Methods and Toolsand Optimization of
For AnalysisPower Plants
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Mohsen Assadi
Division of Power Plant TechnologyDepartment of Heat and Power EngineeringLund Institute of Technology
Doctoral Thesis
Methods and Tools for Analysis andOptimization of Power Plants
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Mohsen Assadi
Akademisk avhandlingsom for avlaggande av teknisk doktorsexamen vid tekniska fidcultetenvid Lunds Universitet kommer att firsvaras vid offentlig disputation
fiedagen den 6 oktober 2000 kl. 1015i sal M:B, M-huse; Ole R6mers v~g 1, Lunds Tekniska Htigskola
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Olganimioss DOcmneutnameLUND UNIVERSITY DOcrOIULDISSERTATIONDivisionofThermalPowerEngineering Date of issue
DepartmentofHeatandPowerEngirteeting September11,2000
LundInstituteofTechnology COD8M ISR3’JLUTIvlDN/TMVK--lO2l--SE
Author(s) Spoosxingorganization
Mohsenkisadi
Titleandsubtitle
MethodsandToolsforAnalysisandOptimiition ofPowerPlantsAbstract
Modemsncieties’timctiorralityisstronslydependentontheelectricity.Eilicientjenvironmentfriendly, and economical pnwer
productionhasbeeninfocus for a Iong time. The introduction of computers and drereby computer-aided tools for pm-designstudies,optimization and choice of the best opcmtinnal strategies, haa changed the conditions for pnwer production tremendously.The moat noticeable advantage of the intmducrinn nf the computer-aidedteds in the field of power generation, haa been the abilityenstudy the plant’s performanceprior tn the construction phase. The results of these studies have nrade it pnasible to change andadjust the plant layout to snatchthe pre-detined requirements.Further developmentof computem in recent yeara haa opened up for implementationof new features in the existing tools and alsofor the development of new tnols for specific applications, like thernmdynamic and economic optimization, prediction nf therefraining component life time, and fault diagnostics, resulting in impmvenrent of the plant’s performance, availability andreliability.The mnat common tmls for predeaign sludies am heat and mass balance programs. Further thermodynamic and economicoptimization of plant layouta, generad by the heat and mass balance programs, ean be accomplished by using pinch pmgmms,esersy analysis and tieorro=onotica. Suweihnceand fault diagnostics nf existing systems can be performed by using tnnls likecmufitionmonitoringsystems and artificial neural networks.The increased number of tools and their various cnnatructionand application arms make tbe choice of the most adequate tnol for acefiin application difiiculL In this thesis the development of different categories of tnnls and tec~lques, and their applicationarm am reviewed and presented. Case studies on both existing and theoretical power plant layouts have been performed usingdifferent commercially available tools to illuminate their advantagea and shortcomings. Tbe development of pnwer planttechnologyand the rqdremurts for new tnola and measurementsystems have been briefly reviewed.This thesis contains alan programming techniques and calculation methods concerning part-load calculations using InealIinearizatinn, which has been implemented in an in-house heat and rnaaa balance pmgmm developed by the author. Results nfcalculations performed by the in-home pmgmm have been compared with results fmm cnmmcrcial pmgmnrs. The comparisonshows gond cmraiaterrcy[1, 2]. Methcds suggested by the author increase the numerical stability, reduce the calculation time, andimpmve the user-fiiendlinesaby facilitating free choice of input data.
Key words
Pre-design, Heat and massbalance,Gashsnbine
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‘-’’tin ?(-e”d~) ~Dwmon of hermal Power engineering, Lund Institute of TechnologyBox 118, S-221 00 LUND, SwedenI,themxkigruc,hewtheeopynght oftheabstract of tbe Arumentioned dkertatfon. hacby grsnt to all mferenee
Signsmre~t= September 11,2000
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Methods and Tools for Analysisand Optimization of Power Plants
Mohsen Assadi
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LUND UNIVERSITYLund Institute of Technology
October 2000Doctoral Thesis
Division of Power Plant TechnologyDepartment of Heat and Power Engineering
Lund Institute of TechnologySE-221 00 Lund$weden
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Mohsen AssadiISBN 91-628-4372-9ISSN 0282-1990ISRN LUTMDN/TMVK-102 l-SEPrinted in Sweden, KFS ABLund 2000
DISCLAIMER
Portions of this document may be illegible
in electronic image products. Images areproduced from the best available originaldocument.
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Abstract
Modern societies’ fimctionality is strongly dependent on the electricity. Efficientenvironment friendly, and economical power production has been in focus for a longtime. The introduction of computers and thereby computer-aided tools for pre-designstudies, optimization and choice of the best operational strategies, has changed theconditions for power production tremendously. The most noticeable advantage of theintroduction of the computer-aided tools in the field of power generation, has been theability to study the plant’s petiormance prior to the construction phase. The results ofthese studies have made it possible to change and adjust the plant layout to match thepre-defined requirements.
Further development of computers in recent years has opened up for implementation ofnew features in the existing tools and also for the development of new tools for specificapplications, like thermodynamic and economic optimization, prediction of theremaining component life time, and fault diagnostics, resulting in improvement of theplant’s performance, availability and reliability.
The most common tools for pre-design studies are heat and mass balance programs.Further thermodynamic and economic optimization of plant layouts, generated by theheat and mass balance programs, can be accomplished by using pinch programs, exergyanalysis and thermoeconomics. Surveillance and fault diagnostics of existing systemscan be performed by using tools like condition monitoring systems and artificial neuralnetworks.
The increased number of tools and their various construction and application areas makethe choice of the most adequate tool for a certain application difficult. In this thesis thedevelopment of different categories of tools and techniques, and their application areaare reviewed and presented. Case studies on both existing and theoretical power plantlayouts have been performed using different commercially available tools to illuminatetheir advantages and shortcomings. The development of power plant technology and therequirements for new tools and measurement systems have been briefly reviewed.
This thesis contains also programmingg techniques and calculation methods concerningpart-load calculations using local linearization, which has been implemented in an in-house heat and mass balance program developed by the author. Results of calculationsperformed by the in-house program have been compared with results from commercialprograms. The comparison shows good consistency [1, 2]. Methods suggested by theauthor increase the numerical stability, reduce the calculation time, and improve theuser-friendliness by facilitating fi-eechoice of input data.
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List of Papers
1.
2.
3.
4.
5.
6.
7.
8.
Assadi M., Johansson K. B., Applying Pinch Method and ExergY Analysis to a BIO-IGIL4T Power Plant, 2nd Co&erence on Process Integration, Modelling andOptimisation for Energy Saving and Pollution Reduction, PRES’99, pp. 139-144,Published by Hungerian Chemical Society, Budapest, Hungary, May 31 - June 2,1999.
Assadi M., H5gglund T., A computational investigation of a combined cycle usingan SI-engine, a Rankine bottoming cycle and a fuel cell, European Fuel cell news,Newsletter of the European Fuel Cell Group, Ltd., Volume 6, Number 2, pp. 5-7,July 1999.
Assadi M., Jansson S. A., Blomstedt M., Increasing thermal eficiency of a PFBCpower plant using a natural gas fueled gas turbine, The First lntemationaSymposium on Computer Aided Process Engineering, ISCAPE 2000, Cartagena deindia, Colombia, January 24-28,2000.
Assadi M., Torisson T., Integration of biomass-fueled power plants and SI-en@”nes,a method for increasing power output from existing plants, 4ti InternationalCotierence of Iranian Society of Mechanical Engineers, ISME 2000, pp. 409-412,Tehran, Iran, May 16-19,2000.
Arriagada J., Assadi M., Air bottoming cycle for Gas Turbines, 4ti InternationalConference of Iranian Society of Mechanical Engineers, ISME 2000, pp. 447-454,Tehran, IrarL May 16-19,2000.
Assadi M., Hildebrandt A., A Computational Investigation of a Biomass FueledIntegrated Gasljication Cascaded Humid Air Turbine, Bio-IGCIiMT, to be presentedat 14* International Congress of Chemical Engineering, QUBEC 2000, Sao Paulo,Brazil, September 24-27,2000.
Assadi M., Mesbahi E., Torisson T., Lindquist T., Arriagada J., Olausson P., ANovel Correction Technique for Simple Gas Turbine Parameters, submitted toASME TURBOEXPO 2001, New Orleans, USA.
Mesbahi E., Assadi M., Torisson T., Lindquist T., A Unique Correction Techniquefor Evaporative Gas Turbine (EvGT) Parameters, submitted to ASMETURBOEXPO 2001, New Orleans, USA.
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Acknowledgements
This work was earned out at the Department of Heat and Power Engineering. I wouldlike to express my gratitude to all colleagues at the department. I would like toespecially thank my supervisor Professor Tord Torisson for his support, encouragementand important suggestions and my old friends and colleagues Ph. D. Jens IUingmannand Ph. D. Hamid Nasri for all interesting discussions, Ph.D. Ehsan Mesbahi, and M. SC.Pernilla Olausson for their comments to the thesis. I would also like to thank my firmc6eNada and my daughter Anahita for their support and patience.
This research was financed by the National Swedish Board for Industrial and TechnicalDevelopment and the National Swedish Energy Authority (STEM). Their financialsupport is gratefully acknowledged.
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Nomenclature
ABC: Air Bottoming CycleANN: Artificial Neural NetworkCC: Combined CycleCHAT: Cascaded Humid Air TurbineCHP: Combined Heat and PowerCMS: Condition Monitoring SystemDS: Directionally SolidifiedEvGT: Evaporative Gas TurbineFC: Fuel CellGT: Gas TurbineHAT: Humid Air TurbineHEN: Heat Exchanger NetworkHMBP: Heat and Mass Balance ProgramHRSG: Heat Recovery Steam GeneratorPFBC: Pressurized Fluidized Bed Combined cycleRC: Rankine CycleSI-engine: Spark Ignition EngineSOFC: Solid Oxide Fuel CellSTIG: Steam Injected Gas TurbineTBC: Thermal Barrier CoatingTJT: Turbine Inlet TemperatureTNUIC Total Number of UnKnowns
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Contents
Nomenclature ..............................................................................................vi1. Introduction .............................................................................................1
1.1. Objectives ............................................................................................21.2. Limitations ..........................................................................................21.3. Metiods ...............................................................................................31.4. Dispositionof the thesis......................................................................3
2. Power plant systems ................................................................................52.1. Steamcycle .........................................................................................52.2. Gas cycle .............................................................................................62.3. Combined cycle ...................................................................................62.4. New concepts ......................................................................................7
3. Gas turbine development ........................................................................93.1. Introduction.......................................................................................103.2. Blade cooling ....................................................................................ll3.3. Materials development...................................................................... 133.4. Thermal barrier coatings ...................................................................l4
4. Tools and methods for power plant analyses and optimization ........174.1. Heat and mass balance programs (HMBPs) .....................................17
4.1.1. General layout ............................................................................................... 184.1.2. Mathematical calculation methods ............................................................... 194.1.3. Program structure .......................................................................................... 194.1.4. Part-load calculations .................................................................................... 21
4.2. Optimization ......... .. .. ... .... ... .. ... .. . . .. .. ... ... .... ... ... .. .. . ... ... ... ... ... ... .. .... .. .. .23
4.2.1. Parameter stidy .............................................................................................234.2.2. Pinch tecbolo~ ...........................................................................................244.2.3. Exergy and tiemoeconotics ....................................................................... 254.2.4. Condition monitoring and artificial neural networks .................................... 26
5. Discussion and future work ..*....eo.o.e.*..*.eo.e*ee*.e*....................................296. Conclusions ............................................................................................337. Summary of papers ...............................................................................35Appendix 1..................................................................................................41Appendix 2.e.**o................***......*...****...........................................................45Reference list ..............................................................................................51
t.
vii
1. Introduction
The fimctionality of modem societies is strongly dependent on the electrical power.Today’s generation of heat and power is dominated by fossil fuel based energyconversion technology. Major fuel resources for combustion based energy conversionare oil, gas and coal. The major drawback for this type of technology is the unavoidableemission of harrnfid species to the environment. Emissions related to combustion basedprocesses, such as S0. and NO., cause acid rain, and emission of C02 is believed tocontribute to global warming. Emission reduction techniques have been in focus formany years now. Technical developments concerning combustion devices have resultedin lower emissions from all kinds of fossil fueled power plants. During the last decadestwo approaches for emission reduction have been usedj namely increased power plantefficiency, and reduced emission formation during the combustion process.
To be able to get a perspective over the research field in the area of power generationuntil today, and to identi~ research areas for the foreseeable fhture, it is necessary tounderstand the mechanisms affecting the heat and power generation market.
The higher I%el price after the oil crisis, the enormous development of personalcomputers, the end of the cold war, global climate changes and the deregulation of theelectricity market are some of the most important factors that have had a large influenceon the development of the electricity generation devices and related research field.
Power plant efficiency enhancement is important for two reasons:. economizing the world’s limited fossil fuel reserves, and. lowering the specific emission per produced power unit.
Increased power plant efficiency has been possible due to both sofhvare development,resulting in more effective tools for pre-design studies, and hardware developmentallowing for higher firing temperatures.
The software development experienced a boom during the late 1980s and early 1990sbecause of the revolutionary development of personal computers. More flexible andpowerful tools for theoretical pre-design studies like heat and mass balance programsand three-dimensional flow path analysis became available. Using these tools has madeit possible to design and analyze power plant solutions prior to the construction phase,so that the most effective and cost-optimal solution can be selected.
Afier the end of the cold war, advanced materials and cooling technologies used inmilitary aircraft was made available for industrial gas turbines. They resulted in a majorefficiency increase in gas turbine based power plants, mainly due to a higher allowablemaximum temperature. A combination of advanced technologies, three-dimensionalflow path analysis and gas turbine adjustment to combined cycle plants have resulted inelectrical efficiencies in the order of 60°/0.
Further increases in plant efficiency, emission control, availability and condition basedmaintenance are (most likely) going to dominate power generation research areas in thenear future. The development of computer capacity and speed of data processing areopening up continuous operation surveillance and data acquisition. It is necessary forthe competitiveness of advanced power generation units to keep the availability highand the maintenance costs low. It is also important to be able to handle an enormousamount of data in an intelligent way, to predict faults before they occur, and to estimatethe time to the next stop for maintenance.
Development of tools for analysis and optimization of power plants has been highlyaffected by changes in the condition of the global market. Comprehension of thesof?svaredevelopment process requires knowledge of demands and abilities of the actualtime period. Therefore an analysis of computer aided tools and their development,isolated from the development of engine components and materials and changes ineconomical conditions of the global market, will be incomplete. Chapter three dealswith gas turbine development in recent years, and is included in this thesis to illuminatenew demands for analysis tools that have arisen from the technical development ofpower plant systems.
This thesis mostly illuminates the following two research areas:. the development of calculation programs for pre-design studies, with suggestions for
further improvement of these tools, and● new tools for continuous surveillance and data analysis that increase plant reliability
and availability, and decrease maintenance costs.
1.1. Objectives
The objective of this thesis is to contribute to deeper understanding of methods andtools for analysis and optimization of power plant systems at the pre-design stage and atthe operational phase, and to develop more effective methods for par-load calculationof steam and gas based power plant systems in a heat and mass balance progmm.
1.2. Limitations
The methods and tools studied in this thesis are based on thermodynamic andeconomical parameters. Reliability related methods, based on material properties andlife time, are discussed but not taken into consideration in the studied tools. Tools basedon three-dimensional calculations and dynamic simulation are outside the scope of thiswork.
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1.3. Methods
The present work started with a literature study over relevant tools. The literature studyhas been reported in the authors Iicentiate thesis [1]. An in-house program for heat andmass balance calculations at both design point and part-load was developed by theauthor, and compared to other commercial heat and mass balance programs [1, 2]. Thepart-load calculation metho~ used in the in-house program, is unique and waspublished for the first time in the author’s licentiate thesis. The calculation method issummarized in chapter 4 and in appendix 2 in this thesis.
In order to illuminate the requirements for the analysis tools, not only at the presenttime but also in the fiture, a study concerning the development of hardware in steamand gas cycles, as well as in power plants based on new concepts, has been performed.
The theoretical work in the thesis has been supplemented by experimental data from thegas turbine laboratory at the department, and the existing power plants in Europe and inthe USA.
Many of the articles in this thesis have been written by the author in close co-operationwith colleagues from industry and other universities. Co-authors from Sydkrafi ABBStal, Wartsila NSD, Hannofer University and Newcastle University are represented inthe papers.
1.4. Disposition of the thesis
A short introduction and the scope of the work are presented in chapter one.
In chapter two, there is a brief description of the systems to which the studied methodsand tools are applied.
Chapter three presents a survey of the interesting hardware development affecting thefuture development of the methods and tools studied in the thesis.
The most important concept analysis tools, i.e. the heat and mass balance programs, arediscussed in chapter four. This chapter also includes a brief review of power plantoptimization and operational tools.
Discussion and fhture work are presented in chapter five, and conclusions are presentedin chapter six.
Chapter seven provides a discussion about the papers this thesis is based on.
At the end of the thesis, there are eight papers enclosed in the form they have beenpublished. Papers seven and eight focus on the operational phase of the power plants,while the first six papers deal with problems in the retrofit and pre-design phase.
2. Power plant systems
Power plant systems can be divided into two major categories, steam and gas cycles.Power cycles consist of different components comected to each other, like pump,compressor, heat exchanger, expander, etc. The simplified working principle of a powercycle is tha~ a working medium is pressurized, heated up, and expanded, therebygenemting power.
Power cycles of different types have been described and analyzed in many books [3,4,5]. In this chapter, some of these cycles are briefly described.
2.1. Steam cycle
The simplest steam cycle using water as the working medium, the Rankine Cycle (RC),consists of a pump, where the feed water is pressurize a boiler, where the water isboiled and high-pressure steam is generated, a steam turbine, where the high-pressuresteam is expanded, and a condenser where the low-pressure steam is condensed beforeentering the pump. The steam turbine is coupled to a generator, converting themechanical shall power to electricity.
A real steam plant may contain many other components, e.g. feed water pre-heaters, andcan utilize reheat and several pressure levels to improve the cycle efficiency.
Working fluids other than water have also been used in steam cycles, to utilize low-temperature heat sources and/or to keep the size and thereby the efficiency of the steamturbine in small plants (~OOkW) at an acceptable level [6, 7].
A mixture of water and ammonia has also been used as the working fluid in a steamcycle, known as the Krdina cycle. The major idea of this cycle is to minimize exergylosses that occur during heat transfer. Since a mixture of two different fluids boils andcondenses at a sliding temperature, instead of at a constant temperature as in the case ofpure substances, exergy losses during heat transfer are reduced [8, 9].
Since heat from combustion gases is transferred to the water circuit by heat exchangersin the boiler, the steam plant provides a large fiel flexibility. Nuclear power plants arealso based on steam cycles. The heat source in the nuclear power plants is radioactivematerial, and fission in the reactor replaces the combustion process in the boiler.
Using a liquid as the working medium is also very economical, due to the small amountof power needed to pressurize the liquid in a pump compared to pressurizing gases.
As mentioned above, the most important advantage of the steam cycle is the large fielflexibility (see papers 3,4 and 6). Some of the disadvantages with a steam cycle are thelimited maximum allowable temperature that curtails the maximum cycle efficiency, theplant size because of the low specific power, the need for low-temperature heat sink forcondensation, and the slow startup and load following capability.
2.2. Gas cycle
Major components of a gas cycle are the compressor, where the combustion air ispressurized, the combustion chamber, where fiel is combusted using the pressurized airas oxidizer, and the expander, where the hot combustion gases are expanded.Mechanical shail power from the expander can be converted to electricity in agenerator, or used for mechanical drive.
The most common type of gas cycle is the open simple gas turbine. More complex gascycles have also been designed and built to improve their performance. Using intercooler, afier cooler, sequential combustion, etc. are measures taken to improve theplant’s efficiency resulting in increased cycle complexity [10, 11, 12, 13].
Closed loop gas cycles using a working medium other than air, like Helium and C02,have also been studied. Closed gas cycles, using C02 as working medium, were builtand tested in Europe during the 1950s and 1960s. The major idea with closed gas cyclesis to increase fuel flexibility. Since the hot combustion gases are directly expanded inthe open gas cycle, fhel quality becomes a crucial factor for the lifetime of thecomponents in the hot gas path, specially in the modem and highly cooled gas cycles. Inthe closed loop, heat is transferred to the working medium through a heat exchanger.The increased fuel flexibility here has a drawback caused by the limitation in themaximum allowable Turbine Inlet Temperature (TIT). When heat is transferred to theworking medium by a heat exchanger, the maximum TIT is decided by the materialtemperature of the heat exchanger. One other advantage of the closed loop cycle is thepossibility to use working media with special thermodynamic properties, e.g. Helium[11].
A higher maximum allowable temperature means a higher cycle efficiency. Themaximum allowable temperature in a modem gas cycle is 2-3 times higher than thesteam cycle’s. This can be explained by, for instance, the short distance the hot gases atmaximum temperature are transported in the gas cycle before expansion, the steamdissociation at temperatures higher than 850° C and the construction of the controlvalves, etc., in the steam cycle.
Some of the advantages of gas cycles arefootprint low investment costs, etc. A moretheir current status is given in chapter 3.
2.3. Combined cycle
fast startup and load following, smalldetailed description of gas turbines and
The Combined Cycle (CC) consists of both steam and gas cycles. The gas cycle,working at a higher temperature is called the topping cycle, and the steam cycle,utilizing the remaining heat in the exhaust gases, is called the bottoming cycle [14].Low exhaust gas temperature in simple gas turbines results in increased thermalefficiency. However, modem heavy duty gas turbines have been developed to match thebottoming cycle, in the sense that the exhaust gas temperature is increased to provide
6
better bottoming cycle efficiencies. Today, combined cycles are approaching 60%electrical efficiency.
An additional gas turbine developmen~ resulting in a better integration in the combinedcycle, is steam cooling. Further efficiency improvement requires higher TIT. IncreasedTIT demands more cooling air, while keeping the emissions at a low level requires moreair to the combustion process. To get around these conflicting demands, steam has beenused for cooling stationary components, like the transition piece and the vanes of thegas turbines [15]. General Electric has announced a new generation gas turbine to be inservice at the end of 2002, called H-class, that utilizes steam cooling even for rotatingblades [16, 17, 18].
2.4. New concepts
Integration methods between gas and steam cycles other than the combined cycle havealso been studiec+ resulting in interesting concepts, e.g. the STeam Injected Gas turbine(STIG), the Cheng cycle, the Evaporative Gas Turbine (EvGT), also known as HumidAir Turbine (HAT), and the Cascaded Humid Air Turbine (CHAT).
The major idea with these power cycles is to recover the heat from the exhaust gases,using water steam or humidified air, and put it back into the system.
In STIG and Cheng cycles, heat is recovered from the exhaust gases by rising steam in aheat recovery steam generator. The steam is injected into the combustion chamber,reducing the NO. generation and increasing the power output. However, in the EvGTand the CHAT cycles, heat at high temperature is recovered in a recuperator, and atlower temperature in an economizer, generating hot water. The hot water is thenbrought in contact with the compressor discharge air, in a humidifier, resulting insaturation humidification of the air flow. The humidified air is then fin-ther heated inthe recuperator before entering into the combustion chamber (see papers 6, 8).
Since the presence of steam during combustion has shown to suppress the NOXformation, these concepts have turned out to be interesting in the sense that they bothreduce the NOX emission and increase the efficiency. The EvGT seems to be the mostpromising concept that can achieve efficiencies comparable to the CC at lowerinvestment costs and emissions [19]. For more detailed description of these cycles, thereader is referred to the publications mentioned in the reference list [20, 21,22,23, 24].
To achieve higher efficiency at lower emission levels, some other combinations ofpower production units are also studied. These systems are based on fuel cells andcombined cycles. A gas turbine, as well as a Spark Ignition (SI) engine, can be used asthe topping cycle of the CC in these systems. Paper 2 presents results from a study,where a system based on SI-engine, flel cell and a steam bottoming cycle isinvestigated. The fhel cell model used in this study is simplified, and contains only thematerial properties of the connected streams. However, results from detailed studies,concerning fuel cell modeling [25, 26], show that higher accuracy in calculated resultsrequires at least a two dimensional model of the fiel cell.
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3. Gas turbine development
The development of gas turbine technology during the last decade has made combinedcycle efficiencies near 60% possible today, and hybrid plants, with fuel cell and gast&bine, with efficiencies higher than 70% will be achieved in the near future [27].
Besides high efficiency, a combined cycle offers other benefits, like a small footprint,short construction time and low investment costs. All these benefits have madecombined cycles the most interesting alternative for power production today.
The most advanced piece of equipment in a combined cycle is the gas turbine. Gasturbines, specially heavy duty gas turbines, have been through a tremendousdevelopment after the end of the cold war.
Increasing the turbine inlet temperature has been the primary approach taken to improvegas turbine efficiency. The TIT has increased steadily from about 540”C in the 1940s toover 1425°C today. Increased TIT has become possible by the development of newmaterials and the innovative cooling techniques for the critical components, such ascoating the turbine blades with ceramic layers and cooling the blades with the dischargeair from the compressor.
Ionil LzYIwil 1970 1980 mm 21M0 alo
Year
Figure 1: Increased TIT overtime, [28]
This development has also resulted in more expensive components, requiring new toolsfor continuous monitoring and data analysis to prevent a forced outage. Newmeasurement systems have been developed to measure the critical components’temperature of the hot gas path during operation. These systems are based on opticalaccess to the components, optical pyrometry. The pyrometer collects the infi-aredradiation from blades and converts it to a temperature, using the emissivity of thematerial. Measured data are monitored so that the operator can follow the changes inmaterial temperature during operation. One of the disadvantages with these monitoring
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systems is the large amount of data collected continuously during operation. Theoperator will not be able to follow all the changes and simultaneously analyze theupcoming problems. Therefore new tools for intelligent data analysis have beendeveloped to assist the operator to identi~ engine faults.
These tools are generally based on either mathematical modeling of the enginecomponents combined with heat and mass balance calculations or statistical dataanalyses identi&ing the relationship between system input and output, like artificialneural networks. A more detailed description of these tools and methods is presented inchapter 4.
Minimizing the maintenance costs also requires prediction of the engine components’remaining lifetime. The extended blade life management requires predictive models forassessment of coating degradation and remaining life. Life management involves lifeprediction via calculation and monitoring. Here, aerothermal analyses are combinedwith the measured data to validate calculation models for prediction of components’remaining life.
Life-limiting degradation of gas turbine blades includes oxidation of the coating andspallation of the protective A1203 scale that forms on the surface of aluminide andMCrAIY *coatings. AI additional degradation mode is interdiffusion of elements of thecoating and substrate, which reduces the amount of Al available to form the protectiveoxide scale. Prediction of the remaining life for Thermal Barrier Coatings (TBCS)requires, among other factors, information about the thickness of the ~-NiAl layer thatdelivers Alto build the protective oxide layer [29, 30].
In this chapter, the effect of technology development on gas turbine improvement willbe focused on. Cooling techniques and materials development, concerning both thesubstrate superalloy and the coating, will be briefly discussed.
3.1. Introduction
Technology transfer from military jet engines to heavy duty gas turbines, after the endof the cold war, has contributed to a large extent to the rapid development of theseturbines. However, improved casting technique, allowing complex blade constructionsand cooling systems, materials development and coating technique have alsocontributed to the gas turbine development.
As mentioned earlier, increased TIT results in higher cycle efficiency. Since air coolingof the hot gas path reduces the TIT, accurate thermodynamic modeling of the gasturbine cooling system is an important issue, specially for modem gas turbines thatutilize a large amount of cooling air[31, 32].
,? .
*MCrAIY:M representseitherCobaltorNickel,Chrome,AIuminu~ andYttrium
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Other factors that influenced gas turbine improvement are using three-dimensional hotgas path modeling, replacing the silo combusters with annular combustion chambers,having less number of blades and stages to achieve the same performance, developingthe premix burners and dry low NOX burners, and adjusting the gas turbine exittemperature for better combined cycle performance.
The most important factors that have made gas turbine development possible are
. Development of material and production techniques:- directionally solidified and single crystal blades- better thermal barrier coating material and application technique- advanced cooling techniques, such as convection, film and impingement cooling
● Development of three-dimensional calculation tools foc- flow path analyses, improved geometry and higher component efficiencies– thermomechanical calculations, reduced number of blades at higher output
All these improvements have resulted in increased thermal efficiency for gas turbines,reducing the fuel costs for the same power output but tley have also resulted in moreexpensive components, like blades and vanes. To provide protection for these expensivecomponents, condition monitoring and intelligent data analyses have become the newresearch and development areas with condition based maintenance as a key factor.
3.2. Blade cooling
Advanced casting techniques make it possible to produce blades and vanes withcomplex internal cooling channels in one single crystal or directionally solidifiedstructure.
Further temperature increases, and consequently higher efficiencies, require moreeffective cooling or a better coating that can keep the material temperature under itslimits.
Air cooling is the most common cooling method used in modem gas turbines. Thecoolant, used to reduce material temperature, can provide cooling by
● convection where the coolant circulates through internal channels and is releasedinto the hot exhaust gas path through holes in the blades’ trailing edges and in thetop of the blades. Ribs or turbulators on the channels’ walls improve the heattransfer through increased turbulence intensity.
● generation of an externally isolating layer of air over the blade, called film cooling.The cooling film is generated when the air from internal cooling channels is drawnout through small holes on the blade surface. The density of cooling holes over thewhole blade depends on the thermal load on the blade parts. A shower head isusually placed in the blade’s leading edge, where the stagnation point occurs.
11
. impingement in the sense that cooling air, slotted through cooling channels insidethe blades, is released through holes building jet streams against the wall, separatingthe hot gas from the cooling air, to remove the isolating boundary layer and enhancethe heat transfer.
Usually, a combination of these three cooling methods is used in modem gas turbineblades to provide the most effective blade cooling. The objective is to reduce theamount of cooling air needed to keep the material temperature under its limits, else aconsumption of a larger amount of air for cooling purposes will result in less airavailable during the combustion process leading to increased NOXemissions.
The negative effects of open-circuit cooling on gas turbine performance (e.g. delusionof hot gases, disturbance of the flow pattern), make it important to limit the amount ofcooling air mixed with the main combustion gases. This has led to closed-circuitcooling, where the coolant is drawn through cooling channels without mixing with thehot gases. Using steam for gas turbine blade cooling will reduce the need for cooling airthat is higher than 20% of the compressor discharge in modern gas turbines. Sinceclosed-circuit cooling does not dilute the hot gas, it permits higher ftig temperaturesfor a given combustor exit temperature, leading to higher efficiency. Using steam ascoolant allows an increase in the TJT by about 100 “C without an increase in thecombustion temperature [18]. Steam is also a more effective heat transfer medium thanair. Difficulties related to steam cooling refer to fouling in the cooling channels and thethermal stresses caused by the large temperature gradients.
Effective convective cooling means relatively large temperature gradients. Fihn coolingmeans that the hot gas temperature, as experienced by the blade, decreases. This meansthat the temperature gradient over the wall is lower for the same mean metaltemperature. Instea& high stress concentrations appear at the cooling holes. Acombination of convective cooling by steam and fihn cooling by air will result in bettercooling and a homogeneous material temperature [15].
Cooling holes are problem areas because of the locally high strain ranges. The basemetal/coating interaction makes the problem more complex. The manufacturing processof the holes is also important since it may produce micro cracks and other defects closeto the inner walls of the holes.
Disadvantages with blade cooling refer to the differences in material thickness indifferent parts of the blade and the extensive cooling near the cooling holes on the bladesurface, resulting in concentration of thermal stresses. For example, cooling ribs lead tostress concentration. The middle part of the cooled blades is usually stiffer than thetrailing edge. Therefore, the material temperature at the trailing edge should not be toohigh compared to the temperature in the middle part of the blade; otherwise, there willbe a large mechanical strain range with compressive stress at high temperature. Stressrelaxation during the running of the turbine will lead to tensile stresses on shutdown,causing crack initiation and crack propagation [29].
12
3.3. Materials development
Materials developmen~ concerning both substrate superalloys and the thermal barriercoating, has had a large impact on gas turbine improvement.
Creep is the failure mode most sensitive to temperature. A 50°C non-consemative errorin average temperature results in a four- to five-fold reduction in creep life. The creepstrength of Directionally-Solidified (IX) superalloy is about three times higher thanthat of the conventional polycrystalline materials, and superalloy in single crystalsexhibit almost nine times higher creep strength than conventional polycrystallinematerials [30].
9x
3x
lx
TMFWe of DimiiOdlySOMtid, singleCrystal,8dEqui-wd CWings
nm Pofyxistd
~ cofum!mr.c@al
m single
.,.!%.;
: ,.
Cltepshnglh mmalFa!&wI?.e+sti CO&n Resklam
Figure 2: Life of blades and vanes produced by different casting techniques [30].
In polycrystalline material, grain boundaries promote creep deformation through grainboundary sliding, resulting in creep cracking. DS casting reduces intergramdar creepcracking by aligning grains and grain boundaries parallel to the axial loading on theblade, caused by the rotational movement. In single crystals, the whole blade is onecrystal without any grain boundaries, so boundary sliding is not possible in singlecrystals, and thereby creep strength is considerably improved (see Fig. 2).
Grain boundaries are also especially vulnerable to corrosion and oxidation attack. Toimprove the resistance against corrosion and oxidation, certain alloying elements areadded to the superalloy. These alloying elements have some negative effects onmechanical properties, such as ductility, and also lower material melting temperature. Insingle crystals, these alloying elements are not needed, resulting in a higher allowabletemperature.
A polycrystalline material has isotropic behavior in all directions, regardless of loaddirection. The anisotropic nature of the single crystal material can be utilized forminimizing thermal stress in the axial direction. The idea is to avoid having both highthermal and rotational loading stresses in the axial direction.
Dii3ision of alloying elements through interaction with the hot gas, changes thecomposition and therefore the basic behavior of the material.
13
The second generation superalloys are characterized by the introduction of Rhenium,which decreases the difisivity. Lower diffusivity improves the stability of themicrostructure, and therefore increases the allowable working temperature.
In cooled turbine blades and vanes, surface temperature is typically higher than theaverage metal temperature, resulting in surface degradation. In order to decrease thesurface temperature and increase the resistance against hot corrosion and oxidation,blades and vanes can be coated by different materials.
3.4. Thermal barrier coatings
The hot gas path components in a gas turbine are basically exposed to two types ofchemical attack hot corrosion and oxidation. Various salts, most of them containingsulfur, are present in the gas flow, attacking the material particularly when the salts arefluid. Therefore hot corrosion is a problem at intermediate temperatures. The oxidationaccelerates, however, at higher temperatures. Two basic classes of coatings have beenused to protect the metal blades against corrosion and oxidation, namely difiisionaluminide coating and overlay coating.
Thermal barrier coating is increasingly applied to industrial gas turbines, providing atemperature shield for metal blades, and protecting them from the high firingtemperatures. TBC is used to improve the efficiency by higher operating temperatures,to reduce the need of active cooling, and to extend the life at current firing temperatures.
Typicrd TBC systems consist of two coats, a metallic bond coat and a ceramic top coat.The bond coat, generally MCrAIY, is designed to provide oxidation and corrosionresistance. It is also a good adhesive base for the ceramic top coat. The ceramic top coatprovides a high temperature shield for the blades. The bond coat forms a thin, slow-growing M203 scale during service, which provides protection against furtheroxidation. Unfortunately, this coating can also contribute to spalling in someapplications.
TBCS which consist of a porous layer of zirconium oxide (Zroz) stabilized by yttria(y2@) and magnesia (MgO), exhibit lower thermal conductivity. As a result, theydecrease the metal surface temperature and eliminate hot spots, thus reducing thermalfatigue stress.
Hot corrosion, due to alkali sulfate ingestioq is frequent in land-based turbines and canlead to yttria depletion in the TBC and corrosion of the bond coat resulting in earlyfailures.
The oxide layer is normally where cracks initiate, since it typically has quite differentthermal properties compared to the metal layers, and because oxides are brittle. Cracksmay also transfer oxygen and other harmfid substances down to the base material wherethey might significantly contribute to crack propagation and failure.
14
The primaryadvantage of TBCs is their ability to reduce the blade metal temperature byabout 11O“C, for a 0.25mm coating. TBCS can also dampen thermal transients. One ofthe disadvantages of the TBCS is their surface roughness, which decreases aerodynamicefficiency. Another shortcoming is failure by spalling at or near the interface betweenthe bond and ceramic coat. The major reason for spallation is compressive stresses dueto thermal cycling and the growth of oxide scale at the interface [33].
Further increase in gas turbine efficiency at lower NO. emissions requires moreadvanced TBCS with lower conductivity. Multilayer coatings seem to be a promisingtool to achieve the required petiormance improvements. Phonon transport is thepredominant conduction mechanism in dielectric materials, like TBCS [34]. The basicidea with multilayer TBCS is based on the enhancement of the phonon scattering toreduce conduction. One of the advantages of multilayer coatings is that the alteringlayers can be selectively designed to reflect radiant energy.
Test results show that multilayers composed of yttria stabilized zirconia and aluminareduce the thermal conductivity effectively. Muhilayer TBC has a thermal conductivityof about half that of conventional TBCS and also rejects up to 70% of incoming mdiantenergy [33].
The use of alloyed ceramics is beneficial for TBC applications, because the presence ofimpurity atoms in solid solution decreases the thermal conductivity. Decreased thermalconduction is the result of phonon scattering due to differences in mass of thesubstituted elements, differences in binding forces of the substituted atom comparedwith the original lattice, and the elastic strain field around the substituted atom. Theamount of scattering that occurs, and the associated reduction in thermal conductivity, isproportional to the volume fraction of impurity elements that are added to the parentmaterial.
15
4. Tools and methods for power plant analyses and optimization
Computer-aided tools for power plant analysis and optimization are designed to handledifferent types of problems. These tools c- however, be placed in two majorcategories, one utilizing thermodynamic based models, like heat and mass balanceprograms, and the other using statistical relationships between input and output of aspecific system, like artificial neural networks.
Tools from both categories have their advantages and shortcomings, and it is importantto select the proper tool for a specific type of study. Heat and mass balance programsare very usetid, specially during pre-design studies, and can be used for performanceanalysis during operation (see papers 2, 3, 4, 5, 6). Heat and mass balance programs arealso the major part of the condition monitoring systems. Dynamic modeling andsimulation can be performed using these types of tools with time dependent calculationprocedures.
Tools based on artificial neural networks, using the statistical relationship between inputand output values of a specific system at different operational conditions to generate amathematical model, have been used for a wide range of applications. Conditionmonitoring, sensor validation, fault diagnostics, dynamic modeling and control are someof the applications of these tools in power plant systems (see papers 7 and 8).
4.1. Heat and mass balance programs (HMBPs)
Heat and mass balance programs comprise the heat and mass balance calculations ofcomponents in power plants and industrial processes. Results of these calculations areneeded, for example, to determine plant efficiency.
The most important application field for HMBPs is probably the investigation of a newpower plant concept’s potential, prior to the construction phase. Modeling the processschema without restrictions, and studying its thenuodynamic and economic conditionsin pre-design studies, saves both time and money. Therefore program flexibility,modularity, mathematical stability and user-fiiendliness have been in focus during thelast decades.
Before the computer age, the calculation process for power plants was a difficult andtime-consuming task and the possibilities to investigate new concepts’ advantages werevery limited. The first generation of computer programs for heat and mass balancecalculations were designed so that a few well-knowq pre-defied plant layouts could bestudied. The major objective of these programs was to study the effect of changes incurrent parameter values on the plant performance. When changes that were needed toimprove the plant efficiency were identitled it was easy to validate the results byadjusting the parameters in the existing plant. The lack of flexibility to createcompletely new plant layouts or to modifi the existing plants was a serious limitation.
17
The rapid development of computer technology during the last decade has had a largeimpact on HMBP development. The most important effects of increased calculationspeed and data storage capacity in modern computers are the possibilities to implementgraphical interface, new programming methods and advanced mathematical calculationprocedures.
In this chapter, programmingg aspects concerning mathematical modeling and programstructures are discussed, and methods for further improvement of HMBPs, implementedin an in-house program developed by the author, are presented. A brief discussionconcerning part-load calculations based on local linearization, suggested by the author,is also presented.
4.1.1. General layout
A HMBP based on a graphical interface is generally built as a menu controlled program.Components such as heat exchangers, turbines, etc. are presented as icons in a screenmenu. The user is able to compose the desired process plant, using a drag and droptechnique.
A component in the HMBP is presented as a graphical icon with incoming and outgoingstreamlines. Working medium and electrical effect are transported by the streamlines.In- and out-flows are presented as a set of property data, describing the current status ofthe working medium. Material properties of the working medium, presented as tables orprogram codes, are delivered at call.
To create a system, the user connects components by streamlines. Connecting twostreams means that stream data are compared and copied over, so that one streamlinecontaining the whole data set is generated. During generation of the plant’s flowdiagram, components and points receive numbers that identi~ their generationsequence. These numbers will be referred to as point or component numbers. Asystematic data presentation, adopted to the material’s properties in the streamlines,makes the data transfer between connected points easier [1].
,.
The icon itself represents the component and contains a set of equations. Theseequations model the component’s thermodynamic behavior. The mathematical modelpresenting a component can be simple or detailed. The modular program structuremakes continuous updating of the mathematical models and use of several parallelmodels for the same component possible. Figure 3 illustrates a general componentmodel.
I I
Figure 3: Component model as presented in the computer program.
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4.1.2. Mathematical calculation methods
As mentioned earlier, every component of the HMBP is presented as a set of equations.During the calculation process, these equations are treated by one of the following threemethods: the sequential, the equation-oriented or the semi-parallel method [1].
In the sequential method, the calculation process starts in a component and follows thematerial flow downstream of the system. Generally it is assumed that calculation of onecomponent generates enough data to calculate the next component downstream of theprevious one. An important advantage of this method is a straightforward programmingprocedure. One disadvantage of this method is the unavoidable iteration process causedby recirculating flows within the system. These iterations can be very time-consumingand sometimes end with a divergence.
The equation-oriented method was developed to avoid problems related to the iterativeprocess and mathematical difficulties in the sequential method. In this method allequations representing the entire system are collected in a matrix and solvedsimultaneously. One difficulty with this method is the need for “good” starting values toinitiate the system matrix, to avoid mathematical instability. Experiences with this typeof system have shown that lacking good guesses during the initiation process can resultin severe mathematical difficulties. One approach to avoid this kind of problem is tobuild the plant in smaller segments and use the resolved data from the segments as thestarting values for initiation of the matrix.
The semi-parallel metltod is a combination of the two previously mentioned methods.Using the sequential method to solve as many unknown parameters as possible beforegeneration of the system matrix, decreases the size of the matrix and thereby also themathematical instabilities. The combination of the free choice of variables, i.e. data toinitiate the system with, and the semi-parallel method results in the fastest and moststable program algorithm for a HMBP.
4.1.3. Program structure
Creating a hierarchical structure gives the programmer the possibility of implementingcontrol routines and monitoring the calculation process. The idea is to generate aprogram structure that makes it possible to address a specific variable at any time duringthe calculation. The most important advantage gained by this programming method isthe possibility of identi@ing numerical difficulties, e.g. division by zero, and generatingan error message addressing a specific equation in a specific component, where theproblem has occurred. An exact localization of the error prior to a mathematical crashdown increases the user’s ability to handle the problem quickly and efficiently [1].
.,
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4.1.3.1. Variables
The smallest unit of a mathematical system is the variable. The most importantinformation carried by the variable is its numerical value. The variable can also bedefined in such a way that it contains special properties to ease the programmingprocess and the calculation control. One of these properties can be a status identifier thatobtains one of the three following values.. Knowm the user has initiated a value for the variable. Unknovvm the variable has no numerical value and must be calculated● Calculated: the variable has received a numerical value as a result of calculations
Variables can easily be sorted using a status identifier, such as one that identifiesunknown variables after a calculation loop.
Another identifier needed, e.g. for automatic transformation of the unit system, carriesthe following information.● Current value: numeric value of the variable, converted to the unit system used
during the calculations.. Symbolic value: numeric value with a specific unit, given to the variable by the
user.
The data initiated by the user can be converted to a pre-defined unit system before thecalculation process. However, calculation results are presented in the unit system usedduring the initiation [1].
4.1.3.2. Free choice of data
High flexibility and user-friendliness in combination with calculation control and emorlocation can be implemented in HMBPs in different ways. Free choice of parametersduring system initiation is one of the most important factors to increase flexibility anduser-friendliness. It means that all variables, representing streams and components, arepresented in the data initiation form, and the user can fi-eely select the parameters to beinitiated as input to the system.
Free choice of data requires that every single equation is presented as many times as thenumber of parameters it includes, so that every parameter can be expressed as a functionof the other parameters of the equation. Of course this solution metiod enlarges theprogram code enormously with all the disadvantages that would follow. This solutioncan be avoided by presenting the basic mathematical operations as subroutines, likeaddition, subtractio~ etc. The idea is to calculate a value for any of three parameters, ine.g. a multiplication like “A = B. C “, when two variables are known. This method
makes it possible to control the calculation procedure and report upcoming errors priorto a calculation crash. The mathematical operations are then expressed in a sequence ofbasic operations [1].
An example is given in Appendix 1.
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4.1.3.3. Calculation procedure
A combination of the sequential calculation method and the equation-solving methodimproves the calculation’s speed and its reliability. One approach to combine thesemethods is illustrated by Figure 4.
Figure 4: The calculation sequence. Every circle represents a component and the ellipserepresents the whole system.
When the power plant system is initiated by input data and the number of unknownvariables is equal to the number of system equations, the number of unknown variablesin every stream and every component is calculated and stored in two vectors. Thelengths of these vectors are equal to the number of points and components within thesystem. The number of unknown parameters, i.e. parameters that have not received anynumerical values, in each point and component are stored at the vector positionmatching the current point or component number in the flow diagram. Using a programcode, built on the previously described method, makes it possible to start the calculationin one component and repeat the calculation loop until the number of unknowns in thecomponent or in the connected points is not decreasing. This procedure is then repeatedfor the next component in the system. When the last component is calculate~ the TotalNumber of UnKnowns (TNUK) within the system is updated. A comparison betweenthis value and the previously stored TNUK gives a signal to begin a new round ofcalculatio~ if TNUK is changed. Otherwise the calculation is completed and thecalculation results are monitore~ or the unsolved equations are collected in a matrix andsolved simultaneously. It is worth mentioning that the size of this matrix is very small,and thereby very easy to solve [1].
4.1.4. Part-load calculations
Pm-load calculations in HMBPs are generally carried out using a parallel set ofcomponent models, modeling the part-load behavior of the components. These modelsare based on equations that use different values of constants to model different part-loadlevels. Tables or curves, delivering the specific data and constants for each component,are available within the program.
An alternative approach for part-load calculation is local linearization. This method isbased on the assumption that the system parameters vary linearly between two part-loadlevels close to each other. Performing part-load calculations based on local linearizationrequires derivatives of the system equations in respect to parameters that can determineother properties of the system unambiguously. A detailed study of the material propertydata, such as a steam table, shows that there are only two parameters, namely pressureand enthalpy, that can be used to determine other parameters of the working material inthe entire validation range [1]. Representing the derivatives of system equations in
,..! ,,,
21
respect to pressure and enthalpy makes it possible to calculate the changes in materialproperties between two different load levels. However, carrying out a complete massand heat balance calculation requires information about changes in mass flow rate.Therefore, the differentiation of the system equations must be performed in respect topressure, enthalpy and mass flow rate.
Every component is thereby represented by a set of equations for full load calculationsand the differentiated set of the same equations for part-load calculations. To performpart-load calculations, components of the system transfer their set of differentiatedequations to a matrix that is completed by equations representing the part-loadregulation conditions.
Starting from the full load conditions, where all parameters of the entire system areknow necessary changes of pressure, enthalpy and mass flow rate in every point andevery component of the system can be calculated in order to determine the current valueof these three parameters at the new load level. Using the calculation method presentedin section 4.1.3.3, every parameter of the system can be calculated at this new loadlevel. These data are used as the new starting point for the next par-load calculation. Asillustrated in Figure 5, the accuracy of this calculation method is dependent on the stepsize betsveen the load levels. Decreasing the step size increases the number ofcalculations. However, using the previously presented calculation method on a moderncomputer allows decreasing the step size to a level so that the best possible accuracy isachieved within an acceptable amount of time [1].
Parameter
4value
Figure 5: Illustration of the effect of step size on the accuracy of the results, usinglinearization. The error (the gap between approximated value and the real data)increases by increased step size (Ax).
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4.2. Optimization
Processes embody many technical considerations. Some investment and operatingdecisions are dictated by process requirements, regulatory mandates, safety standards,etc. However, many options in equipment selection, plant configuration and operatingpractices remain. The choices made can affect capital and operating costs. Finding theminimum total cost is the ultimate goal.
Generally, optimization of power plant systems aims at optimizing either the plant’slayout or the plant’s parameter values, to maximize the plant’s electrical efficiency.Selecting the best power plant configuration to achieve the highest efficiency is usuallycarried out by “trial and error”, using the knowledge of earlier experiences and systemlimitations. There are also optimization tools based on mathematical programming thatcan search for the “best solution” manipulating the problem variables. These programsrequire definition of the objective fimction and the constraints of the system to find thebest economical solution [35].
The most common optimization technique used in HMBPs is based on an automaticparameter variation tool, usually connected to standard programs for data presentation,e.g. Excel, that performs repeated calculations of the plant’s model, changing oneparameter each time. The calculated values of the objective fimctiom here the electricalefficiency, can be plotted against the studied parameter. The parameter value giving thehighest plant efficiency will be used as the design parameter value.
There are also optimization tools based on exergy analysis, e.g. pinch technology. Pinchtechnology identifies the fimdamental temperature constrain the “pinch” temperature,which thermodynamically limits energy recovery in the system. Identification of thisconstraint makes it possible to establish practical standards for the operating and capitalcosts of energy systems before they are designed or, in the case of existing systems,modified [36].
Pinch technology’s focus is on the heat exchanger network. By defining energy andeconomic targets for the plant, pinch technology optimizes the network by mhimkingthe exergy losses during the heat transfer process.
4.2.1. Parameter study
As mentioned before, a parameter study is the most common optimization procedureadopted in HMBPs. In the early days of HMBP development the parameter study wascarried out manually by the user so that the value of one parameter of the plant wasvaried and the objective Iimctioq usually the plant efficiency, was calculated. The usercompiled the results to generate a curve showing the variation of the objective fimctionby the decision parameter. This process was very time-consuming, containinguncertainties related to the manual treatment of the data. The major reason for manualdata processing was the limitations of the computer hardware and software.
,,
23
Parameter studies in modem HMBPs are carried out by defining the upper and thelower limits of the decision parameters and the step size for parameter variation. Theprogram itself performs repeated calculation loops, and the calculation results can besaved in files or monitored in a standard program like Excel. The user can study theimpact of parameter changes on the plant’s efficiency and select the list of the designparameter values.
4.2.2. Pinch technology
Conceptually, pinch technology is based on identi&ing targets for a process andrecognizing the pinch. First, the best energy and capital petiormance for a given processis predicted. Then, a design which satisfies the targets is invented. The “pinch”temperature thermodynamically limits energy recovery in the system. Knowledge of theprocess limitations or constraints is used during the design stage to ensure that theprojects developed are compatible with the process and its operating procedures.
Studies in pinch technology vary with the objectives, scope of investigation, processcomplexity, and other factors. However, most fall into two broad categories: scopingstudies and detailed analyses.
Scoping studies identi~ opportunities and quantifi the potential for improvement. Theygenerally consider just one operating case and use readily available data. Data gaps orinconsistencies are resolved using engineering judgment or estimates. The extracteddata are interpreted and characterized in pinch technology terms to establish enerW andcapital cost targets. Process changes capable of reducing the targets are identified. Pinchtechnology uses conventional data, basically heat and mass balance information. Thebasic data requirement is a complete and consistent heat and mass balance for theprocess. Utility data and economic data are also required. Data collection and dataextraction are often the most important stages in a pinch study.
To produce a successfid study, pinch technology practitioners must also be skilledengineers who understand the facility’s constraints and needs. Developing pinchtechnology expertise is not a trivial undertaking. Ailer training in pinch technologyconcepts, at least 1-2 years of intensive practice is required to become proficient in theirapplications [36].
Among pinch technology’s most usefid attributes is the targeting capability. Design dataare used to generate targets for energy consumption and capital cost, prior to design.The minimum total cost identifies the design philosophy offering the most favorableoverall economics.
Simple processes may not be significantly altered by pinch technology studies, althoughsuch studies would be correspondingly quick and inexpensive. In systems with only afew heating or cooling duties, the right course of action may seem apparent. However,complex power plant systems can be analyzed systematically by pinch technology.Pinch technology is best applied early, before major design decisions are made, to beable to vary tie plant layout for the best alternative.
24
The most important tools provided by pinch technology for process analyses arecomposite and grand composite curves.
The composite curves clearly present interesting information about the process. Thecomposite curves allow the minimum energy requirements and the overall heat transferarea to be determined as targets. They show the energy target, where the pinch occurs,and where the design of the heat recovery system is likely to be difficult because of thesmall temperature driving force. However, composite curves have a shortcoming. Theyassume that utilities will be used at the temperature extremes of the process. This is notalways desirable. The composite curves do not readily show the ways in which theseobjectives can be achieved. Thus a better tool, the grand composite curve, has beendeveloped for this purpose.
The grand composite curve represents the horizontal separation between compositecurves after these have been temperature-shi fied to allow for the pinch temperaturedifference. The grand composite curve can be used to quickly screen rdternative hotutilities. The appropriate placement principle is based on overall balances. Aquantitative tool is also needed to assess exactly how much heat the process can acceptat exactly what temperature level [36, 37, 38].
4.2.3. Exergy and thermoeconomics
Exergy is a second law concept, fiuthering the goal of more effective ener~ use.Exergy is a property, enabling determination of the usefid work potential of a givenamount of energy at a specified state. Exergy analyses enable the locatio~ cause andmagnitude of waste and loss to be determined. Such information can be used to designnew efficient plants and increase the efficiency of existing plants.
The work output of a system at a specified state is maximized when the final state is thedead state, defined as the thermodynamic and chemical equilibrium of the system andits environment.
The requirement for a specified dead state, is the major disadvantage of the exergyanalyses method. The definition of dead state gives opportunity for arbitmriness andmakes a global comparison of different system solutions difficult. However, exergyanalyses, implemented in more traditional power plant evaluation tools, like heat andmass balance programs, make a general optimization process easier.
Additionally, exergy is important because it provides the basis for thermoeconomicanalysis of power plants. Thermoeconomics combines exergy analysis and economicprinciples to provide the system designer with information crucial to the design andoperation of a cost-effective system. Thermoeconomics can be considered as exergy-aided cost minimization. Evaluating the costs for exergy destruction and loss is veryuseful for improving the cost effectiveness of the system. The objective of athermoeconomic analysis might be to calculate the costs of each product generated by asystem, to understand the cost formation process, to optimize specific variables in asingle component, or to optimize the overall system [3,5, 39].
. ..
: ,,
25
4.2.4. Condition monitoring and artiilcial neural networks
Low maintenance costs, high availability and reliability, as well as increased powerplant efficiency, are some of the greatest ambitions for the independent power producersin the deregulated electricity market. Increased power plant efficiency is achieved byimplementation of more complex and highly loaded components. To prevent forcedoutage and damage to these expensive components, and to provide the system with faultdiagnostics, a surveillance system, such as a Condition Monitoring System (CMS) andan Artificial Neural Network (ANN), can be utilized [40].
The condition monitoring system is based on thermodynamic models of the power plantcomponents. The CMS uses heat and mass balance programs and componentcharacteristics, like compressor maps, if available. Online measured data are used asinput to the HMBPs to evaluate the plant performance and component degradation, andto perform fault diagnostics. To complete the analysis, it is, however, necessary to makeassumptions about parameters that are not measured, such as cooling flows. Thecalculation results are compared to the measured data, and discrepancies betweencurrent and expected values are used to diagnose the system performance, and identififaults orland component degradation.
Since the CMS is utilizing measured data, sensor validation for detection of faultysensors should be petionned prior to the calculation, to insure that the sensors arefunctioning. Sensor validation in the CMS is, however, restricted to detection of faultyor disconnected sensors, indicated by measured signals equal to zero. The possibilitiesfor detection of sensor degradation, and data recovery are limited.
Dynamic modeling and simulation in the CMS are not possible, since it takes a longtime for a physical model based dynamic HMBP to perform a dynamic simulation.Therefore, online dynamic modeling, based on real time data, cannot be performed inthe CMS.
The ANN provides a generic functional relationship between the system’s input andoutput [41, 42]. A collection of system da~ representing as many operational andenvironmental conditions as possible, is used for generation of a model that captures thespecific functional relationship. The accuracy of the ANN model is apparentlyproportional to the amount and the range of the data. The model comprises of two ormore weighting matrices and a set of mathematical equations that can be programmedin any programming language. The final model can be executed without access to theANN training program. Results of studies concerning simple gas turbine andevaporative gas turbine static modeling by using ANNs are presented in papers 7 and 8.
The static ANN model is also able to generate performance maps for the studied system,utilizing nodnear interpolation and extrapolation. Performance maps can be used forprediction of system peri?ormance at a wide range of operational conditions.
In static networks the input signal flow is directed to the output with no informationfeedback path; while in dynamic networks, the output of each layer is fed back asadditional input. Real time measured data can be used in dynamic networks for
26
generation of a dynamic model of the system. One of the advantages of a dynamicmodel is its capability to predict faulty conditions as they are approaching, since it canrecognize changes toward a pre-defined fault. As the sensor is degrading, its readingdeviates more and more from the expected value. The dynamic model registers changesin sensor readings continuously and is thereby also able to detect the upcoming sensordegradation.
A fault diagnostic ANN may also be trained to tag existing patterns of inputs andoutputs to a particular healthy or faulty condition. The fault diagnostic ANN recognizesthe pattern of current system input and output, which follows a message indicating ahealthy system if the current pattern is matching the healthy tag. In the same manner,the ANN can recognize a certain pattern that is pre-defined as a specific fault. In thiscase an error message is generate~ informing the operator about the fault. When anunknown system fault occurs, the tool generates an error message, that ANN is not ableto identi& the fault, but cm however, be trained to learn on line and recognize the newfaulty pattern the next time it occurs.
The ANN based sensor validation is more flexible than the validation model pefiormedby the CMS. The ANN based sensor validation is capable of including the sensors’degradation and also facilitates data recove~, in the sense that missing data can berebuilt using the relationship behveen the existing data set. Each operational condition isrelated to a specific pattern. If the reading of a sensor is slightly deviating ilom theexpected pattern, while the rest of the readings are corresponding to it, a sensordegradation is detected and can be reported.
Since the surveillance tools mentioned above provide possibilities for remotemonitoring and control of modem distributed power production units, it is most likelythat their development will bean interesting research area in the fhture.
.,
27
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5. Discussion and future work
As shown in previous chapters, there are different categories of commercial programsfor analysis and optimization of power plants. Each category, in its turn, containsseveral alternatives. This chapter includes general conclusions for each category, as wellas a discussion concerning the development trends for new tools, needed to fidfil therequirements of the modern power plant systems.
The HMBPs are the primary choice for pre-design studies, and they are also the majorpart of optimization tools, such as pinch programs, and performance analysis tools, likecondition monitoring systems. There are several types of HMBPs, developed fordifferent purposes. One major group is composed of HMBPs developed for modelingand analysis of chemical processes. These programs are usually used by processindustries. There are also HMBPs specially developed for power plant analysis. Thesetools can be divided into two categories, one utilizing an open program environment,where all component models are available for completion and/or modification and theother category, where the main codes are not available, but the user can add modelsprogrammed in a specific progr amming language, e.g. fortran.
Programs tailored for power plant analysis are also developed to fulfil differentrequirements. Some of them contain more accurate part-load calculation models; othersare adjusted to specific needs, like modeling district heating systems; and some areeasily programmable, giving the users opportunity to adjust the program to veryspecialized applications. Commercial heat and mass balance programs usually do notcontain an accurate model for a gas turbine cooling system. Accurate modeling of thecooling system is an important issue, specially for modern gas turbines that utilize alarge amount of cooling air. Since the choice of the tool is determined by its applicationarea and the specific needs of the system, it is not possible to provide general adviceconcerning the selection of an appropriate tool.
Pinch programs are usefil tools, specially for optimization of Heat Exchanger Networks(HENs). These programs require thermodynamic data for the streams of the HEN-system to be studie~ prior to the analysis. Therefore, stream data must be supplied by aHMBP. Pinch programs show to advantage in sophisticated HEN-systems, and complexpower plant systems. The major differences among available pinch programs are theamount of infonnatiou and the ability to facilitate modeling and data presentation in anintegrated graphical environmen~ rather than their mathematical construction.
Exergy analysis is a good complement to the energy analysis, performed in the HMBPs.It can be implemented in most of the existing HMBPs, since it requires entropy data thatis already available in such programs. Exergy analysis generates information aboutlosses in every component and takes into account the quality of the energy which is notconsidered in the ener~ analysis. Putting a price label on the losses of the systemmakes an economical optimization possible. The combination of the exergetic and theeconomic optimization is treated in tools based on the thermoeconornics. HMBPs canbe completed by thermoeconomic analysis after the exergy analysis has beenimplemented.
29
The development of modem engines and power plants in combination with the changesin the electricity market requires new tools, tailored to fidfil the needs of the powerproducers today. The economic demands for profitability of the power generationsystems requires high efficiency, availability and reliability, as well as low maintenancecosts. Condition based maintenance has become a key phrase requiring continuouscondition monitoring to diagnose the upcoming engine faults. Two categories of toolshave been developed for this purpose. One is based on physical component models andHMBPs, called condition monitoring systems, and the other is based on the statisticalrelationship between the system’s input and output, called artificial neural nehvorks.Both categories utilize measured data, so the final results are highly dependent on thecorrectness of the measured data.
Since the most common failure has been shown to be faulty sensors, a primary measureensuring that sensors are functioning, a sensor validation, should be taken. In the case ofCMS, the sensors’ readings are transferred to the calculation process as long as theirvalues are different from zero. A zero reading indicates that the sensor is faulty. Sensorvalidation applied in the CMS, does not give any possibility of taking the degradation ofthe sensors into account. However, the ANN can recognize sensor degradation when themeasured data at a specific operational condition is slightly different from an expectedvalue. It can also perform data recovery when a sensor is faulty, since there is a certainrelationship between the input and output of the system. This relationship can be usedfor generation of an approximate value for the missing parameter.
An additional advantage of the CMS and the ANN is the capability of fault diagnostics.In the CMS, a group of measured data is used as input to the HMBP to calculate theexpected performance data for the system at the current operational condition. If theexpected data differs from the measured values, a fault is indicated, and expertise isneeded to analyze the results and find the source of the fault. The ANN based systemsrecognize a certain relationship between the system’s input and output at differentoperational conditions. Their accuracy is directly proportional to the number of data setsrepresenting these conditions. It is also possible to train ANN models to recognize acertain combination of input and output as a specific system fault. The faulty conditionresults in an error message directly addressing this fault. New faults, unknown for thesystem, can also be added to the register of known faults by training the system.
The application of ANNs on a power plant system provides also system performancemaps, and static or dynamic models of the system. A system performance map, that is aresult of static modeling, can cover possible operational conditions, giving anopportunity to predict system pefiormance at specific conditions. Using dynamicmodeling that provides the system with data from earlier time steps, makes it possible torecognize changes in a certain direction toward a specific fault. This recognition can beused to generate a warning signal predicting an upcoming fault.
The mathematical model generated by the ANN can be presented as a set of equationsand two or more weighting matrices that are specific for the studied engine. Theequations and the weighting matrices can be implemented using any programminglanguage to generate the ANN model of the studied system. It should be mentioned thatthe ANN tool is applicable only on existing systems.
30
,
It would be desirable, as a fiture work, to include calculation procedures for evaluationof the investment costs for every power plant modeled in the HMBPs. This kind ofanalysis evidently requires manufacturer data for calculation of the component costs.Other factors, important for the future work, are the implementation of the life cycleassessment and the prediction of the remaining life time, based on the collected dataconcerning components’ degradation.
Table 1: A comparison of different toolsPrograms HMBP Pinch Exergy Thermo- CMS ANN
Program analysis economics
Component Yes No Yes No Yes YesanalysisSystem analysis Yes Yes Yes Yes Yes YesOptimization Yes Yes Yes Yes Yes YesOperation/ Yes No No No Yes YesSurveillance (limited)Pre-design Yes Yes Yes Yes No No
Table 1 is a general overview of different categories of tools and should not beconsidered as a guide for selection of tools. For instance, comparison between CMS andANN tools using the table can be deceptive, resulting in the false conclusion that thesetools are equivalent.
31 \ ..
32
6. Conclusions
The knowledge about methods and tools for analysis and optimization of thermal powerplants at the pre-design and the operational phases has been improved.
An in-house heat and mass balance program, suitable for thermal power plants, with aunique method to handle the part-load calculations has been developed and tested.
The technical development in the field of power plant technology, which has to behandled by the studied tools, is identified and discussed.
In order to illuminate advantages and shortcomings of different commercial tools, anumber of concepts, based on both conventional and new techniques, have beenanalyzed.
33
i’
34
7. Summary of papers
In this section, eight papers containing studies of system modeling and analysis arepresented. The primary aim of the studies was to compare capabilities and shortcomingsof the tools and identifi the most favorable application areas for each of them. Therewas also the aim to investigate new power plant configurations and repowering optionsfor I%rther improvement of system performance. The studies have been performed usingdifferent commercial and in-house computer-aided tools, namely, heat and mass balanceprograms: IPSEpro, ASPEN Plus, PROSm pinch programs: Super Target, and Pro-Pi;and an artificial neural network tool, developed at Newcastle University by Ph. D.Ehsan Mesbahi.
IPSEpro is an equation oriented HMBP, providing an open code environment wherethe user can modify existing component models or create user defined components.New components become an integrated part of the program, as they are compiled.
ASPEN Plus is a sequential modular program, containing a large database for materialproperties. This program is the most common tool for modeling and analysis ofchemical processes. In power plant analysis, ASPEN Plus is very useil.d for modelingthe gasification process of solid fiels, as well as biomass.
PROSIA4 is also a sequential modular HMBP with capability for modeling, among othertypes of power plants, biomass fueled Combined Heat and Power (CHP) systems.
Super Target (ST) is a user-iliendly pinch program with the facilities for heat exchangernetwork analysis. The other previously mentioned pinch program, Pro-Pi, is similar tothe ST concerning the mathematical construction but its graphical capabilities are not assophisticated as those of ST.
The ANN tool was used for modeling the simple gas turbine and the evaporative gasturbine, operating at the gas turbine laboratory of the Department of Heat and PowerEngineering in Lun~ Sweden. Models generated by the ANN tool can be executedwithout the ANN sofhvare. The systems’ specific weighting matrices and the generalmathematical equation can be implemented in any programming language. Furthersystem analysis can be performed by execution of the static models.
Studies concerning gas fueled SI-engines are presented in the papers 3 and 5. Thethermodynamic properties of the engine flows were not modeled, but provided by themanufacturer. It should be mentioned that heat and mass balance programs, designed forpower plant studies, generally do not contain models for SI-engines. However, in heatand mass balance programs providing an open model environment modeling of anytype of the components is possible.
The accuracy of calculated results are primarily affected by simplifications that areimplemented during the modeling process. In heat and mass balance calculations, thematerial properties are also crucial for the accuracy of calculated results. Since previousstudies [2], show that the thermodynamic models used in different HMBPs are similar,and that the material data banks generate comparable values for material properties at
35
same physical conditions, the search for differences between HMBPs should be focusedon:
- the numerical stability that is a consequence of the mathematical calculationmethod used in the program,
- the variety and the number of the component models available,- the user-fiiendliness and capabilities for data presentation and monitoring,
rather than differences in numerical values.
The papers presented in this thesis contain numerical results, given by more than onedecimal. The number of decimals does not reflect the accuracy of calculated results.However, since the superiority of different layouts utilizing same models and propertydata is evaluated by comparison of the numerical values, the decimals given here makesuch a comparison easier.
Paper 1: Applying Pinch Method and Exergy Analysis to a BIO-IGH4TPower Plant.
This paper presents results from a study where the pinch method and exergy analysiswere applied to optimize a biomass-fieled integrated gasification humid air turbine.Two different pinch programs, Pro-Pi and Super Targe~ were used. Comparison ofcalculation results showed good agreement. A simple method to overcome pinchprogram difficulties in modeling streams that experience combined heat and masstransfer, is also presented. Modeling and calculation of the power plant system wereperformed by the heat and mass balance program PROSIM.
It is shown that the combination of the pinch method and exergy analysis makes iteasier to optimize the system. The pinch method provides the user with usefidinformation such as composite and grand composite curves. The curves give increasedunderstanding of the heat transfer and the exergy losses occurring within the system.The basic rules of exergy analysis can be used to identi~ measures necessary for theimprovement of the system.
The paper was mainly written by the author. Kent Johansso~ from Sycon, contributedto this paper by performing calculations in the pinch program Pro-Pi and participating inthe comparison of results from Pro-Pi and Super Target.
Paper 2: A computational investigation of a combined cycle using an SI-engine, aRankine bottoming cycle and afuel cell.
Results from theoretical studies, concerning hybrid plants containing a Fuel Cell (FC)and a Gas Turbine (GT), have been presented in several publications. All these studiesshow that a hybrid plant with FC, GT, and steam bottoming cycles can achieveelectrical efficiencies in the order of 75’Yo.This paper presents the results of a theoreticalstudy of a hybrid plant consisting of a Solid Oxide Fuel Cell (SOFC), a spark ignitiongas fueled engine (lWrtsiki 18V34SG), and a steam bottoming cycle. The SI-enginedata were provided by the company W&-tsiEi.
36
The improvement of modem turbo-chargers makes it possible to provide a fuel cell withair, waste gated from the gas engine’s flow surplus. The FC can hereby be pressurizedby the air flow, and its efficiency and power output will be increased. Pressurizing a FCto 3 bar, using a compressor with 80% isentropic efficiency, consumes about 25% of theelectrical output of the FC. The synergetic effect of the integration of the FC and the SI-engine results in considerably increased efficiency and power output for the hybridplant.
The hybrid plant has been modeled and simulated in the heat and mass balance programlPSEpro, by varying the amount of airflow flom the turbo-charger and optimizing thepressure level of the bottoming cycle. It is shown that the integration of a SOFC and anexisting gas engine results in increased electrical efficiency. The magnitude of thisefficiency increase depends on the power output (i.e. size) of the SOFC.
The paper was mainly written by the author. The co-author Thomas Hiigghmd, fromW5rtsi15, suggested the method for integration of the fiel cell into the combined cycle.
Paper 3: Increasing thermal eficiency of a PFBCpowerpiant using a natural gasfueled gas turbine.
The highest allowable temperature in a Pressurized Fluidized Bed Combined cycle(PFBC) plant is limited to 850° C for two reasons, to utilize an uncoole~ robust gasturbine, and to avoiding fuel ashes from melting. This temperature limits maximumachievable electrical efficiency of the PFBC-plant. Paper 2 presents a study wherepossibilities to brake through the efficiency limit by completion of the PFBC-plant witha modem natural gas fieled gas turbine have been investigated. Standard gas turbinesand Heat Recovery Steam Generators (HRSG) of different sizes were used to model ahybrid plant in the heat and mass balance program IPSEpro. Plant data for the PFBC-plant was provided by the company ABB STAL (ALSTOM). The steam generated inthe HRSG was mixed with reheat steam from the PFBC-plant and expanded in thecommon part of the hybrid plan~ i.e. the intermediate- and low-pressure steam turbine.Calculation results show that the efficiency of the hybrid plant is higher than the stand-alone PFBC-plant’s. The marginal efficiency, defined as the ratio behveen the increasedelectrical effect and the increased fuel input, for the hybrid plan~ based on the GTX1OOgas turbine without post combustion, is of the same magnitude as the efficiency of thestandard combined cycle based on the same gas turbine. The high marginal efficiencyfor the hybrid plant is achieved using a simple HRSG with one pressure level, while thestandard combined cycle is utilizes a more complex HRSG. This is the most importantsynergetic effect of the hybrid plant. Additional synergy effects of this concept areincreased availability, due to the presence of two independent power generationsystems, reduced COZ emission, due to the lower carbon/hydrogen ratio of the naturalgas, as well as reduced investment costs, due to the integration into the already existing
steam plant, e.g. steam turbine, condenser and auxiliary machinery.
This paper is result of the cooperation with the colleagues horn former ABB STAL. Thepaper was mainly written by the author. The co-authors contributed with the real plantdata and participated in analysis of the calculation results.
37
Paper 4: Integration of biomass-ftleled power plants and SI-eng”nes, a method forincreasing power outputfiom existing plants.
This paper presents results of a study performed to increase the power output of existingbiomass-fueled combined heat and power plants through extension of the plant bynatural gas fieled spark ignition engines. Thermodynamic and economic data for the SI-engine were provided by the company W5rtsilii NSD.
The Swedish government has decided to phase out nuclear power by the year 2010.Bearing in mind that about 46% of the electrical power in Sweden is generated bynuclear power plants, it is easy to understand the proportions of this issue. It isimportant that the replacement of the electricity from nuclear plants does not increasethe emission burden on the environment. Therefore this study was carried out to modeltypical biomass-fueled power plants in the heat and mass balance program, extend themby a specific SI-engine, and to investigate the synergy effects and investment costs ofthe integrated system.
The gas fieled SI-engine used in this study is a Wiirtsi15 16V34SG. Calculation resultsshow that the total power output of the integrated system is larger than the sum of theelectricity produced separately by the SI-engine and the biomass- fheled combined heatand power plant. It is shown that in the hybrid pkm~ synergy can be achieved for bothelectrical power output and the electrical efficiency. Further advantages of the hybridplant are short construction time and the relatively low investment cost per kW installedelectrical power. Achieving the highest possible electrical efficiency requires differentways of integration of the CHP plants and the SI-engines, and is dependent on, amongother parameters, the size and the coupling schemes of the CHP plants.
The marginal efficiency, defined as the ratio between the increased electrical effect andthe increased fiel inpu~ of the integrated systems is in the order of 50%. An economicalinvestigation shows that the current electricity price in Sweden is too low (24$fMWh) tomotivate the repowering suggested here. However, it is most likely that the electricityprice is going to increase afier a nuclear power phase-out. The studied concepts becomeeconomically interesting for the electricity prices higher thin 36$lMWh.
This paper was mainly written by the author. The author chose the plants to be analyzedand provided necessary data. Jarmo Mauno from Wiirtsilli perllormed the heat and massbalance calculations, Andreas Magnusson carried out the economical analysis and theco-author, Tord Torisson, participated in analysis of the results and conclusions.
Paper 5: Air bottoming cycle for Gas Turbines.
This paper presents results of a study where an Air Bottoming Cycle (ABC) has beenmodeled in the heat and mass balance program PROSIM, and its potential to be analternative for electricity production in the Scandinavian market has been investigated.Several layouts for combined heat and power plants have been generated. The effect ofthe gas turbine’s size on the plant efficiency and economy has also been investigated.Calculation results show that the ABC-cycle achieves an electrical efficiency in theorder of 46°/0 and the fiel utilization in CHP-plants is more than 80°/0.It is also shown
,.
38
that emission of the greenhouse gas COZ is reduced by 20% compared to conventionalCHP-plants. The economical investigation shows that the production price forelectricity generated by the ABC-cycle (14 US$/MW) is higher than the price of thepower available at the market.
This paper was mainly written by Jaime Arriagada. The author contributed to analysisand evaluation of the studied systems and the calculated results.
Paper 6: A Computational Investigation of a Biomass-Fueled Integrated Gasz~cationCascaded Humid Air Turbine, Bio-IGClZ4Z
This paper presents results of a study where a power plant containing a cascaded humidair turbine, an atmospheric gasification unit, and an exhaust gas dryer for biomass wasmodeled and simulated in the heat and mass balance program ASPEN Plus. Calculationresults for the natural gas fueled CHAT-cycle correspond to published results. Theimpact of replacement of natural gas by gasified biomass has also been investigated. Itis shown that the electrical efficiency for the biomass-fieled CHAT-cycle is higher than40% at zero COZemissions.
This paper, which summarize results of Andre Hildebrandt’s final thesis was mainlywritten by the author. Hildebrant petiormed calculations and modeling of the studiedsystem under authors supervision.
Paper 7: A Novel Correction Technique for Simple Gas Turbine Parameters.
Data normalization for gas turbines is necessary for comparison of test data collected atvarious environmental conditions. This paper presents results from a study where asimple gas turbine (VOLVO, VT600) has been modeled by an artificial neural network,using measured data at various environmental conditions. Engine pefiormance mapscontaining pefiormance data at ISO-conditions have been generated by nonlinearinterpolation and extrapolation. Comparison of the normalized/experimental daqresults provided by thermodynamic models using heat and mass balance programs andresults generated by the Artificial Neural Network model shows a high level ofconsistency.
This study shows that artificial neural networks are useful tools for generation ofaccurate power plant models and performance maps for specific engines. It also showsthat data normalization can be carried out easily and accurately by using performancemaps. The engine performance at conditions other than the 1S0 can also be predicted.
The paper, was mainly written by the author and Ehsan Mesbahi, from the University ofNewcastle. The author and Mesbahi performed the modeling and analysis of the studiedphm~ using an ANN tool. The ANN progr amming was pefiormed by Mesbahi. Severalcolleagues at the department of Heat & Power Engineering in Lund also contributed tothis paper. Pernilla Olausson and Jairne Arriagada performed heat and mass balancecalculations in PROSIM and IPSEpro, respectively. Torbjom Lindquist contributed bymeasurement data, and Tord Torisson participated in analysis of the results.
,.
39
Paper 8: A Unique Correction Technique for Evaporative Gas Turbine (EvGT)Parameters.
In this paper results from a study concerning data normalization for the evaporative gasturbine are presented. The data normalization was pefiormed by using a modelgenemted by an artificial neural network. The EvGT works with a mixture of air andwater steam as working medium, and is thereby strongly affected by the changes in theenvironmental conditions that affect the water content of the working fluid. Thenormalization process for the EvGT becomes very difficult since the properties of theworking fluid are changing continuously. To achieve high accuracy during the datanormalization, it is necessary to take these changes into account. Measurement datafrom the EvGT plant in Lund, Sweden, have been used for the generation of amathematical model by an ANN. Performance maps generated by the ANN have beenused successfully for data normalization of the EvGT. This study shows that the ANN iscapable of capturing the system behavior and modeling the system. The ANN modelcan be used to generate performance maps, providing prediction of the systemperformance at a wide range of operational conditions.
The paper, was mainly written by the author and Ehsan Mesbahi, from the Universit y ofNewcastle. The author and Mesbahi performed the modeling and analysis of the studiedplant, using an ANN tool. The ANN pro -g was perfo~ed bY Mesb*i” The co-authors Torbjom Lindquist and Tord Torisson contributed by measurement data, andanalysis of the results respectively.
40
Appendix 1
The possibility to choose the input data freely from a list, containing all system data,increases the user-friendliness of the analysis tools considerably. The tool utilizing freeinput data, presented in section 4.1.3, requires that the system equations are presented asa sequence of basic mathematical operations (e.g. addition, subtractio~ division, etc.).This tool also performs control of the calculation processes prior to the execution,preventing forbidden mathematical operations, like division by zero, which generates anundesired interrupt. The example below shows the programming technique used in theanalysis tool, presented in the section 4.1.
Example:The energy balance for a component presented in Figure 1 can be written as:
ml. hl+-m2. h2=mj. h3 (A)
Figure 4: Component model with in- and out-flows
The energy balance (A) contains six variables. Calculating any one of them, knowingthe other five, demands the following mathematical presentation in the program code:
Rewriting equation (A):ml = (m3. h3 - ml. h&il
hl = (m3. h3- m2. h#ml
m2 = (m3. h3 - ml. hJh2
h2 = (m3. h3 - ml. hl)/ml
mj = (ml . hl + ml. hJh3
h3 = (ml. hl +-m~. hj/m3
Apparently, this should result in an unacceptably large program code, demanding lots ofman hours and slowing down the calculation speed. To overcome these problems, everycalculation step can be presented as a combination of basic mathematical operations. Anexample for such a basic operation is the DMSION.
, .,
To petiorm the division “C= A /B”, allowing free choice of daq operational control,and error tracing back to the equation where the error has appeare~ the followingapproach is required:
41
The division” C = Al B” can be rewritten as:A=B. C and B=AIC
covering all possible ways to present the basic operation, DIVISION. Independent ofwhich two parameters are known, the third one can be calculated in a calculationprocedure containing all these three expressions.
A difficulty associated with the free choice of input data is that the user can fix toomany parameters prior to the calculation. The variable properties presented in chapter 4can be utilized to prevent overwriting the user-defined values by calculated ones, in thecase that the difference between those two is greater than a pre-defined tolerance. If thedifference exceeds the tolerance, an error message is generated. The procedure CHECKused in the program code below, performs the control prior to the assignment of anumerical value to a variable (see the reference [ l]).
Procedure Divzkiou (var A,B, C:variabeltype);var D:variabeltype;begin {C=AIB}
if (A.knownOunknown) and (B.knownounknown) then{If A and B have received numerical values}begin
if B.valueOO then{IfB’s value is not equal to zero}begin
D.knowm= calculated;D.value:=A.value/B value;check(D,C);{The procedure CHECK generates an error message if there are any conflicts}
endelse
Message(’ Division by Zero !’);{In the case that B’s value is equal to zero, the error message is shown on thescreen)
end{The rest of the code handles the rewritten equations}else if (C.knownO unknown) and (13.knowno unknown) thenbegin
D.knowrr= calculated;D.value:=C.value*B value;check(D,A);
endelse if (C.knownO unknown) and (A.knowno unknown) thenbegin
if C.valueOO thenbegin
D.knowm= calculated;D.value:=A.value/C.value;check(D,B);
end
42
elseMessage(’ Division by Zero !’);
end;end; {End of the program code}
Using basic mathematical operations in the form presented above, makes it possible togenerate the following program code to calculate the energy equation
ml=(m3. h3-m2. h2)/hl
Multiplication (m3, h3, al);{Replacing the: al=m3.h3, maintaining free choice of data}Multiplication (n12,h2, a2);Subtraction (al, a2, a3);Division (a3, hl, a4);Check (ml, a4);
,...
The enthalpies, used in the energy balance equation, are supplied by material propertydatabases within the program.
43
Appendix 2
Local linearization
Heat and mass balance programs (HMBPs) usually contain component models for part-Ioad calculations, parallel to the models used for the design calculations. The part-loadmodels utilize, besides mathematical equations, tables and curves describing thecomponents’ behavior at part-load. Generally, part-load calculations are pefiormed byiterative calculation procedures, with the uncertain convergence as a consequence.
An alternative approach for part-load crdculation is local linearization. This method isbased on the assumption that the system parameters vary linearly in between two part-load levels close to each other. A part-load calculation based on local linearizationrequires differentiation of the system equations. Selection of differentiation parametersis an important task that should lead to an unambiguous determination of the remainingsystem parameters at any operational condition. Water steam and exhaust gases are themost common working media for the thermal power plants. A detailed study of thematerial properties in the steam table shows that there are only two parameters, namelypressure and enthalpy, that can be used to determine the remaining parameters of theworking medium in the entire validation range [1].
Equation (1) and (2) present differentiation of the temperature and entropy with respectto pressure and enthalpy
AT= (dT/dp). Ap + (dT/dh)- Ah (1)
As= (ds/dp). @ + (ds/dh). Ah (2)
The temperature and the heat capacity of exhaust gases, are fictions of enthalpy only,since the entropy is a fanction of pressure and enthalpy, i.e.
T= ffi), Cp= ffi), S=f(P,h)
The differentiation takes the following form:
AT= (dT/dh). Ah
AC, = (dCP/dh).Ah
As= (ds/dh). Ah
Further rewriting of equations 3,4, and 5 gives:
(3)
(4)
(5)
L
dT/dh = l/CP
ds/dT = CP/T
ds/dh = l/T
45
(6)
(7)
(8)
Entropy changes, caused by changes in pressure, can be presented as follows:
R.ln(pO/p)= R.(lnpO-lnp)
R in equation 9 is the gas constant.
The differentiation results ix
b= (ds/dh).Ah - (R/p). Ap
Linearization of the equations, modeling the “STEAM TURBINEK
(9)
(lo)
The example below presents the application of the linearization technique to the steamturbine.
The total shaft power of a steam turbine can be noted as:
P=iz. (bin-hOU,) (11)
The isentropic efficiency of the steam turbine is defined as:
q,= (hin– hO.r-rdO)/thin - ‘i) (12)
where the index (i) denotes the isentropic properties, (in) the property at the turbineinlet, and (out) at the turbine outlet.
Stodola’s equation (13) is needed to perform part-load calculations for the steamturbine:
m A4w pinIi&r
‘h
Pin_O “‘in_O_——
~ - m. pin_o Pin “‘in ~((n+l)ln)
Pow-o
Pin_O
where MW is the molecular weigh~ m is the steam flow, v is the volumetric flow and(n+l)/n is the polytropic exponent. Indexes in and out, denote the properties at turbineinlet and outlet, and index W“, refers to the design condition.
(13)
46
A simplified version of equation 13, presented below, is usually used for part-loadcalculations:
r 2
J-L
1 Pout
lh Pill Pin—=—2
~zo Pin-o
[1
Po”I_o1- —Pin_o
The linearized model of the steam turbine contains the following equations:
Rewriting equation 14:
P: = P:., +Kz “rn*
(14)
(15)
where K is:
Differentiating (15) gives:
The entropy is unchanged during an isentropic expansion, i.e.:
S,n =s, that gives: Asin= ‘i (17)
Differentiation gives:
A.stn= (ds/@)p,n,h,n“@in+ (ds/dk)~,n,h,n “‘ii. (18)
As, = (ds/dP)POU,,ii-~,n + (ds/dh)~ou,.h, “‘ii (19)
Equations (17), (18) and (19) @~
(ds/d’)p,n,h,n‘&,. +(ds/dh)p,n,hin“~,. = (ds/c?P)pOw,fii“@in ‘(ds/dh)Pou,,hi “% (20)
i,:,.., ,,
Rewriting equation (12) gives:
h, = h,. “(1–n,)+how/~i (21)
47
Differentiating (21) gives:
Ahi= Ahin“ (1–l/??i)+ Aho”,/qi (22)
Equations (20) and (22) give:
(ds/dP)pin,/Iin“@in‘(ds/dh)pin,/Iin‘“in =(ds/d~)pou,,/Ii‘&in+ (ds/dh)pou,,hi“(”i. -(l-l/qi)+ ‘..f/qi)
(23)
The mass balance equation for the steam turbine and its differentiation give:
hi” = Li~u,* Alilin= Ailou, (24)
Equations (16), (23) and (24) with the following notation give the linearized matrixbelow, representing the steam turbine:
‘4= (Wldpin,b,nB = (~S/@)pin,~,n– (ds/dh)pou,,hi“(1- Wi )
c= –(w@pou,,hi
D = -(~s/cJ~)POu,,hi “lh?i
4Pin
[
AB o CDOAhin
~n
[]
&linO –K2-rn ‘pOu, O 0
00 1 0 0 –14P..
Ahou,
Alilou
(25)
To perform part-load calculations, the system matrix, containing all componentmatrices, is to be solved. To calculate the system matrix, the design data (at 100% load)are used as the start point. When the step size to the next part-load is determined (e.g.step size equal to 1’%gives 99’% as the next load level), changes in pressure (Ap),
enthalpy (M) and mass flow rate (M), needed to achieve the next part-load, are
calculated for every point within the power plant system. Decreased step size increasesthe accuracy of the calculation results. Knowing the magnitude of tie changes gives thepossibility of calculating the cun-ent values of pressure, enthalpy and mass flow rate forthis part-load level. Calculation of the remaining parameters of the system is thenperformed, using the method presented in appendix 1. The whole procedure is repeated,using data fi-om the previous load level as the start point, until the desired part-loadlevel is achieved.
48
Results of part-load calculations, covering the entire operational range of an existingsystem, can be stored and presented as curves and tables, making new calculationsunnecessary.
: ...
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49
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>.,“ ,,
,, ,,
List of Papers
1. Assadi M., Johansson K. B., Applying Pinch Method and Exergy Analysis to a BIO-IGIZ4T Power Plant, 2ndConference on Process Integration, Modelling and Optimisationfor Energy Saving and Pollution Reduction, PRES’99, pp. 139-144, Published byHungerian Chemical Society, BudapesC Hungary, May31 - June 2, 1999.
2. Assadi M., H5gglund T., A computational investigation of a combined cycle using an SI-enghze, a Rankine bottoming cycle and afuel cell, European Fuel cell news, Newsletter ofthe European Fuel Cell Group, Ltd., Volume 6, Number 2, pp. 5-7, July 1999.
3. Assadi M., Jansson S. A., Blomstedt M., Increasing thermal ej?ciency of a PFBC powerplant using a natural gas fueled gas turbine, The First Iutemationa Symposium onComputer Aided Process Engineering, ISCAPE 2000, Cartagena de india, Colombia,January 24-28,2000.
4. Assadi M., Torisson T., Integration of biomass-fueled power plants and SI-engines, amethod for increasing power output from existing plants, 4fi International Conference ofIranian Society of Mechanical Engineers, ISME 2000, pp. 409-412, Telmq Iran, May 16-19,2000.
5. Arriagada J., Assadi M., Air bottoming cycle for Gas Turbines, 4ti InternationalConference of Iranian Society of Mechanical Engineers, ISME 2000, pp. 447-454,Tehran, IrruL May 16-19,2000.
6. Assadi M., Hildebrandt A., A Computational Investigation of a Biomass FueledIntegrated Gasljication Cascaded Humid Air Turbine, Bio-IGCH4T, to be presented at14ti International Congress of Chemical Engineering, QUBEC 2000, Sao Paulo, Brazil,September 24-27,2000.
7. Assadi M., Mesbahi E., Torisson T., Lindquist T., Arriagada J., Olausson P., A NovelCorrection Technique for Simple Gas Turbine Parameters, submitted to ASMETURBOEXPO 2001, New Orleans, USA.
8. Mesbahi E., Assadi M., Torisson T., Lindquist T., A Unique Correction Technique forEvaporative Gas Turbine (EvGT) Parameters, submitted to ASME TURBOEXPO 2001,New Orleans, USA.
55
