in fact profitable.
The flue gas condensation process was not considered in theevaluation of a waste combustion plant producing only electricity.Therefore, only the efficiency improvements of Modifications 2, 4and 5 were investigated and compared with the base plant. InFig. 5, the negative values of the net present value show that boththe base case plant and the different modification processes werenot economically feasible, indicating that the district heating net-work contributes significantly to the profitability of a waste-to-energy plant. The revenue generated by the combined heat andpower plant, shown in Fig. 4 by the base case (BC) (the leastincome earned), is M$ 16.7 more than the highest revenue gener-ated in Fig. 5 by Modification 4 (the highest income earned) in thewaste combustion plant producing only electricity. It can also beobserved in Fig. 5 that Modification 4 generates the highest rev-enue, although it does have the lowest net present value whencompared with other improvement options.
The variations in the price range due to uncertainties in theprice of district heat and electricity, gate fees and production costsare presented in Fig. 6. The indication is that the price of districtheat and its maintenance costs have a significant impact on the
Fig. 2. The ratio of the increment in the capital cost to the increment in the exergy efficiency and capital cost per total revenue.
Fig. 3. Net Present Value (NPV) and Internal Rate of Return (IRR) for the base case and various modifications.
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economic viability of the plant: more than 70% of the energy prod-ucts and 49% of the annual income of the waste combustion plantsinvestigated are derived from the production of district heat(Fig. 4).
Fig. 6c shows the effect that NPV has on the variations in theprice of electricity produced and the gate fees paid. It can be seenthat gate fees affect the economic viability of waste combustionwith electricity production only: this is in agreement with previouseconomic assessments of energy recovery from solid waste (Lemeet al., 2014). The base case plant, as shown in Fig. 6c, becomes moreattractive at about a 122% increase in the price of gate fees.
3.3. The impact of improvements in waste-to-energy plants in acircular economy
A circular economy involves a reduction in the use of waste andresources by maintaining the value of products, materials and
resources for a long time (European Commission, 2015). Theenergy recovered from waste combustion can be one of the keyfactors for the successful conversion of waste into a valuableresource when managed efficiently. According to The EuropeanCommission (2017), waste-to-energy can contribute effectively inthe transition to a circular economy provided that it is firmlyguided by the waste hierarchy: the top priorities should be basedon the minimization of environmental effects and the optimizationof resource efficiency. One of the ways of achieving this target is touse state-of-the-art energy-efficient waste combustion technolo-gies, as investigated in this study. Modifications 4 and 5, involvingintegrated waste gasification-combustion methods, are the mostefficient options for improving the waste combustion process andthereby supporting the transition to a circular economy. Thesetwo modification processes have the highest exergy efficiencies,with lesser emissions to the environment. In general, waste-to-energy CHP plants are better alternatives for improving efficiency;they help provide an action plan for achieving a circular economythat is more in agreement with the waste hierarchy than wastecombustion plants that produce electricity only.
In order for the transition towards a circular economy to besmooth, the increase in the capacity of energy recovery from wastecombustion needs to be balanced to ensure that recycling andreuse are not jeopardized (European Commission, 2017). The over-capacity of waste-to-energy facilities, especially in the northernpart of Europe, has been seen as the result of a high demand forheat via district heating networks. Sweden and Denmark havethe highest incineration capacity of 591 kg/capita and 587 kg/cap-ita, respectively, (European European Commission, 2017). How-ever, there is no overcapacity of incineration as far as the entireEU member states are concerned: the eastern and southern partsof the EU are highly dependent on landfill and, moreover, they lackadequate waste combustion facilities (European Commission,2017). The high processing capacity in Sweden contributes, forinstance, to the low profitability of Waste-to-Energy plants inNorway, as much of their municipal solid waste is exported to Swe-den due to of lower gate fees (Lausselet et al., 2016). According toLausselet et al. (2017), Sweden could accept lower fees thanks tothe higher income generated from its well-developed district heat-ing system.
The circular economy action plan, which involves changingfrom mixed waste to separate collection, will affect the composi-tion of waste used in waste combustion plants. Although the planwill increase the recycling rates of valuable materials and decreaseenvironmental emissions from the waste combustion processes,the energy production from waste-to-energy plants will, however,be reduced. Moreover, with the full implementation of a circular
Fig. 4. Energy production and annual revenues for the base case and all sevenmodifications.
Fig. 5. Net Present Value and total revenue generated for plants producing electricity only.
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economy plan, waste combustion plants will be useful detoxifyingfacilities for eliminating toxic constituents in the environment.
4. Conclusions
Evaluations of improvements that can be made in a waste com-bustion plant may be more effective when the costs of suchimprovements are considered. It enables adequate decisions tobe reached between various enhancement alternatives for theprofitability of the process plant. Seven different modificationsfor improving the efficiency of WTE plants CHP and their economicperformance have been assessed. Modification 5, which is a combi-nation of waste gasification, a gas boiler and flue gas condensation
achieved the highest percentage of improvement in efficiency. Itdoes, nevertheless, involve a higher capital cost than the otheralternatives. The capital cost for improving efficiency is seen tobe most economical in this optimization by re-arrangement ofthe air heater and changing the air heating medium (from steamto flue gas), but it shows only a very marginal increase in efficiency.Modifications 1 and 7 with flue gas condensation are the best alter-natives for the capital cost per total unit of revenue generated.Moreover, these two modifications are seen to be the most lucra-tive ventures, with the highest net present values and internalrates of return. District heating network is the most profitableincome generated by waste process plants: it has a significantimpact on the viability of the project over the expected range ofvariations in both the income and the production cost. The
Fig. 6. NPV of the base case with variations in (a) the price of the income generated, (b) production costs for CHP and (c) variations in the price of the income for electricityproduction only.
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improvement methods that have been identified as having a highdegree of efficiency enhancement do not necessary make themmore economically feasible than other alternatives. Economicviability is required for the efficient evaluation of possibleimprovements that may be made to a system. In general, awaste-to-energy combined heat and power plant is an attractiveinvestment with a positive net present value, as seen in the basecase plant and from the different modification methods studied.
Acknowledgements
The financial support from University of Borås, Sweden and theGovernment of Nigeria through the Tertiary Education Trust Fundis highly appreciated.
Appendix A. Supplementary material
Supplementary data to this article can be found online athttps://doi.org/10.1016/j.wasman.2019.09.008.
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