Development of Basic Engineering Modules for Prospective Estimations of the Material Flow and Energy...

Ghent, 10 June 2013

Authors

Steven De Meester, Jo Dewulf (UGent)

Lex Roes, Martin Patel (UU)

Stefanie Hellweg (ETH)

Prospective Sustainability Assessment of Technologies: Development of Basic Engineering modules for prospective estimations of the material flows and energy requirements

Report prepared within the EC 7th framework project

Project no: 227078

Project acronym: PROSUITE

Project title: Development and application of a standardized methodology for the

PROspective SUstaInability assessment of TEchnologies

Start date project: 1 November 2009

Duration: 4 years

Deliverable No: Interim Deliverable 1.2

Date due: Month 24

Date submitted: Month 24 (1st version)

Date submitted: Month 43 (revised)

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For more information on this report please contact:

Ghent University, Department of Sustainable organic Chemistry and Technology, EnVOC

Coupure Links 653

B-9000 Ghent

Belgium

www.envoc.ugent.be

http://www.envoc.ugent.be/

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Executive summary

After determining the goal and scope of a study, the starting point of a sustainability assessment is

to compile reliable data inventory. Especially in prospective assessments, which is the goal of the

PROSUITE project, doing so is not always possible as the technology might not be implemented and

data is not readily available. The goal of this report is therefore to develop basic engineering

modules that allow the estimation of material and energy flows for process flow diagrams of future

technologies. For this purpose, engineering modules were constructed for 25 Basic Unit Operations:

3 reaction types (chemical reactions, incineration for heat and power and fermentation), 7

separation processes (evaporation, distillation, filtration, sedimenting centrifuges, electrostatic

precipitation, electrodialysis and pressure swing adsorption), 9 physical mechanical processes

(mechanical compression – single stage, mechanical compression – multi stage, pumping

incompressible fluids, pumping incompressible fluids through packings, agitation and mixing of

liquids and suspensions, comminuting, fluidization, pneumatic drying and pneumatic conveying,

conveying solids and fans, blowers & vacuum pumps), 3 types of utilities (heating, cooling and

steam generation) and furthermore 3 modules focusing on the processing and use of materials

(electric manufacturing processes, material requirements as a function of mechanical properties

and fuel use of cars). The processes can be assembled to simulate datasets in the foreground

system, obtaining data compatible with life cycle practices, which allows coupling the modelled

system to a background system.

It was attempted to keep these modules user friendly, including the use of default and rule of

thumb values. Furthermore, parameters can be modified, which allows fitting the Basic Unit

Operations easily to the specific application, whilst results are made compatible for LCA studies.

These points are a major advantage in comparison to the use of generic data or other modelling

options.

Of these twenty-five parameterized modules, ten were tested in a case study. Most of these

modules yield relatively good results compared to the real values with an overall average relative

approximation error of 22%. When these modules are used in the case study to estimate the carbon

footprint of a biorefinery, an accuracy of 96% was achieved. It should, however, be noted that there

is a compensation between over- and underestimation in the case studies conducted. Furthermore,

three major shortcomings were identified. Firstly, whereas the inventory of the core unit process

was obtained with an acceptable error, the supporting unit operations such as pumping are very

specific and difficult to obtain whilst not being negligible. Secondly, a factory is complex and

processes are not independent from each other; a successful application of the BUO approach

therefore requires a careful integration of heat use instead of a linear summation of heat use of

different processes. Thirdly the list of selected BUO is still limited to twenty-two, whereas many

other operations are also applied in industry. For example, in biorefineries, operations such as

cyclones and dryers are frequently used. More parameterized modules should thus be developed in

the future.

It is clear that there is no ’silver bullet’ for an accurate and generic estimation of process flow

diagrams. A discrepancy exists between the level of detail of the module and the ease of use and

thus required time and expertise. Each technology has its own features, and the more case specific

data is used, the more accurate the results will be. As results can differ significantly per specific

application, and (small) modifications of the processes are possible in new technologies,

determining statistical uncertainty was not possible. Nevertheless, while more case studies are

necessary for confirmation of the results in this study, the currently available modules produce

relatively reliable results. The possibility of integration of these modules as parameterized unit

operations in LCA software and economic assessment tools can be of major value to obtain a

detailed data inventory necessary for prospective sustainability assessments. The modules should

however be used with care and with a realistic design of a production chain in mind.

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5

Table of contents

Executive summary ............................................................................................ 3

Table of contents ............................................................................................... 5

Introduction ...................................................................................................... 7

Goal... ........................................................................................................... 10

Part I: Development of the engineering modules ..................................................... 11

1. Principles ............................................................................................... 12

1.1 General ........................................................................................... 12

1.2 Units .............................................................................................. 14

1.3 Time aspect...................................................................................... 14

2. Unit operations included ............................................................................ 15

2.1 Reactions ......................................................................................... 15

2.2 Separation processes ........................................................................... 29

2.3 Physical mechanical processes ............................................................... 46

2.4 Utilities ........................................................................................... 75

2.5 Processing and use of materials .............................................................. 84

Part II: Validation of the engineering modules ....................................................... 102

1. Introduction .......................................................................................... 103

2. The biorefinery case study ....................................................................... 103

3. Results and discussion ............................................................................. 106

3.1 Validation examples ........................................................................... 106

3.2 Overview of the validation................................................................... 107

3.3 Using the BUO approach in LCA ............................................................. 109

General conclusions ........................................................................................ 111

References ................................................................................................... 112

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List of tables:

Table 1: preferred quantitative and qualitative units used in the calculation tool ................................... 14 Table 2: Included setups for energy generation ........................................................................... 21 Table 3: Stoichiometric fermentation reactions and current and future product yields per substrate consumed

(Patel et al., 2006) ............................................................................................................. 25 Table 4: Elemental formula for micro-organisms (Harding, 2008) ....................................................... 26 Table 5: Default steam savings in one to triple effect evaporators ..................................................... 30 Table 6: Typical solids concentrations in the underflow of a centrifugation ........................................... 36 Table 7: typical centrifuge configurations and power (pw) (Perry & Green, 1999) .................................... 38 Table 8: Default adiabatic coefficients for different ranges ............................................................. 46 Table 9: Default evaporator efficiencies for different types of evaporators .......................................... 47 Table 10: Default roughness factors of different piping material ....................................................... 54 Table 11: Equivalent lengths for different piping parts ................................................................... 55 Table 12: Dimensionless loss coefficient for gradual pipe enlargements ............................................... 56 Table 13: Default pump efficiencies for pumping incompressible fluids ............................................... 57 Table 14: Sphericity of different packing particles ........................................................................ 59 Table 15: Power numbers for newtonian fluids in the laminar (KL), intermediate (P0) and turbulent region (KT)

for different types of impellers (McCabe et al., 2004) ................................................................... 62 Table 16: typical rotational speeds for different impellers in mixing vessels (McCabe et al., 2004) ............... 64 Table 17: Default pumping numbers for types of impellers .............................................................. 65 Table 18: Work Index for various materials in kWh/tonne (Perry & Green, 1999) .................................... 67 Table 19: Typical pressure differences covered in vacuum pumps, fans and blowers ................................ 74 Table 20: Default efficiencies ................................................................................................ 74 Table 21: Steam tables ........................................................................................................ 81 Table 22: Energy use of different types of injection molding machines (Thiriez, 2005) ............................. 85 Table 23:Energy use of four different types of milling machines (Dahmus & Gutowski, 2004) ...................... 87 Table 24: Energy use for the production of a 0.13-µm metal microprocessor. The active area on the wafers is

261 cm2 consisting of 1 cm2 dies (Murphy et al., 2003) .................................................................. 88 Table 25: Ductile iron induction melter energy usage per tonne shipped (Jones, 2007) ............................. 88 Table 26: Energy use of two different waterjet machines (Kurd, 2004) ................................................ 88 Table 27: Characteristic values CWR, CWL and CWa for different driving cycles ......................................... 98

List of figures:

Figure 1A (left) : ethanol from corn, available in separation, not available in a database. Figure 1B (right):

ethanol from corn after protein a database (Jungbluth et al., 2007). .................................................. 8 Figure 2: the structure of the basic engineering modules, based on Van Der Vorst et al. (2009) ................... 13 Figure 3: The environmental and economic aspects of the Basic Unit Operations .................................... 13 Figure 4: Example of a gasification at 1000K with an air/fuel ratio of 0.4 and biomass fuel with 90% dry matter

(DM) content .................................................................................................................... 20 Figure 5: Determination of the minimum reflux ratio from the Equilibrium curve and operating line in a

distillation. Point D is the composition of the distillate, whilst point F is the composition of the feed (Perry &

Green, 1999) .................................................................................................................... 32 Figure 6: the heat required per kg ethanol for the ethanol distillation (at 85°C) in MJ/kg ethanol after a

fermentation to obtain 94% ethanol/water ................................................................................ 33 Figure 7: Example of an electrodialysis process (Wikipedia) ............................................................. 41 Figure 8: The kinetic energy correction factor for Herschel-Bulkley fluid foods (Valentas et al., 1997) .......... 51 Figure 9: Power number correlations ........................................................................................ 63 Figure 10: Closed steam system .............................................................................................. 79 Figure 11: Time and speed pattern of the New European Driving Cycle................................................ 97 Figure 12: Willans lines and resulting trend lines of a 1.4 l TSI gasoline engine (90 kW) for low output and low

rpm ............................................................................................................................... 99 Figure 13: Comparison of car efficiency results using the two available algorithms ............................... 100

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Introduction

Technological development is at the basis of the industrial system. Its continuous development,

enhanced by profitability, has resulted in an exponential growth of human population and welfare,

resulting in a severe environmental impact. However, this technological progress will not be stopped

because of the resulting impact. In contrary, it will keep on evolving, but it should start creating

opportunities in a sustainable way. This means that new technologies should be introduced with a

minimal environmental impact and maximal welfare creation. These evolutions can be assessed by

using Life Cycle Assessment (LCA) in addition to production cost analysis and life cycle costing

(LCC). Basically, the LCA methodology analyzes the impact of flows between the natural

environment and the technosphere. Similarly to an economic assessment, this environmental

assessment methodology thus requires a mass and energy balance of processes in the technosphere.

The data inventory is therefore at the basis of sustainability assessment of technologies.

The problem however, is that new technologies should be assessed before large scale

implementation and that the required mass and energy data is often not available. Currently,

assessments are mainly executed with the scope on well implemented technologies based on data

originating from 2 sources:

Existing setups (e.g. an industrial plant)

LCI databases

Often a foreground system is chosen of which data is collected in an existing plant. Such a system

can be defined as those processes of the system that are regarding their selection or mode of

operation directly affected by decisions analyzed in the study (European Commission JRC/IES,

2010). Afterwards the data collected within this system is coupled to a database containing a similar

type of inventory of existing products or services to cover the background system of the production

chain. However, often the assessed foreground system consists of a certain sequence of industrial

processes which is inherent to the existing setup. This type of assessments does not offer the

freedom to modify the sequence or type of Basic Unit Operations (BUO)1 and the input and output

parameters of the production chains which would change in new technologies. For example in the

biorefinery sector, ethanol can be made from corn. This data inventory is gathered and readily

available in a database (Figure 1A) (Jungbluth et al., 2007). However, if firstly the proteins are

separated by a wet milling process, and afterwards a wet starch stream is fermented to ethanol,

the study in the database would not be representative to analyse if this new setup is more

sustainable than the previous situation. The total impact of the end products would thus be

unknown, because the specific Unit Process Raw Data (UPR) with mass and energy data of this setup

is not available (Figure 1B), and because the parameters of the UPR in the database are fixed, and

cannot be adapted to the new situation.

1 A Basic Unit Operation (BUO) is a single engineering process such as a distillation, an evaporation

an oxidation, etc. It is NOT the combination of these Unit Operations to production chains which are

called Unit Operations in e.g. SimaPro

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Corn grains

Ethanol 95% in

H20 from corn at

distillery

Ethanol 99.7% in

H20 from corn at

distillery

DDGS from corn

Corn grains

Ethanol 95% in

H20 from corn at

distillery

Ethanol 99.7% in

H20 from corn at

distillery

DDGS from corn

Wet starch stream Protein slurry

Figure 1A (left) : ethanol from corn, available in a database. Figure 1B (right): ethanol from corn after protein separation, not available in a database (Jungbluth et al., 2007).

The assessment of new technologies therefore often results in so-called “data gaps” that should be

closed to allow a complete analysis of sustainability. Wernet et al. (2012) suggest that there are

four ways to obtain the required data:

Extrapolation from existing data

Substitution with generic/average datasets

Molecular structure-based models

Using process based estimation methods

Wernet et al. (2012) explain that the two first approaches should not be preferred because they

often result in an underestimation of the impact mainly because they are in many cases based on

the most simple products of that sector. Using molecular structure based models has its merits, but

is limited to specific sectors and follows a “black box” approach; as such giving little information on

the detailed impact of the production chain. The process based estimation methods are stated to

have the potential to deliver the best results for closing data gaps such as those occurring in

prospective sustainability assessments.

For this purpose, software such as ASPEN, SuperPro Designer and gPROMS can be used as they allow

changing parameters of the BUO and to simulate process chains used in new technologies. However,

they have several disadvantages for the application in LCA:

They are not open source

They are not compatible with the LCI databases: the output values are not always in the correct

units and are often exported to full reports instead of to a summary of Unit Process Raw Data

(UPR)

They do not offer the freedom of choice of complexity of process models. The calculations are

often very detailed, requiring many different inputs. Therefore they should be operated by

dedicated process engineers, who are often not familiar with LCA practices.

As such, the different sources of data mentioned are inadequate to obtain a full data inventory of

future technologies, hampering prospective assessments that are actually vital for sustainable

development. It is thus necessary to develop a process based approach that makes mass and energy

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data available before the construction of production plants in a format which can easily be coupled

to life cycle databases to simulate the background system. Such an approach would make it possible

to estimate sustainability at different system boundaries before large scale implementation of new

technologies. On the other hand, such an approach deals with the environmental and economic

dimension of sustainable development solely, as generic information on social impact is impossible

to obtain in advance because it depends more on company policy than on the technology itself.

Indeed, two exactly the same sets of unit operations necessary for a specific technology can have a

completely different social impact depending on the policy conditions.

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Goal

This basic engineering module approach aims at broadening the most frequently occurring type of

life cycle assessments, i.e. using existing studies or existing plant configurations added with fixed

data from databases, with a more generic approach which allows the modification of set, sequence

and type of BUO and process parameters in the foreground system that influence the life cycle mass

and energy inventory and thus the result of the sustainability assessment. This means that a set of

processes will be characterized in such a way that plants can be assembled together composed by

BUO, providing the attainable data necessary for prospective sustainability assessments, where the

specific focus of this work is on developing modules to estimate mass and energy flows of Basic Unit

Operations with a balance between complexity and accuracy. The modelling of the BUO of this

foreground system will make it possible to obtain inventory data of production chains of new

technologies compatible with life cycle assessment practices; i.e. easy to link to databases to model

the background system. This type of information, based on engineering approaches and rules of

thumbs, will be vital to know if new technologies will achieve a better performance than the

current ones and if they should be implemented or how they can be improved.

This work aims at a certain flexibility, by allowing the user to simulate the process chain with Basic

Unit Operations with different levels of work intensity (and correctness):

The production chain is fixed use existing studies from databases or using data from an

existing setup

The parameters of the production chain can be modified by the user

o The production chain can be modelled with engineering equations completed with

default values and input parameters from the user per BUO

o The production chain can be modelled with engineering equations completed with

detailed input parameters known by the user per BUO

Use simple rule of thumb values

With respect to Figure 1A, for example, the corn ethanol production chain can be taken from an

existing study from the database with fixed parameters. However, when in future the lignocelluloses

in the corn are co-fermented, the yield will change. This could be altered whilst working with

default parameters accounting for other factors such as electricity use if it is expected that the

setup is similar. Alternatively, if the user knows for example that a higher impeller speed, or a

longer residence time is necessary to obtain this co-fermentation this will influence the electricity

use and can therefore be modified. If changes are made, but very limited information is available,

simple rule of thumb values, if they are available, can be used to model the production.

This work is therefore subdivided in two parts. First, 25 different modules are developed and their

calculation procedures are explained. In the second part these modules are validated in a

biorefinery case study.

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Part I: Development of the engineering modules

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1. Principles

1.1 General

The structure of the Basic Engineering Modules is visualized in Figure 2. A distinction is made between

the foreground system α, which is the Basic Unit Operation under study, the foreground system β,

which includes the Supporting Unit Operations (SUO) necessary to support the foreground system α

within the factory gate (gate in to gate out supplied utilities/auxiliaries, e.g. cooling, pumping), and

the background system, γ, which accounts for the life cycle cradle to grave chain of these BUO. The

latter one is modelled by Life Cycle Inventory databases, whilst the foreground system α and β can be

modelled by the Basic Engineering Modules.

To model this complete system, the user will need to enter a minimal amount of input data:

Which BUO + characterisation

Which SUO + characterisation

Input flow characterisation at process gate in

Whilst the parameters for the latter are always similar, namely flow rate, composition, temperature

and pressure, the characterisation of the BUO and SUO depends on the type chosen.

If the minimal amount of input is entered, this can be completed with default values from the

databases DATABUO, which contains default characteristics of BUO, and DATAPHYSCHEM, which

contains default physical and chemical properties of resources and utilities. Alternatively, if the user

knows more detailed information of the setup, the used default values of these databases can be

edited. If too few information is available to use the calculation algorithms, simple Rule Of Thumb

values, from DATAROT database can be used.

Two types of output data are obtained:

Unit Process Raw Data; inventory data of the BUO in the α system and SUO in the β system, which

can be coupled to life cycle inventory databases to obtain a full LCI.

User output; mass and energy balances of the α and β system, which can serve as input to model

other BUO in the process chain of a final product or service.

As aforementioned, this approach is followed for the eco-efficiency assessment of future

technologies. This is visualized in Figure 3. Basic Unit operations need physical inflows (materials and

energy) to generate a certain amount of useful output and a certain amount of waste and emissions.

Similarly economic goods and services are needed to generate a certain amount of economic outflow.

On top of the costs of the resources, several other cost factors can be identified. This work focuses

on the horizontal (marked in red) structure and thus the mass and energy balances of the Unit

Operations, which can generate Unit Process Raw Data necessary for environmental and certain

aspects of the economic assessments.

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DATAPHYSCHEM:Database with editable

default physical and chemical properties of

utilities and resources such as chemicals and biomass

e.g. compositions, heat capacities, ...

Unit Process Raw data α (UPR) Mass and energy input and output of

the α system boundary

DATABUO:Database with editable

default characteristics of BUO

e.g. efficiencies, friction factors,...

Basic Engineering Modules: calculation algorithms

specific per BUO

Foreground system β:SUO: Supporting Unit Operations for

BUO α Typically cooling, pumping, ...

Unit Process Raw data β (UPR of gate in to gate out): Mass and energy input and output of the β system boundary

(Including α)

Life Cycle Inventory Database e.g. ecoinvent or

ILCD

Background system γ(Life cycle requirements necessary

for BUO α and β)

Life Cycle Inventory (γ)(cradle to gate out; including α and β)

DA

TAB

ASE

SD

ATA

INV

ENTO

RY

OF

SPEC

IFIC

CA

SEO

UTP

UT

β System boundary: gate in to gate out γ System boundary: cradle to gate out

Foreground system α:BUO: Basic Unit Operation

considered

α System boundary

User output

User input:- Choose BUO- Choose SUO

- BUO characterisation- SUO characteriztion

- Flow characterisation

Rule of thumb approach specific per BUO

DATAROT:Database with simple Rule Of Thumb values to model

BUO without need of additional information

Figure 2: the structure of the basic engineering modules, based on Van Der Vorst et al. (2009)

Basic Unit Operation with accompanying Supporting Unit Operations

(β system boundary)

Mass to be processed

(M)

Energy (En)

Auxilliary substances

(A)

Physical Inflow at gate in

Waste & Emissions

(WE)

Useful Output (UO)

Physical Outflow at

gate out

Economic Outflow (EO)

Labor (L)Equipment

(Eq)Other Costs

(OC)

Economic Inflow (EI)

Economic assessment

Environmental assessment

Figure 3: The environmental and economic aspects of the Basic Unit Operations

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For the purpose of the environmental assessment, the Physical Inflows, which can origin from the

technosphere or directly from nature, are categorized in 3 classes, with specific physical

characteristics:

Mass to be processed (M)

Mass flow

o Composition

o Temperature

o Pressure

Energy used (En)

o Electricity (for mechanical purposes)

o Other forms of energy:

Heating energy and/or heating medium

Cooling energy and/or cooling medium

Auxiliary substances (A)

Finally, these inputs will generate a mass and energy outflow, which consists out of useful/usable

products and energy (UO) and out of waste and emissions (WE) which can be emitted to nature, or

which can go back to technosphere. One unit of the useful output can be chosen as a functional unit.

Through the equations of the BUO the inflows, wastes and emissions can be linked to the functional

unit. By coupling this information to other BUO or LCI databases, data of the foreground and

background system can be obtained to obtain a full Life Cycle Inventory, which can then be assessed

for its impact.

1.2 Units

Preferred units are represented in Table 1.

Table 1: preferred quantitative and qualitative units used in the calculation tool

Unit\Category M Electricity Other forms of

energy A UO/WE

Quantitative kg kWh MJ kg kg, MJ or kWh

Qualitative (where relevant)

wt%

K

Pa

K

Pa

wt%

K

Pa

wt%

K

Pa

1.3 Time aspect

All Unit Operations are considered to be in continuous mode for the sake of simplicity and since

continuous operation is expected to be implemented more than batch mode in future industrial

development (f3factory.com, Reintjes, 2009). The unit of time chosen is seconds, thus all units

presented in Table 1 are expressed per second (MJ becomes MW). The different flows can thus be

presented per time or per functional unit.

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2. Unit operations included

The BUO are subdivided in 4 major classes based on their functionality. The calculations will focus on

the industrial application of these BUO.

Reactions

o Chemical reactions

o Incineration for heat and power

o Fermentation

Separation processes

o Evaporation

o Distillation

o Filtration

o Sedimenting centrifuges

o Electrostatic precipitation

o Electrodialysis

o Pressure swing adsorption

Physical mechanical processes

o Mechanical compression – single stage

o Mechanical compression – multi stage

o Pumping incompressible fluids

o Pumping incompressible fluids through packings

o Agitation and mixing of liquids and suspensions

o Comminuting

o Fluidization, pneumatic drying and pneumatic conveying

o Conveying solids

o Fans, blowers & vacuum pumps

Utilities

o Heating

o Cooling

o Steam generation

Processing and use of materials

o Electric manufacturing processes

o Material requirements as a function of mechanical properties

o Fuel use of cars

Each BUO focuses on the process within the α system boundary and generates UPR data for this

system. Supporting Unit Operations of the β system should therefore be considered separately, whilst

flows in the γ system boundary should be taken from a life cycle inventory database.

2.1 Reactions

2.1.1 Chemical reactions

Chemical reactions, are modeled with the thermodynamic equilibrium model. This is a very useful

approach for generic modelling, since it is stated that this approach is independent of reactor design

(Puig-Arnavat et al., 2010). However, for each specific case, several assumptions will have to be

made. Most importantly: a thermodynamic equilibrium should be reached, which is not always the

case. Therefore, residence time in the reactor should be high enough. On top of this, the process is

assumed to be adiabatic (no heat losses), the conditions in the reactor should be constant without

spatial variation and gaseous products are assumed to be ideal. The model does not include effects

with byproducts such as tar, ash, micro-organisms, etc. However, for the latter the amount of such

byproducts formed can be deducted from the original amount of reactants if this quantity is known.

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System boundary

The α system boundary for this BUO includes the reaction of the reactants to products in gas or liquid

(including slurries) state in any type of vessel that allows a thermodynamic equilibrium. It is valid

over a broad temperature range, as long as the coefficients for the heat capacity calculations remain

valid; often between 200 and 1500K. Apart from the quantity of output products (useful, UO, and

wastes, WE), the module calculates the amount of heating or cooling energy, if required (in MJ).

Possible SUO are mixing, pumping and cooling. Heating can be found in LCI databases, or can be

modelled by the BUO ‘heating’, ‘steam generation’ or ‘incineration for heat and power’.

Chemical reaction

α System boundary

Reactants (M)

Products (UO)

β System boundary

Mixing Pumping Cooling

Heating (En)

Products (WE)

Calculation algorithm

Three type of equations are used to solve chemical reactions of which the reactants and reaction

products are known. Firstly an elementary balance can be performed. If this is not sufficient, extra

equations can be added by considering the thermodynamic equilibrium of the different reaction

mechanisms. The third type of equation is solving the enthalpy balance of the reaction, which can be

used to obtain additional stoichiometric information or to have an estimate of temperature.

1. Elementary balance

2. Equilibrium constants (Coker, 2001): temperature dependent

The temperature dependent Gibbs free energy of reaction can be obtained with the enthalpies and

entropies of the different products and reactants:

17

With ΔH the temperature dependent heat of formation, and ΔS the temperature dependent molar

entropy.

Where the heat of formation can be obtained by:

The standard heat of reaction ( ) can be:

Input from user

Calculated

With αp and αr the stoichiometric coefficients.

Without phase changes the enthalpy difference between the products and reactants can be based on

the heat capacity:

With T the actual temperature and T0 the reference temperature (298K).

With a, b, c and d the regression coefficients of the heat capacity temperature dependence equation,

which can be found for many chemicals in ‘The Chemical Properties Handbook’ (Yaws, 1998). Thus:

This value represents the enthalpy change of the reaction, and can therefore be used to calculate the

amount of heating or cooling (q) that is required to keep a constant temperature of reaction.

Similarly, the entropy balance can be calculated by:

With:

Finally, the enthalpy balance of the reaction can be formulated:

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User and database input

Input values required from user DATAPHYSCHEM DATABUO

Mass flow rate and type of feed

Reaction products

Estimated temperature

Elementary composition feed

Standard enthalpy and entropy of

products and reactants

Heat capacity coefficients

-

User output

Output product flows

Temperature of output flows

Cooling energy required (in MJ, to be calculated by cooling module)

Heat required

UPR output

From technosphere:

Heat required if necessary (in MJ), e.g. “heat, natural gas, at boiler atmospheric non-modulating

<100kW”

Quantity of inputs necessary for certain products, e.g. 1 MJ syngas requires 0.074kg wood chips.

Example

A gasification produces an energy gas (syngas) consisting mainly out of hydrogen, carbon monoxide,

carbon dioxide, water vapor, methane and nitrogen.

Where CHxOyNz is a type of reactant (M) (fuel, e.g. coal or biomass), w is the moisture content of the

fuel, m is the used amount of air per mole fuel and x1 to x5 represent the molar composition of the

syngas.

Different reactions occur in the gasification zone, which can be combined to two :

Boudouard reaction: (A)

Water-gas reaction (B)

Methane reaction (C)

(A) and (B) can be combined to the Water gas-shift reaction:

(1)

And (B) and (C) to the methane synthesis reaction:

(2)

In the following, thermodynamic equilibrium is assumed in the gasification zone. This is often done,

however, it might not always be the case at lower temperatures (750-1000°C) (Puig-Arnavat et al.,

2010).

The equilibrium constants for the resulting equations are:

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The 5 equations with 5 unknowns are thus the carbon balance, the hydrogen balance, the oxygen

balance, and the two equilibrium reactions.

Additionally, air necessary (m), or temperature T can be calculated by:

With Hf the enthalpies of formation of the respective components.

As a rule of thumb form, the relationship of Borman and Ragland for combustion can be used (Borman

& Ragland, 1998):

If the fuel has a composition of CaHbOcNd then the air fuel ratio is equal to a + 0.25b-0.5c.

Where a gasification is a partial oxidation, needing 30-60% of this stoichiometric air amount (Vaezi et

al., 2008).

For example if 1000K and an air/fuel ratio of 0.4 is assumed, it can be calculated how much fuel is

needed to obtain the functional unit of a syngas with a LHV of 1MJ.

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Figure 4: Example of a gasification at 1000K with an air/fuel ratio of 0.4 and biomass fuel with 90% dry matter (DM) content

The UPR of 1MJ syngas from wood chips, where it is assumed that no additional heat is added, would

thus be:

From technosphere:

0.074kg wood chips (90%DM)

2.1.2 Incineration for heat and power

Incineration is a technology which is typically used to gain energy from different types of resources by

oxidation. The engineering module calculates the energy in the form of heat, electricity or a

combination, generated in different types of industrial combustion configurations (Table 2). In the

first step, the Lower Heating Value (LHV) of the energy source(s) is calculated, whilst in the second

step efficiencies to convert LHV to electricity (and steam) are accounted for. Other calculated

outputs are: the auxiliary power needed, ash production and emission and a rough estimation of the

other main flue gas components H2O, CO2, NOx, SO2, P2O5.

System boundary

The BUO “incineration for heat and power” includes the reaction of the incoming fuel in an industrial

combustion chamber, and the optional conversion of the flue gas to steam and electricity in a boiler

and/or turbine and generator. A certain amount of power is needed to support the combustion

process in the β system boundary. Literature numbers found vary from 2%(Henderson, 2004) to 6%

(Bedi) of total feed LHV. A value of 3% is chosen (Kehlhofer et al., 2009) which is subtracted from LHV

before calculating the energy output.

21

Combustion chamber

α System boundary

Fuel (M)

Electricity (UO)

β System boundary

Air compressor/

blower

Pumping/conveying

fuel

BoilerTurbine

GeneratorSteam (UO)

Hot flue gas (UO or WE)

Pumping water

Table 2: Included setups for energy generation

Steam boiler

Small burner for electricity (all inputs)

Large burner for electricity (all inputs)

Efficient burner for electricity with combined cycles (fossils)

High Efficient burner for electricity with combined cycles (gas)

CHP (all inputs)

Efficient CHP (fossils)

High efficient CHP (gas)


For fossil fuels standard LHV’s are available in a database, whilst for organic chemicals (e.g. waste

solvents) standard net enthalpies of combustion are documented in literature. These values can be

found in DATAPHYSCHEM.

For coal and biomass standard values can be chosen from DATAPHYSCHEM as well, but since their

composition can differ substantially the LHV can be calculated in MJ/kg (dry weight) based on the

ultimate analysis of the fuels.

For coal this is calculated with so-called Milne formula, which was specifically developed for coal and

tested on real data from different coal types (Institute of Gas Technology, 1978; Hoinkis & Lindner,

2007):

For biomass this can be calculated with the most appropriate equation for biomass (R² = 0.834)

(Sheng & Azevedo, 2005):

Where the elemental compositions are in wt% and where:

22

The database contains default elementary compositions (based on dry weight) for different types of

coal and biomass.

The LHV is in both cases calculated by (Phyllis; Fowler et al., 2009):

The LHVwet can be correlated to the LHVdry by:

With MC the Moisture Content of the wet feed in wt%. The LHVwet value in MJ/kg is then multiplied

with the feed flow in kg/s to give the total LHVwet in MW.

If the energy source is a mixture of fuels, it is assumed that the LHV can be added linearly for input

mixtures. For example for two input streams x and y:

Based on elementary composition, either from the database or from the user, a rough calculation can

be made to estimate the main flue gas components H2O, CO2, NOx, SO2, P2O5. A complete oxidation of

the basic components is assumed for simplicity and the emissions are expressed in kg/s.

Thus making a mass balance:

1kg H 9kg H2O

1kg C 3.67kg CO2

1kg N 3.29kg NOx

1kg S 2kg SO2

1kg P 2.29kg P2O5

CO formation depends on local temperature differences in the combustion chamber and therefore it

is very difficult and data intensive to make generic models for CO formation (Velicu & Koncsag, 2009).

For the mass balance, NOx is calculated as NO2. The main NOx component formed at the incineration

is NO, but this is instable and will react further with oxygen to NO2 when released in air. Only fuel NOx

is considered: the Zeldovich mechanism accounting for thermal NO formation from N2 originating

from fuel or air is not considered, because of uncertainties in the currently existing models and high

required input data (the reactions depend amongst others on residence time, local oxygen

concentrations and local temperature) (Schwerdt, 2006).

Ash production is calculated for biomass and coal based on data from the ultimate analysis in the

database (DATAPHYSCHEM). Of this ash content, 98% is retained, whilst 2% is emitted in the case an

ash filter installation (Röder et al., 2004). If this is not the case, 80% is retained, and 20% is emitted

(Schobert, 2002).

To predict the amount of air that is used, the relationship of Borman and Ragland can be used

(Borman & Ragland, 1998):

If the fuel has a composition of CaHbOcNd then the air fuel ratio is equal to a + 0.25b-0.5c.


Input values required from user DATABUO DATAPHYSCHEM

Mass flow rate and composition of feed

Type of energy generation setup

Ash filter; yes/no

Efficiencies

Ash retention

Enthalpies of combustion

Ultimate analysis of the fuel

23

User output

Energy output in MJ as

o Flue gas and/or

o Steam or/and

o Electricity

Ash retention

Air needed

UPR output

From technosphere:

Fuel needed for 1MJ energy

Disposal:

Ash

To nature:

Emissions of ash, H2O, CO2, NOx, SO2, P2O5

Example

To obtain 1 kWh electricity in a small CHP, following UPR is obtained:

From technosphere:

0.828kg “wood chips, hardwood, from industry, u=40%” is necessary.

Disposal:

0.01kg wood ash mixture

Byproducts (not allocated yet)

6.54MJ steam

To nature

0.00021kg PM

0.449 kg H2O

1.44kg CO2

0.013kg NOx

This UPR can thus be allocated to the electricity and steam. The UPR of the SUO (β-α) can be

estimated as 0.12 kWh electricity.

2.1.3 Fermentation

The fermentation includes the conversion of a starch/glucose feed stream to a product/water/rest

products/biomass mixture and optionally a CO2 gas stream. Calculations are made by using extended

Haldane kinetics, which includes substrate affinity, substrate inhibition and product inhibition.

However, it does not include biomass decay and maintenance.

System boundary

The α system boundary includes only the conversion of the feed stream in the fermentor. The module

also calculates the mixing time and the potential need of cooling or heating, which can be used to

calculate the UPR of the SUO in the β system boundary. Most relevant SUO are thus mixing, pumping

and heating/cooling.

24

Fermentation

α System boundary

Feed (M)

Products (UO)

β System boundary

Mixing Pumping Cooling

Heating (En)

CO2 emissions (WE)

Nutrients (A)


If the feed stream is starch, it is assumed to be completely hydrolyzed according to:

This means that the mass of starch should be multiplied with 180/162 to include the additional water

in the final mass of glucose.

The glucose available is then fermented to an end product according to one of the reactions in Table

3, depending on the reaction conditions and type of micro-organism used:

25

Table 3: Stoichiometric fermentation reactions and current and future product yields per substrate consumed (Patel et al., 2006)

Product Reaction products

Product yield; YP/S

(g product/g substrate)

C F

Ethanol 0.46 0.47

PDO (1,3 – Propanediol) 0.41 0.54

ABE (butanol) 0.42 0.50

Acetic acid 0.50 0.90

Acrylic acid 0.72

Lactic acid 0.93 0.95

Succinic acid 0.88 1.01

Adipic acid 0.17 0.47

Citric acid 0.86 0.96

*C = Current, F = Future

26

The generic reaction which occurs when 1 mole of substrate is consumed includes the formation of

biomass

Where R is the amount of moles carbon in the reaction specific rest products, which can be found in

the reaction equations of Table 3, and B is the amount of moles of biomass which is formed per mole

of substrate consumption and which is based on the carbon balance:

With YP/S is the yield of product per substrate (g/g), which can be found in Table 3. The rest products

are reaction specific (see Table 3). The elementary balance of hydrogen and oxygen will not be

checked due to the complexity of bacterial growth. Therefore standard values of bacterial

composition are taken, which can be found in Table 4 (Harding, 2008). Based on these compositions,

the amount of nitrogen, sulphur and phosphorus nutrients to be added in the fermentor can be

calculated.

Table 4: Elemental formula for micro-organisms (Harding, 2008)

Organism C H O N S P

Aerobacter aerogenes 1 1,83 0,55 0,25

Aspergillus niger 1 1,74 0,711 0,117

Azohydromonas lata 1 1,76 0,48 0,19

Candida sp. 1 1,84 0,52 0,16

Escherichia coli 1 1,77 0,49 0,24

Klebsiella sp. 1 1,75 0,43 0,23

Paracoccus denitrificans 1 1,66 0,49 0,2

Pseudomonas C12B 1 2 0,52 0,23

Saccharomyces cerevisiae 1 1,76 0,53 0,17 0,005 0,01

Other 1 1,82 0,53 0,2

The total output mass flow (kg/s) of end product formed is:

With Sin the mass flowrate of substrate in (kg/s) and Subeff the fraction of input substrate converted:

Where Sused is the substrate fermented and Subeff is input from the user, which is taken as a default

value of 90%. However, the user can vary this efficiency to follow the effect on the reaction time

The unused substrate (Sun) is determined by:

27

In continuous mode, the enthalpy change of the reaction allows the calculation of amount of cooling

or heating medium required to maintain a certain temperature. For example for ethanol fermentation

by Saccharmoyces cerevisiae, following reaction enthalpy can be used:

(Chongvatana, 2007-2008)

To determine the reaction time, and thus reactor volume, necessary for the fermentation, Haldane

kinetics with product inhibition can be used. The rate of biomass formation (rb) can be calculated by

(Ghose & Tyagi, 1979):

Where the specific growth rate, µ (h-1), is determined by:

B = biomass concentration (g/l), Ks Monod constant (g/L), KI inhibition constant (g/L), µmax the

maximum specific growth rate (h-1), P the ethanol concentration in the fermentor (g/l), α the product

inhibition constant, Pmax the maximum ethanol concentration (g/l) and CSun = substrate concentration

(g/L). A perfectly mixed fermentor is assumed, implying that the concentration in the tank is the

same as the concentration in the outlet flow. In a continuous reactor, this can be determined by:

With M and ρ the mass and density (kg/l) of the unused product (un), the water (H2O), the end

product (prod), the biomass (B) and the rest products respectively and assuming that density can be

added linearly.

These kinetics can be simplified to

in the case that substrate and product inhibition are negligible, and if substrate concentration is high

in comparison to the Monod constant. However in industrial practice this might not always be the

case, since it is the goal to consume as much as the substrate as possible to form high concentrations

of end products. Furthermore, the product inhibition factor and substrate inhibition

factor can be neglected if no influence of product and substrate concentration is expected.

Based on these reaction rates, the residence time in the reactor can be estimated:

With MMS the molar mass of the substrate and V the volume of the tank (in l):

Reaction time can thus be determined by:

Where Q is the flow rate in (l/s):

28


Input values required from user DATABUO DATAPHYSCHEM

Fermentation reaction

Type of micro-organism

Mass and composition of input flow

Substrate conversion efficiency

Yield coefficient

Kinetic parameters

Reaction enthalpy

(preferably entered by user)7

Composition feed streams

User output

End product/water/rest product/biomass sludge composition

Reaction time

Cooling/heating energy required (MJ)

UPR output

From technosphere:

Amount of biomass needed for 1 kg fermentation product

Kg nitrogen, sulphur and phosphorus containing product required per kg fermentation product

To nature:

Kg biogenic CO2 emitted

Example

A glucose stream with 1kg/s glucose and 10kg/s water enters a fermentation section with

Saccharomyces cerevisiae to form ethanol. When a substrate consumption efficiency of 90% is taken,

and with a default yield coefficient of 0.46kg/kg, 0.414kg/s ethanol and 0.396kg/s CO2 is formed,

whilst 0.1kg/s substrate remains unused.

Using the elementary carbon balance, 0.6 moles biomass (CH1.76O0.53N0.17S0.005P0.01) is formed per mole

glucose fermented. This means a total mass flow of biomass of 0.075kg/s. As such, 0.102 mole N,

0.003 mole S and 0.006 mole S should be added per mole glucose fermented or 0.51, 0.015 and 0.03

moles nutrients in total. Therefore, 15.3g ureum, 1.47g H2SO4 and 4.26g P2O5 is needed.

The substrate concentration in the fermentor can be determined based on this unused substrate and

the output flow. Csun thus becomes 10.65g/l.

With default chosen kinetic parameters for ethanol production (µmax = 0.584, KS = 0.155g/l, Ki

=160.7g/l, Pmax = 125g/l, α = 3.68), µ becomes 0.138hr-1 and with a biomass concentration of 10g/l, rp

becomes 1.38g/l/h.

Based on the total mass of biomass formed, and on this biomass formation rate, a residence time of

5.11hr is calculated.

Rescaling to the functional unit of 1kg ethanol, the UPR α becomes:

From technosphere

2.41kg glucose, or if glucose is estimated as 60wt% of corn, 4kg corn

37.0g ureum

3.6gH2SO4

10.3g P2O5.

To nature

0.96kg carbon dioxide, biogenic

29

2.2 Separation processes

2.2.1 Evaporation

The module ‘evaporation’ calculates the energy required to concentrate a solution consisting of a

non-volatile solute and a volatile solvent. Where the latter is water in the majority of the cases. In

contrary to drying, the residue of an evaporation is a (sometimes highly viscous) liquid, whilst the

main difference with distillation is the fact that no efforts are made to obtain a concentrated vapour

(McCabe et al., 2004). It can be used for 1 to 3 effect evaporators and for evaporators with

mechanical recompression.

System boundary

The BUO ‘evaporation’ includes the 1 to 3 effect evaporator. Pumping operations for the different

liquid and gas streams, and vacuum pumps are not included in the α system boundary. Furthermore,

recompression should also be added as a SUO if this is necessary. Calculations are valid over a broad

temperature range, as long as the coefficients for the heat capacity calculations remain valid.

Evaporation

α System boundary

Thin liquor (M)

Thick liquor (UO)

β System boundary

Pumping

Heating (En)

Vapor (UO/WE)

Recompression

Condensate (UO/WE)

Vacuum pump


The heat required for the evaporation can be obtained by making an enthalpy balance, with a thin

liquor coming in (f) and thick liquor going out (o):

With m the respective masses, Hv the specific enthalpy of the evaporated vapor, Hf the enthalpy of

the incoming feed, Ho the enthalpy of the thick liquor and η an efficiency factor, typically 0.9,

accounting for heat losses.

30

Or shorter

With λ the latent heat of vaporization of the evaporated vapor, cp the mean heat capacity of the

feed, Tb the boiling temperature of the mixture and Tf the temperature of the feed.

This model assumes a negligible heat of dilution, which is typical for flows containing sugars, organic

salts and papermill liquors (McCabe et al., 2004).

In order to save energy, a part of the enthalpy in the evaporated vapour is recovered in sequencing

vessels or ‘effects’, which is called multi effect evaporation. To estimate the amount of heat savings

per effect, the default values in Table 5 from DATABUO can be used for one to triple effect

evaporators (Grosse & Duffield, 1954). The heat required then becomes:

Table 5: Default steam savings in one to triple effect evaporators

Amount of effects Calculated amount of heat required per amount evaporated (Feff)

1 effect 1.00

2 effects 0.52

3effects 0.37


Input values required DATABUO DATAPHYSCHEM

Mass and type of feed

Feed temperature

Pressure

Mass of water to be evaporated

Number of effects

Recompression; yes/no

Steam savings for 1 to 3 effect

evaporators

Heat capacities

Enthalpy of vaporization

Boiling temperature

User output



Need for recompression

Heat required

UPR output

From technosphere:

Heat required (in MJ)

Example

A wet starch stream of 29000L water enters at 20°C and is evaporated in a single effect evaporator at

1atm to a solution 9000L. Thus 20000L of water is evaporated. In a regular evaporation unit with Hv =

2257kJ/kg, cp, water = 4.17kJ/kgK, cp, starch = 1.53kJ/kgK and Tb = 100°C, this results in a heat

requirement of 54993MJ.

The UPR of evaporating 1L water from this stream is thus:

From technosphere:

2.75MJ heat, unspecific, in chemical plant

31

Rule of thumb values

In the BREW study (Patel et al., 2006) literature values for single-stage- and multi-stage evaporation

have been collected. Steam requirements for single-stage evaporation range from 0.005-1.4 kg

steam/kg evaporated. The recommended value was determined at 1.2 kg steam/ kg evaporated.

Electricity use for single-stage evaporation was estimated at 0.04 kWh/ kg evaporated. For multistage

evaporation, literature values ranged from 0.01-1.25 kg steam / kg evaporated. The recommended

value was determined at 0.1-0.5 depending on the amount of stages. Electricity use for multistage

evaporation was estimated at 0.002-0.0344 kWh/ kg evaporated. The recommended value was

determined at 0.005 kWh/kg evaporated.

2.2.2 Binary distillation

Distillation is used as a separation process based on different boiling points. In contrary to

evaporation, all components are appreciably volatile. Furthermore, the vapour phase is condensed in

an overhead condenser, optionally with a reflux, the latter returning a part of the condensed vapour

to the distillation column.

System boundary

The BUO can be used for non extractive binary distillations with or without reflux. It calculates the

heat required for the evaporation process (often in a reboiler) and the cooling energy required in the

condensation (incl. reflux). This is valid over a broad temperature range, as long as the coefficients

for the heat capacity calculations remain valid. Pumping operations for the different liquid and gas

streams, vacuum pumps and possible mixing operations are not included in the α system boundary.

Furthermore, cooling (in the condenser) should be added as a SUO in the β system boundary.

Distillation column

α System boundary

Distillation feed (M)

Bottoms (UO/WE)

β System boundary

Pumping

Heating (En)

Distillate (UO/WE)Overhead condensor

CoolingVacuum

pump

V

L

Mixing


The general equation for calculating the energy requirements of a distillation is very similar to the

equation for an evaporation, but includes the fraction of reflux that has to be added to the enthalpy

of vaporization:

With mf and mb the mass of the feed and the bottoms respectively, λv the latent heat of the vapor, cp

the mean heat capacity of the feed, Tb the pressure dependent boiling point of the vaporized

component and η an efficiency factor, typically 0.9, accounting for heat losses.

32

The Reflux Ratio (RR, or L/V) can be calculated based on the minimum reflux ratio (RRmin) with a rule

of thumb (Perry & Green, 1999):

Assuming negligible holdup of liquid on the trays, in the column, and in the condenser, the minimum

reflux ratio can be obtained by:

With ypi and xpi the initial molar fraction of the more volatile component and yd and xd the molar

fraction of the more volatile component in the distillate in the vapour phase and liquid phase

respectively. Where xd and yd are equal assuming a total condensation (Figure 5 (Perry & Green,

1999)).

Figure 5: Determination of the minimum reflux ratio from the Equilibrium curve and operating line in a distillation. Point D is the composition of the distillate, whilst point F is the composition of the feed (Perry & Green, 1999)

Assuming that the vapor is cooled to obtain a phase change, the cooling duty for the condensation

(qc) of is:

Aforementioned theory is generally applicable, however does not include the specific cases of

azeotropes. A frequently used example is the case of an ethanol – water – biomass mixture obtained

after a fermentation. To include this case it has been modelled using SuperPro Designer based on the

amount of wt% ethanol in the fermentation slurry.

The equation obtained is:

33

With y the amount of MJ steam required per kg ethanol and x the weight percentage bioethanol in

the slurry.

Figure 6: the heat required per kg ethanol for the ethanol distillation (at 85°C) in MJ/kg ethanol after a fermentation to obtain 94% ethanol/water



Composition feed

Compositions distillate -

Heat capacities

Enthalpy of vaporization

Pressure dependent boiling

temperature

User output



Cooling energy required (MJ)

Heat required

UPR output

From technosphere:

Heat required (in MJ)

Example

A mixture of 800 kg in 9000kg water, 100kg biomass and a substrate leftover of 100kg glucose means

8% ethanol weight percentage resulting in a heat requirement of 4.63MJ/kg. Assuming a reflux ratio

of 0.9 (RR=L/V), the cooling requirement becomes 1.60MJ/kg.

34

The UPR α of distilling 1kg ethanol to 94% is thus.

From technosphere:

4.63MJ heat, unspecific, in chemical plant


In the BREW study (Patel et al., 2006) literature values for distillation have been collected ranging

from 0.9-4.4 kg steam/kg of product and 0.07-0.14 MJe/kg of product. No recommended value was

suggested but instead the energy use for distillation should be determined by multiplying the heat of

evaporation by a reflux factor of 1.3. Electricity demand to generate vacuum, which may reduce the

heat requirements of the distillation column are assumed to be very low to negligible.

2.2.3 Filtration

In chemical processes, filtration is the mechanical or physical operation which is used for the

separation of solids from fluids (liquids or gases) by leading them through a medium that allows only

the fluid to pass and that retains oversize solids. As a filtration medium, normally a solid sieve or a

membrane (surface filter) is applied although filtration can also occur through a bed of granular

material (depth filter). Fluids flow through a filter due to a difference in pressure – fluids flow from

the high pressure side to the low pressure side of the filter, leaving solids behind. The application of

gravity is the simplest way to achieve this, but in the laboratory, pressure in the form of compressed

air on the feed side or vacuum on the filtrate side may be applied to enhance the filtration process.

In industry, when a reduced filtration time is important, the liquid may flow through a filter by the

force exerted by a pump.

System boundary

The α system boundary for this BUO includes the filtration of a suspension and the pumping operation

applied to create a pressure difference. Depending on the requirements, either the filtrate or the

solids are the useful output.



The power for filtration (Pf in W) is calculated according to (Harding, 2008):

Filtration

System boundary

Pumping

System boundary

Suspension

(M)

Filtrate

(UO/WE)

Solids

(UO/WE)

Electricity

(En)

35

In which:

P = Pressure difference across the filter (Pa)

Q = Flow through the filter (m³/s)

eff. = Pumping efficiency (dimensionless)



Flow of fluid Pressure difference Pumping efficiency

User output

-

UPR output

From technosphere:

Amount of electricity required, e.g. “Electricity, medium voltage, production UCTE, at grid”.

Example

An industrial filtration unit is assumed with a filtration flow 1 m3/s. The pressure difference is 1000

kPa and the pumping efficiency is 0.8 (Harding, 2008). In this situation, the required power for

filtration is 1250kW, or 0.35kWh/m³.


In the BREW study (Patel et al., 2006), Values for different types of membrane filtration have been

estimated. An overview is given below:

Type of membrane filtration Unit Value range Chosen value

Microfiltration kWh/m3 permeate 1.2-2.6 2

Ultrafiltration kWh/m3 permeate 3.5-16 5

Diafiltration kWh/m3 permeate 5 5

Nanofiltration kWh/m3 permeate 1-7 7

Reverse osmosis kWh/m3 permeate 2.5-10 9

2.2.4 Sedimenting Centrifuges

Sedimenting centrifugation is a broadly used, but energy intensive equipment to separate solids from

liquids based on a difference in density, without using a filter (Perry & Green, 1999).

System boundary

This BUO focuses in the α system boundary on the electricity use of the centrifuges. Data is available

for tubular, disk, disk with nozzle discharge and decanters/helical conveyors. Necessary pumping

operations are situated in the β system boundary. An approximation can be made of the separation

efficiency, to serve as input for further BUO.

36

Sedimenting centrifuge

α System boundary

Feed (M)

Solids (UO)

β System boundary

Pumping

Electricity (En)

Centrate (UO/WE)


Due to the complexity of the process, where the settling speeds and power consumption depends

heavily on type of centrifuge and particle size and shape of the solids, it is very difficult to obtain

one generic equation. Therefore, the power consumption is modeled by using default powers of

different types of equipment, which can be taken from DATABUO (Table 6 (Perry & Green, 1999)).

Separation efficiencies can be calculated with the sigma (Σ) concept, starting from the solids

concentration in the underflow stream, which can be estimated by (Van Der Meeren, 2004; Brennan,

2006; Woon-Fong Leung, 2007):

Table 6: Typical solids concentrations in the underflow of a centrifugation

Type Typical solids concentration (volume%)

Tubular 95%

Disk 40%

Disk; nozzle discharge 40%

Decanter / Helical conveyor 95%

Whilst the efficiency of the separation can be based on the maximal theoretical flow (m³/s) rate at

which a 100% separation is reached (Axelsson & Madsen, 2006):

With ε an efficiency factor, correcting the theory for practical application, where this factor is 95%

for tubular bowl centrifuges, 60% for decanter centrifuges, and 60% for disk centrifuges.

v is the settling velocity (m/s):

Dp is the diameter of the solid particle (m) to be separated, µ is the dynamic viscosity of the liquid

(Pa.s), g the gravity constant (9.81m/s²) and ρp and ρ are the densities (kg/m³) of the particle and

the liquid respectively.

37

Σ is the sigma factor, depending on the type of centrifuge used:

For disk-bowls

Where r2 is the outer radius of the disk, and r1 the inner radius of disk, N is the number of disks and θ

is the half cone angle of the disks. w is the rotational speed (rad/s):

With n in rpm, which can be taken from Table 7.

For tubular centrifuges:

With R the radius of the bowl (m), and ri the radius of the inner layer of the fluid (m), thus R-ri being

the thickness of the fluid in the centrifuge.

For decanters:

With L1 the length (m) of the cylindrical part, and L2 (m) the length of the conical part.

38

Table 7: Typical centrifuge configurations and power (pw) (Perry & Green, 1999)

Type Bowl diameter

(cm)

Typical speed

(r/min)

Maximal centrifugal

force x gravity

Liquid throughput

min (l/s)

liquid throughput

max (l/s)

Solids

throughput min

(kg/s)

Solids throughput

max (kg/s)

Typical

motor size

(kW)

Tubular

4.45 50000 62400 0.003 0.016

2

10.48 15000 13200 0.006 0.630

1.5

12.70 15000 15900 0.013 1.260

2.2

Disk

17.78 12000 14300 0.006 0.630

0.2

33.02 7500 10400 0.315 3.150

4.5

60.96 4000 5500 1.260 12.600

5.6

Disk; nozzle

discharge

25.40 10000 14200 0.630 2.520 0.028 0.278 14.9

40.64 6250 8900 1.575 9.450 0.111 1.111 29.8

68.58 4200 6750 2.520 25.200 0.278 3.056 93.3

76.20 3300 4600 2.520 25.200 0.278 3.056 93.3

Decanter / Helical

conveyor

15.24 8000 5500

1.260 0.008 0.069 3.7

35.56 4000 3180

4.725 0.139 0.417 14.9

45.72 3500 3130

6.300 0.278 0.833 37.3

60.96 3000 3070

15.750 0.694 3.333 93.3

76.20 2700 3105

22.050 0.833 4.167 149.2

91.44 2250 2590

37.800 2.778 6.944 223.8

111.76 1600 1600

44.100 2.778 6.944 298.4

137.16 1000 770

47.250 5.556 16.667 186.5

2 Turbine driven, 372kPa necessary (steam or air compressor)

39



Type of centrifugation

equipment

Liquid/solid throughput

Centrifuge size

characterisation

Centrifuge power

Rotational speed

Bowl diameter

Viscosities

Densities

User output


UPR output

From technosphere:

Electricity used (kWh)

Example

A stream of 20kg/s water and 0.5kg/s solids (assuming density of solids 1300kg/m³ and the

viscosity of the fluid 0.005Pa.s) is centrifuged in a disk centrifuge with nozzle discharge. This

requires a centrifuge of 93.3kW.

If the assumed rotational speed is 4200rpm, in a disk with nozzle discharge centrifuge of 76cm

diameter, with 30 disks at 35° the sigma factor becomes approximately 30000m², and the

maximum flow to obtain a centrate stream free of solids of 0.1mm diameter is 5.76m³/s. This

means that in this situation a pure water stream of 19.42kg/s is obtained and a underflow

containing 0.5kg solids and 0.58 kg water per second.

The UPR α of separating 1kg solids from 97.5 to 60%v/v moisture content is thus:

From technosphere

0.052kWh electricity, medium voltage


In the BREW study (Patel et al., 2006) literature values for centrifugation have been collected for

yeast centrifugation and for bacteria centrifugation. The values for yeast centrifugation range

from 0.7-2.5 kWh/m3 of feed. The recommended value was determined at 1.5 kWh/m3 of feed.

The values for bacteria centrifugation range from 6.2-25 kWh/m3 of feed. The recommended

value was determined at 7 kWh/m3 of feed.

2.2.5 Electrostatic precipitation (ESP)

Electrostatic precipitators use an induced electric charge to separate solids from a gaseous

stream.

System boundary

This BUO includes the electricity used for the corona generation and is valid for gas velocities

between 0.5 and 3m/s. The module is aimed at dry electrostatic precipitation of particulate

matter from 0.01 to 10µm. It does not include pumping operations.

40

Electrostatic precipitation

α System boundary

Gas (M)

Cleaned gas (UO)

β System boundary

Pumping

Electricity (En)

Removed PM (WE)


The electrical corona produced requires a certain amount of power, which can be calculated by

(University of Florida, Environmental Engineering Sciences, Aerosol & Particulate Research Lab,

2011):

P is the Power (in W), Q is the volumetric gas flow (in m³/s) and η is the efficiency of the

precipitation in %.



Gas flow rate + composition

Efficiency of solids removal - -

User output

Mass of solids precipitated

UPR ourput

From technosphere:

Electricity use

Example

A polluted stream of 2m³/s contains 500g/m³ particulate matter. To achieve a removal efficiency

of 95%, 0.116kWh/h electricity is consumed. 950g/s of pollutant is precipitated.

The UPR α of cleaning 1m³ gas is thus:

From technosphere

1.61E-5 kWh electricity, medium voltage

2.2.6 Electrodialysis

Electrodialysis is a separation technique that uses an applied electric potential difference for the

transportation of salt ions from one solution to another through ion-exchange membranes. This is

done in a configuration called an electrodialysis cell. The cell consists of a feed (dilute)

compartment and a concentrate (brine) compartment formed by an anion exchange membrane

41

and a cation exchange membrane placed between two electrodes. Under the influence of an

electrical potential difference, negatively charged ions in the dilute stream migrate towards the

positively charged anode. These ions pass through positively charged anion exchange membrane,

but are prevented from further migration toward the anode by the negatively charged cation

exchange membrane and therefore stay in the concentrate stream, which becomes concentrated

with the anions. The positively charged species in the dilute stream migrate toward the

negatively charged cathode and pass through the negatively charged cation exchange membrane.

These cations also stay in the concentrate stream, prevented from further migration toward the

cathode by the positively charged anion exchange membrane. Anion and cation migration is

enabled by an electric current that flows between the cathode and anode. A schematic example

of an electrodialysis process is given in Figure 7 for NaCl concentration. Only an equal number of

anion and cation charge equivalents are transferred from the dilute stream into the concentrate

stream, maintaining the charge balance in each stream. The overall result of the electrodialysis

process is an ion concentration increase in the concentrate stream with a depletion of ions in the

dilute solution feed stream (American Water Works Association, 1995).

Figure 7: Example of an electrodialysis process (Wikipedia)

This basic engineering module calculates the energy requirements for the electrodialysis process.

System boundary

The system boundary of this process includes the electrodialysis process in which ions are

shifted from a diluate stream to a concentrate stream. Either the diluate stream is the useful

output (if the aim is to get rid of certain substances in a liquid) or the concentrate stream is the

useful output (if the aim is to concentrate certain substances in a liquid). There is an additional

flow, i.e. the electrode stream. The electrode stream flows past each electrode in the stack. This

stream may consist of the same composition as the feed stream or may be a separate solution

containing different compounds. Depending on the stack configuration, anions and cations from

the electrode stream may be transported into the concentrate stream, or anions and cations from

the diluate stream may be transported into the electrode stream. In each case, this transport is

http://upload.wikimedia.org/wikipedia/commons/7/75/Electrodialysis.jpg

42

necessary to carry current across the stack and maintain electrically neutral stack solutions. The

system boundary includes pumping requirements for the transport of the streams.


The electricity requirement for electrodialysis is calculated according to (Perry & Green, 1999):

n

FUE

I

ed

In which:

Eed = Energy consumption (J/ mol equivalent salt shifted)

U = Applied voltage (V)

F = Faraday constant (= 96485.34 Amp.s/mol)

I = Current efficiency (dimensionless)

n = number of cell pairs (dimensionless)

The energy consumption is expressed per mol equivalent salt shifted. An equivalent stands for a

unit of charge. This means that, e.g., 1 mol SO42- equals 2 mol equivalents. And likewise, the shift

of 1 mol H2SO4 equals 2 mol equivalents. The meaning of expressing shifts per equivalent appears

from the following formula for current efficiency:

In

FccQ

In

FN iipiI

0

In which:

Ni = Flow of ionic substance i formed from the splitting operation (mol eq/s)

I = Current intensity through the stack (A)

Qp = Volumetric flow of the product (m3/s)

ci = Concentration of ionic substance i in the product stream (mol eq/m3)

ci0 = Concentration of ionic substance i in the inlet stream (mol eq/m3)

The current efficiency basically determines the amount of coulombs shifted per coulomb applied

by means of external power. The amount of coulombs shifted is determined by the charge of the

ions. Each coulomb originates from 1 charge equivalent of an ion. It is represented by the

numerator and expressed in Amp.s/s = Coulomb/s. The amount of Coulombs externally supplied is

determined by the denominator and is expressed in Amp which is also Coulomb/s.

Electrodialysis

System

boundary

Pumpin

g

System

boundary

Concentrate feed

stream (M)

Diluate feed

stream (M)

Concentrate stream

(UO/WE)

Diluate stream

(UO/WE)

Electricity (En) Electrode

stream (A)

43



Applied voltage

Number of cell pairs

Current intensity through the stack

Volumetric flow of the product

Concentration of ionic substance i in

the product stream

Concentration of ionic substance i in

the inlet stream

Faraday constant -

User output

-

UPR output

From technosphere:

Electricity required, e.g. “Electricity, medium voltage, production UCTE, at grid”.

Example

A small lab-scale electrodialysis process is considered in which a solution of acetic acid is

concentrated. At the start of the process the concentration of acetic acid in the concentrate

stream is equal to that in the diluate stream, i.e. 0.84 mol/l. The process runs for 330 minutes at

a flow rate of 0.174 l/h. At the end of the process, the concentration in the concentrate stream

is 1.0 mol/l. Furthermore the following parameters/ process conditions apply:

Number of cell pairs: 7

Applied voltage: 24 V

Applied current: 0.24 Amp

Faraday constant: 96485.34 (Amp.s)/mol

Molar mass acetic acid: 60.05 g/mol

Inserting the above values in the formulas defined for this process gives a current efficiency of

0.44. The electricity requirement is 0.75 MJ/mol salt shifted, or 3.45 kWh/kg acetic acid or 12.4

MJ/kg acetic acid.


In the BREW study (Patel et al., 2006) literature values for different electrodialysis processes

have been gathered. This yielded a range of energy values, i.e. 0.07-0.34 kWh/mol eq. The

recommended value in this study is to use 0.1 kWh/ mol eq. The respective equivalent value from

the example above is 0.2 kWh/mol. The difference to the generic value according to the BREW

study (factor of 2) may be explained primarily by a rather low current efficiency in the lab-scale

process (44%).

2.2.7 Pressure swing adsorption

Pressure swing adsorption is a technology that is used for the separation of gas species from a

mixture of gases under pressure. It is based on the species molecular characteristics and affinity

for an adsorbent material. Pressure swing adsorption processes rely on the fact that under

pressure, gases tend to be attracted to solid surfaces, or ‘adsorbed’. Special adsorptive materials

are used as a molecular sieve, for example ‘zeolites’. The process then swings to low pressure to

desorb the adsorbent material.

This basic engineering module calculates the energy required to separate a gas from a gas

mixture by pressure swing adsorption.

System boundary

44

The system boundary of this process includes the separation of (a) gas(es) from a gas mixture

by means of pressure swing adsorption. Either removed gas(es) or the remaining gas mixture can

be the useful output product. For this process, energy in the form of electricity is needed as well

as adsorbent material. The adsorbent material can be reused many times however. In the

system boundary, some pumping is required to transport the gases.


An extensive theoretical analysis of a pressure swing adsorption process is given by Huang et al.

(Huang et al., 2008). The power requirements of a pressure swing adsorption process are

calculated according to:

feedfeedbed

atm

feed

feedg curp

pTRP 2

1

11

In which:

P = Power (W)

= Ratio of heat capacities, cp/cv (dimensionless)

Rg = Ideal gas constant, i.e. 8.314 J mol-1K-1

Tfeed = Temperature of the feed (K)

pfeed = Pressure of the feed (bar)

patm = Atmospheric pressure, i.e. 1.013 bar

rbed = Column radius (m)

ufeed = Interstitial gas velocity (m/s)

cfeed = Concentration of the feed stream (mol/m3)

The ratio of the heat capacities for various gases can easily be found in chemical handbooks

(White, 1999; Lange & Dean, 1973) or even on the internet.

The interstitial gas velocity is calculated with:

A

Qu feed

In which:

Q = Volumetric flow rate (m3/s)

Pressure Swing

Adsorption

Pumping

System

boundary

System

boundary

Gas mixture

(M)

Separated gas(es) (UO)

Electricity (En)

Separated gas(es) (WE)

Adsorbent (A)

45

A = Cross sectional area of the bed (m2)

= Void fraction of the bed, i.e. ratio of the void volume to the total volume of the bed

The concentration of the feed stream (all compounds) is calculated based on the ideal gas law:

feedg

feed

feedTR

pc

If the flow rate of the separated gas is known, the total energy requirements (in J/kg) can be

calculated:

v

PE

In which:

E = Energy use (J/kg)

P = Power (W)

v = Flow rate separated gas (kg/s)



Temperature of the feed

Pressure of the feed

Column radius

Interstitial gas velocity

Concentration of the feed

Flow rate separated gas

Atmospheric pressure

Ratio of heat capacities -

User output

-

UPR output

From technosphere:


Example

A pilot plant for the purification of hydrogen from a mixture containing 25% H2 and 75% CH4 has

the following dimensions and operating conditions:

Column radius: 0.25 m

Interstitial gas velocity: 0.25 m/s

Temperature of the feed: 303.15 K

Pressure of the feed: 500 kPa

Atmospheric pressure: 101.325 kPa

Ratio of heat capacities: 1.342 (25% H2 and 75% CH4)

Ideal gas constant: 8.31 J/(mol.K)

The concentration of the feed is calculated with the ideal gas law and amounts to 198 mol/m3.

Power requirements are 48.4 kW. Assuming a column radius of 0.5 m and a voidage of 0.4 gives a

flow rate of 0.0196 m3/s. If it were assumed that 25% is hydrogen, the hydrogen flow rate from

the PSA process is 17.7 m3/h. If a hydrogen recovery of 79.6% were assumed and a hydrogen

density of 0.08988 g/l at atmospheric pressure, this results in an energy requirement of 138

MJ/kg hydrogen.

46

2.3 Physical mechanical processes

2.3.1 Mechanical compression – Single stage

Compression uses mechanical energy to increase the pressure of a gas. This can be used for

steam, to recover the latent heat, or for other gases such as air, to obtain higher driving forces.

System boundary

The BUO includes the mechanical energy for the compression in a reciprocating or centrifugal

compressor. It assumes the adiabatic compression of ideal gases, which is a reasonable

approximation for most compressors (Perry & Green, 1999). SUO such as pumping are not

included in the α system boundary.

Compression

α System boundary

Gas (M) Compressed gas (UO)

β System boundary

Pumping

Electricity (En)


The mechanical energy needed for the compression (P, in kW) can be obtained by:

With m the mass flow in kg/s, MM the molar mass and the specific compression work W (in

kJ/kmol). In the case of ideal gases, the specific work can be obtained by (Perry & Green, 1999):

R = universal gas constant (J/molK), Pc = pressure after compression (Pa), pi initial pressure (Pa),

Ti initial temperature (K) = Tb; the boiling temperature, α = efficiency factor, ratio of specific

heats, adiabatic coefficient. For the latter, the values in Table 8 can be used as defaults

(www.engineeringtoolbox.com), however for other gases, or other conditions, this value should

be modified by the user.

Table 8: Default adiabatic coefficients for different ranges

Range Ratio of heat capacities

Steam 0.068 atm 50 – 316 oC 1.32

Steam 1 atm 107 –316 oC 1.31

Steam 10,2 atm 182 – 316 oC 1.28

Air 1.41

47

Temperature and pressure before and after compression can be linked to each other to increase

user friendliness:

With Tc the temperature after compression.

The efficiency factor depends on the type of compressor and the compression ratio or flow. The

user has to choose a type of compressor and default efficiency factors will be taken from

DATABUO (Table 9 (www.cheresources.com)).

Table 9: Default evaporator efficiencies for different types of evaporators

Compression ratio (pc/pi) Efficiency

Reciprocating compressors

1.5 65%

2 75%

3-6 80-85%

Centrifugal compressors

2.8 to 47 m3/s 76-78%



Mass and type of feed

Feed temperature

Type of compressor

Compression ratio

Efficiency Adiabatic coefficients

User output

Physical properties of output stream

UPR output

From technosphere:

Electricity use

Example

A vapour leaving an evaporator is recompressed with a compression ratio of 1.5. The intial

temperature is 373K and the adiabatic coefficient is 1.31. The specific work W becomes

2030J/mol or 113kJ/kg and the output temperature becomes 411K.

The UPR α of compressing 1kg of vapour with a compression ratio of 1.5 is thus:

From technosphere


2.3.2 Mechanical Compression- Multi stage

With multistage compression, the pressure to which gases can be compressed is much higher than

with single stage compression. Another reason for applying multistage compression is that the

same compression task can be realized with lower energy use. Multistage compression is a

sequence of compressions. After each compression stage the heat that is generated in the

compression is removed by cooling, making multistage compression less adiabatic and more

http://www.cheresources.com)/

48

isothermal. The choice of the compressor type for each compression step depends mainly on the

flow rate and the differential pressure (Damen, 2007). The theoretical energy requirements can

be estimated, however. This basic engineering module calculates the estimated energy

requirements for multistage compression of gases.

System boundary

The system boundary of this BUO includes the multistage compression of a gas. Electricity is

needed for the compressor. The system boundary includes the pumping and cooling

requirements.


For a multistage compression with n stages, power requirements are given by following equation

(Diomedes Christodoulou, 1984):

11

1

1

21n

p

pn

M

ZRTW

In which:

W = Specific compression work (J/g or kJ/kg)

Z = Compressibility factor (Dimensionless)

R = Universal gas constant i.e. 8.314 J mol-1K-1

T1 = Suction temperature (K)

n = Number of stages (Dimensionless)

= Ratio of heat capacities, cp/cv (dimensionless)

M = Molar mass (g/mol)

p1 = Suction pressure (MPa)

p2 = Discharge pressure (MPa)

Since this is the theoretical power use of the multistage compression process, a correction has to

be made for efficiency losses:

mis

WP

Multistage

compression

System boundary

Pumping Cooling

System boundary

Gas

(M)

Compressed

gas (UO)

Electricity (En)

49

In which:

P = Power requirements (kJ/kg)

is = Isentropic efficiency (Dimensionless)

m = Mechanical efficiency (Dimensionless)


Input values required DATAPHYSCHEM DATABUO

Suction temperature

Number of stages

Suction pressure

Discharge pressure

Universal gas constant

Specific heat ratio

Molar mass

Compressibility factor

Isentropic efficiency

Mechanical efficiency

User output

Physical properties of the output stream (pressure, density)

UPR output

From technosphere:


Example

Koornneef (2010) has assumed multistage compression of carbon dioxide for injection in an

underground well in his study on carbon capture and storage. He has assumed the following

parameters:

Compressibility factor: 0.9942

Universal gas constant: 8.3145 J mol-1 K-1

Suction temperature: 313.15 K

Specific heat ratio: 1.293759

Molar mass: 44.01 g/mol

Suction pressure: 10.7 MPa

Discharge pressure: 15 MPa

Number of compression stages: 2

Isentropic efficiency: 80%

Mechanical efficiency: 99%

When inserting these values in the algorithms defined above, the specific compression work is

20.0 MJ/ton. The specific electricity requirement is 25.2 MJ/ton.

2.3.3 Pumping incompressible fluids

This module includes the pumping of incompressible fluids. Whilst in principle all fluids are

compressible, the compressibility of liquids is low, and for gases, incompressibility is often used

as an approximation at lower speeds.

System boundary

The electricity use is calculated to pump an incompressible fluid over a certain distance and

height. The calculation algorithm can be used for centrifugal, axial, rotary and reciprocating

pumps, for Newtonian and Power law fluids. Herschel – Bulkley can also be chosen, but larger

errors are expected, due to limited knowledge of their behavior in different pipe configurations.

Numerous assumptions are made in the equations: constant fluid density, the absence of thermal

energy effects; single phase, uniform material properties, uniform equivalent pressure (Valentas

et al., 1997).

50

Pump

α System boundary

Incompressible fluid (M) Incompressible fluid (UO)

β System boundary

Electricity (En)


The starting point to estimate the pump power is to determine the type of fluid. Three different

types of fluids are considered depending on the relation between the shear stress (σ) and the

shear rate (γ):

Newtonian fluids:

Power law fluids:

Herschel – Bulkley fluids:

With µ the viscosity, µ’ the plastic viscosity, K the consistency index, n the flow behavior index

and σ0 the yield stress. Often the Newtonian equations are used, but in many real cases such as

different types of slurries the Newtonian theory is not applicable. Even fibrous slurries such as

fermentation broths, fruit pulps, crushed meal animal feed, tomato puree, sewage sludge, and

paper pulp, which may not contain a high percentage of solids may flow as non-Newtonian

regimes (Abulnaga, 2002). Because of its ease of use, the empirical Power Law is often used (Rao,

1999).

Pump power

The basic equation to estimate pump power for transporting incompressible fluids is:

Pn = pump power output in W, H the total dynamic head in Nm/kg, Q the capacity in m³/s, ρ the

density (kg/m³), g the gravity constant and eff. the efficiency of the pump (Perry & Green,

1999).

The main bottleneck for the calculation of the necessary power is thus determining the dynamic

pump head to displace the fluid. This factor depends on the conditions at starting and end

position, on the flow rate, on the type and configuration of the pipes used, and on the type of

fluid that is pumped. The dynamic head can be obtained from the Bernoulli equation: (Valentas

et al., 1997).

With Δz the difference in height between the starting and end position, which equals zero in

closed systems. Δp is the pressure difference between initial and end situation, α is the

correction factor for velocity distribution in the pipe and hw is the resistance head.

Velocity head

The difference in velocity is represented by Δu, where the velocity behind the pump can be

calculated based on the flow rate and the cross sectional area of the pipes (A):

51

According to www.cheresources.com liquid transport pipes should be sized according to:

With D the diameter of the pipe (in m).

For circular pipes:

Filling this in in eq. links the flow (in m³/s) to the diameter (in m):

This can be solved if the flow is known.

α is the correction factor for velocity distribution in the pipe. For the turbulent flow this is

always approximated as 2, whilst for laminar flow it depends on the type of fluid (Ibarz &

Barbosa-Cánovas, 2003):

Newtonian fluids: α=1

Power law fluids (using the flow behavior index n)

Herschel – Bulkley fluids: The correction factor can be deducted from Figure 8:

Figure 8: The kinetic energy correction factor for Herschel-Bulkley fluid foods (Valentas et al., 1997)

52

The dimensionless yield stress (c):

σ0 = the yield stress, σw the shear stress at the wall and ΔP/L the pressure drop per unit of length.

Resistance head

hw is the resistance head (m), which consists out of a basic friction resistance, with f the fanning

friction factor, and a factor kf accounting for supplementary losses, with b the amount of fittings,

valves, elbows,...

This resistance factor depends heavily on the flow regime. Therefore the Reynolds number has to

be determined, which can be calculated for Power Law fluids by:

For fluids without yield stress, the critical Reynolds number is determined with:

When the magnitude of n < 1 the fluid is shear-thinning or pseudoplastic, and when n > 1 the fluid

is shear thickening or dilatant in nature (Rao, 1999). For the special case of a Newtonian fluid (n

= 1), the consistency index K is identically equal to the viscosity of the fluid. Thus for Newtonian

fluids this becomes:

And the critical Reynolds number is 2100, meaning that a Reynolds numbers lower than this value

are laminar. Reynold numbers above 4000 are situated in the turbulent region. For the

intermediate region (2100<Re<4000), it is impossible to obtain the pumping equations, thus an

approximation should be made. Often Reynolds numbers above the critical number (2100 for

Newtonian fluids) are assumed to be in the turbulent region.

When a fluid has a non negligible yield stress, the Herschel – Bulkley theory should be used. The

critical Reynolds number can then be calculated by:

With HeG the Hedstrom number (Ibarz & Barbosa-Cánovas, 2003):

53

The critical Reynolds number can be obtained graphically, however, in this work the Herschel-

Bulkley fluids are only used in the laminar region, which is realistic due to the high viscosity and

elasticity of these fluids.

Fanning friction factor

Laminar flow

Since the Herschel – Bulkley equation is the general form of the three previous equations, this

will be taken as a starting point, where the friction factor can be calculated as:

With

For power law and Newtonian fluids c=0, making Ψ=1 and thus the friction factor can be

simplified to:

For turbulent flow, it is thus assumed that the fluids are obeying Newton, or Power law

equations.

Turbulent flow in smooth pipes:

For Power Law fluids the Dodge and Metzner (1959) equations give good results for (n = 0.4 to 1):

This equation is simplified to the Von Karman equation for Newtonian fluids (n=1) (Chhabra &

Richardson, 1999):

For turbulent flow in rough pipes, the roughness of the pipe has an influence. However, for non

Newtonian flows, these relationships are not well studied.

The Torrance equation is used for fluids with n<1 (Liu, 2003).

D represents the inner diameter and ε the absolute roughness which depends on the material

used. Default values can be found in DATABUO (Table 10) (Van Der Meeren, 2004). For other fluids

it is stated that the equations valid for Newtonian fluids can be used as an approximation, since

54

the turbulence becomes more important (Abulnaga, 2002). A popular equation for Newtonian

fluids is given by the Colebrook-White equation (Perry & Green, 1999):

Table 10: Default roughness factors of different piping material

Type Absolute roughness ε in m

PVC, plastic, glass 0

Drawn tubing 0.0000015

Commercial steel and wrought iron 0.000045

Asphalted cast iron 0.00012

Galvanized iron 0.00015

Cast iron 0.00026

Wood stave 0.00018-0.00092

Concrete 0.00030-0.0030

Riveted steel 0.00092-0.0092

Supplementary losses

The second term in friction factor accounts for supplementary losses. In general these factors are

determined experimentally for Newtonian fluids and due to insufficient data they are also used

for non-Newtonian fluids (Perry & Green, 1999; Chhabra & Richardson, 1999). In general two

options exist. For elbows, gates, valves, equivalent lengths are used:

With equivalent lengths (in m) taken from Table 11 (Brannan, 2002):

55

Table 11: Equivalent lengths for different piping parts

Nom

inal pip

e d

iam

ete

r cm

Glo

be

valv

e

or

ball

check

valv

e

Angle

valv

e

Sw

ing c

heck v

alv

e

Plu

g c

ock

Gate

or

bal valv

e 45° e

llbow

Short

ra

diu

s

elb

ow

Long

radiu

s

elb

ow

Hard

T

Soft

T

90

mit

er

bends

Weld

ed

Thre

aded

Weld

ed

Thre

aded

Weld

ed

Thre

aded

Weld

ed

Thre

aded

Weld

ed

Thre

aded

2 m

iter

3 m

iter

4 m

iter

3.81 16.76 7.92 3.96 2.13 0.30 0.30 0.61 0.91 1.52 0.61 0.91 2.44 2.74 0.61 0.91

5.08 21.34 10.06 5.18 4.27 0.61 0.61 0.91 1.22 1.52 0.91 1.22 3.05 3.35 0.91 1.22

6.35 24.38 12.19 6.10 3.35 0.61 0.61 - 1.52 - 0.91 - 3.66 0.91 -

7.62 30.48 15.24 7.62 5.18 0.61 0.61 1.83 1.22 4.27 1.22

10.16 39.62 19.81 9.75 9.14 0.91 0.91 2.13 1.52 5.79 1.52

15.24 60.96 30.48 14.63 21.34 1.22 1.22 3.35 2.44 8.53 2.44

20.32 79.25 38.10 19.51 36.58 1.83 1.83 4.57 2.74 11.28 2.74

25.4 100.58 48.77 24.38 51.82 2.13 2.13 5.49 3.66 14.33 3.66

30.48 121.92 57.91 28.96 51.82 2.74 2.74 6.71 4.27 16.76 4.27 8.53 6.40 6.10

35.56 137.16 64.01 32.00 24.38 3.05 3.05 7.92 4.88 18.90 4.88 9.75 7.32 6.71

40.64 152.40 73.15 36.58 44.20 3.35 3.35 8.84 5.49 21.95 5.49 11.58 8.23 7.32

45.72 167.64 85.34 42.67 48.77 3.66 3.66 10.06 6.10 24.99 6.10 12.80 9.14 8.53

50.8 198.12 91.44 47.24 64.01 4.27 4.27 10.97 7.01 27.43 7.01 14.02 10.06 9.75

55.88 209.70 102.11 51.82 68.58 4.57 4.57 12.19 7.62 30.48 7.62 15.85 10.97 10.36

60.96 228.60 112.78 56.39 77.42 4.88 4.88 13.41 8.23 33.53 8.23 17.07 11.89 10.97

76.2 95.10 6.40 6.40 16.76 12.19 42.67 12.19 21.34 15.54 13.41

91.44 7.62 7.62 20.12 14.33 51.82 14.33 25.60 18.29 15.85

106.68 9.14 9.14 23.47 16.76 60.96 16.76 29.87 21.03 19.51

121.92 10.67 10.67 26.82 19.81 67.06 19.81 34.14 24.69 21.95

137.16 12.19 12.19 30.18 21.34 76.20 21.34 38.40 27.43 24.38

152.4 13.72 13.72 33.53 24.38 79.25 24.38 57.91 30.18 28.04

For enlargements and narrowings, a dimensionless coefficient is used:

With kf a dimensionless loss coefficient, which depends on several parameters for different

occasions (Van Der Meeren, 2004):

From pipe to reservoir Kinetic energy gets lost = 1 independent of geometry

From reservoir to pipe

o Well rounded inlet kf = 0.04

o Chamfered inlet kf =0.25

o Square edge inlet kf =0.5

o Inward projecting pipe kf =1

For abrupt pipe enlargement (d1<d2)

56

For gradual pipe enlargements values from Table 12 can be chosen.

Table 12: Dimensionless loss coefficient for gradual pipe enlargements

D2/D1

Angle of cone °

2 6 10 15 20 25 30 35 40 45 50 60

1.1 0.01 0.01 0.03 0.05 0.10 0.13 0.16 0.18 0.19 0.20 0.21 0.23

1.2 0.02 0.02 0.04 0.09 0.16 0.21 0.25 0.29 0.31 0.33 0.35 0.37

1.4 0.02 0.03 0.06 0.12 0.23 0.30 0.36 0.41 0.44 0.47 0.50 0.53

1.6 0.03 0.04 0.07 0.14 0.26 0.35 0.42 0.47 0.51 0.54 0.57 0.61

1.8 0.03 0.04 0.07 0.15 0.28 0.37 0.44 0.50 0.54 0.58 0.61 0.65

2.0 0.03 0.04 0.07 0.16 0.29 0.38 0.46 0.52 0.56 0.60 0.63 0.68

2.5 0.03 0.04 0.08 0.16 0.30 0.39 0.48 0.54 0.58 0.62 0.65 0.70

3.0 0.03 0.04 0.08 0.16 0.31 0.40 0.48 0.55 0.59 0.63 0.66 0.71

∞ 0.03 0.05 0.08 0.16 0.31 0.40 0.49 0.56 0.6 064 0.67 0.72

Abrupt pipe narrowing (d1>d2)

For non cylindrical pipes

D = De with

Efficiency

The pump efficiency (Eff.) can be calculated by (Brannan, 2002):

With F the developed head (in ft) and G the flow (in GPM). To convert to SI units (head H in

meters and flow rate Q in m³/s):

The equation is valid for F=50-300ft and G=100 – 1000GPM

If this is not the case, default values can be used (Table 13) (www.cheresources.com):

http://www.cheresources.com/

57

Table 13: Default pump efficiencies for pumping incompressible fluids

Pump type flow m³/s Efficiency

Centrifugal pump

0.0063 45%

0.0315 70%

0.63 80%

Axial pump all 75%

Rotary pump all 65%

Power (kW)

Reciprocating pump

7.46 70%

37.3 85%

373 90%



Mass (flow rate) and type of fluid

Starting and end temperature and pressure

Type pipe material, distance of straight

pipes, height difference between starting

and end point, number and type of turns,

valves, enlargements and narrowings

Friction coefficients

and roughness factors

Efficiencies

Fluid characterisation factors

User output

-

UPR output

From technosphere:

Electricity use

Example

A Newtonian fluid is pumped at a rate of 2m³/s. Total height difference between starting and end

point is 3m and total pipe length is 20m. Starting and end pressure are equal and the end velocity

is zero. If the pipe is made of riveted steel and if the fluid is pumped via 2 valves, 2 short radius

elbows and 2 long radius elbows, the power use of a rotary pump is 441 kWh/h.

The UPR of pimping 1m³ fluid over this distance becomes:

From technosphere


2.3.4 Pumping incompressible fluids through packing

This module includes the pumping of incompressible fluids through packings, beds or filters. It

can thus be used for pumping operations such as adsorption, filtration, catalytic beds, etc.

However, only the electricity use is modeled of the pumping operation. No interactions between

the fluid and the bed is included.

System boundary

The calculations in this module can be widely applied, for several unit operations where beds and

packings are involved. However, the equations are only valid if the characteristics of the bed

remain constant and if no fluidization occurs. Especially the porosity should be constant (Van Der

Meeren, 2004). For example clogging filters are out of the range of this work. The module can be

58

coupled to the pumping operations in 4.3.2. since they only include the pumping through the

bed. The same pumps and pump efficiencies are included as for the regular pumping module.

Pumping through packing

α System boundary

Incompressible fluid (M) Incompressible fluid (UO)

β System boundary

Electricity (En)


The approach is similar to pumping incompressible fluids. However, on top of the regular friction

term, an additional head loss is caused by the packing material. To account for this, the approach

from Chhabra & Richardson (1999) can be used where the bed head loss is added to the modified

Bernoulli equation:

Which can be used in:

The head loss of the bed (in m) can be calculated from the pressure drop:

With:

With ε the pore volume/bed volume, u the superficial velocity (m/s), L the length of the bed (m),

ρ the density of the fluid (kg/m³), dp is the sphere diameter (m). The latter depends strongly on

the particle shape:

With Г the sphericity of the particles and dr the equal volume sphere diameter (Table 14) (Ibarz &

Barbosa-Cánovas, 2003; McCabe et al., 2004)

59

Table 14: Sphericity of different packing particles

Form of the particle Sphericity (Г)

Sphere 1

Cube 0.81

Cylinders

h=d

h=5d

h=10d

0.87

0.70

0.58

Discs

h=d/3

h=d/6

h=d/10

0.76

0.60

0.47

Beach sand As high as 0.86

River sand As low as 0.53

Other types of sand 0.75

Triturated solids 0.5-0.7

Granulated particles 0.7-0.8

Wheat 0.85

Raschig rings 0.26-0.53

Berl saddles 0.3-0.37

Coal dust 0.73

Mica flakes 0.28

Crushed glass 0.65

Cf is a correction factor to account for the fact that the packing material is more dense in the

center of the column and less dense near the wall. It can be calculated for cylindrical vessels:

With D the vessel diameter

fbed is the friction factor of the bed, which can be obtained from the Ergun equation:

With the Reynolds number for Power law fluids:

It is stated that this equation is a good approximation for ε ≤ 0.41 and Re*<100.

For ε > 0.41 and Re*>100

With

60



Mass (flow rate) and type of fluid

Type and size of packing material

Pore volume/bed volume (should remain

constant)

Sphericity

Pump efficiency Fluid characterisation factors

User output

-

UPR output

From technosphere:

Electricity use

Example

Pumping 0.5m³/s water through a vertical packed bed of 2 m length with mica flakes of 1cm and

a porosity of 0.4 requires a rotary pump shaft power (65% efficiency) of 268kW.

The UPR α of pumping 1m³ water through this bed is thus:

From technosphere

0.15 kWh electricity, medium voltage

2.3.5 Agitation and mixing of liquids and suspensions

Agitation and mixing are operations often needed in process industry and relying on a certain

amount of mechanical energy which depends on many different parameters. This module includes

mixing two or more liquids or suspensions and agitation of one or more liquids or suspensions.

Whilst these two operations might not exactly have the same purpose, their power consumption

can be modelled with the same equations.

System boundary

The mixing and homogenization of liquids operation includes the electricity use necessary for the

impeller. Several types of propellers and turbines are included, plus a paddle with 2 blades, an

anchor and a helical impeller. Equations are available for Newtonian and Power Law fluids, the

latter thus also including pseudoplastic and dilatants suspensions. Furthermore, the effect of gas

bubbling can be included. However, the power for the gas bubbling and pumping operations are

SUO in the β system boundary.

61

Agitation and mixing

α System boundary

Feed (M) Agitated or mixed feed (UO)

β System boundary

Pumping

Electricity (En)

Bubbling


Newtonian fluids

One of the most important factor for the power requirement is the Reynolds number. Similarly to

pumping fluids, a difference can be made between Newtonian and Non-Newtonian fluids. In the

latter case the main complication is related to the shear rate (γ). The calculation in the two

situations is very similar, starting from the Reynolds number (McCabe et al., 2004):

With N the rotational speed (rps), DA the impeller diameter (m), µa the apparent viscosity (Pa.s)

and ρ the density of the fluid (kg/m³).

Newtonian fluids

For Newtonian fluids: µa = µ

The standard equation for the total power needed for mixing and homogenization (P in W) is

calculated by:

This is valid in for intermediate Reynolds numbers between 100<NRe<10000, whilst for the

turbulent region, NRe>10000, the modified version is used:

In the laminar region the viscosity and shear become more important, thus the formula is

converted to:

Power numbers for different flow regimes and impellers can be found in Table 15.

62

Table 15: Power numbers for newtonian fluids in the laminar (KL), intermediate (P0) and turbulent region (KT) for different types of impellers (McCabe et al., 2004)

typical

viscosity (cp) Type KL P0 KT

<2000 Average small propeller (3 blades) 41.00 0.75 0.32

<2000 Average small propeller with pitch of 2 (3

blades) 43.50 0.75 1.00

<2000 Average large propeller (3 blades) 41.00 0.75 0.32

<2000 Average large propeller with pitch of 2 (3

blades) 43.50 0.75 1.00

<20000 Turbine 6 flat blades 71.00 5.00 6.30

<20000 Turbine 6 curved blades 70.00 5.00 4.80

<20000 Fan turbine 6 blades 70.00 5.00 1.65

<80000 Paddle (2 blades) 36.50 2.60 1.70

<100000 Anchor 300.00 10.00 0.35

<1000000 Helical impeller 300.00 10.00 0.35

>1000000 Extruders. roll mill. etc

Non Newtonian fluids

For non Newtonian fluids, the apparent viscosity should be used in the Reynolds number

(Heldman & Lund, 2007):

For dilatants liquids (Perry & Green, 1999)

For pseudoplastic and Bingham fluids

For turbines 10 can be replaced by 11.5.

The Power Number can then be found in Figure 9 (Heldman & Lund, 2007) for different types of

impellers and it can be applied in:

63

Figure 9: Power number correlations

Vessel design

The diameter of the impeller is estimated from the volume of the cylindrical tank, on its turn

calculated from the flow (Q in m³/s), and the residence time (t in s):

A certain excess volume is added (Ex. in %) to add a buffer volume:

Standard this excess volume is set on 10%.

A cylindrical vessel is assumed with H = Dt with H the height of the tank and Dt the diameter of

the tank:

64

Furthermore, we assume that the diameter of the impeller is 1/3rd of the tank diameter:

Rotational speed and mixing time

For mixing operations, the residence time and impeller speed are 2 interlinked parameters which

therefore cannot be fixed by default values. According to Herbert et al. 1994 (a dimensionless

mixing time Θ can link the mixing time (t) with the impeller speed for baffled agitated vessels

with a centrally located impeller:

With NP the power number. Typical N values for the different impellers are presented in Table 16

and typical power numbers can be found in Table 15 for laminar, turbulent and intermediate

region.

Table 16: typical rotational speeds for different impellers in mixing vessels (McCabe et al., 2004)

Type Typical N ranges for mixing

Average small propeller (3 blades) 1150-1750

Average small propeller with pitch of 2 (3 blades) 1150-1751

Average large propeller (3 blades) 400-800

Average large propeller with pitch of 2 (3 blades) 400-801

Turbine 6 flat blades 200-400

Turbine 6 curved blades 200-400

Fan turbine 6 blades 200-400

Paddle (2 blades) 20-150

Anchor 50-350

Helical impeller 5-20

The rotational speed of disc turbine blades applied in Newtonian fluids can be estimated by using

a scale of agitation SA based on the pumping number (NQ) which can be found in Table 17

(Chhabra & Richardson, 1999):

The scale of agitation ranges from 1, which is mildly mixed, to 10, which can be taken for intense

mixing.

65

Table 17: Default pumping numbers for types of impellers

Type Pumping number (NQ)

Average small propeller (3 blades) 0.5

Average small propeller with pitch of 2 (3 blades) 0.5

Average large propeller (3 blades) 0.5

Average large propeller with pitch of 2 (3 blades) 0.5

Turbine 6 flat blades 0.7

Turbine 6 curved blades 0.8

Fan turbine 6 blades 0.8

Paddle (2 blades) 0.6

Anchor 0.5

Helical impeller 0.5

For gassed liquids, the power (Pg) for mixing and homogenization can be calculated by:

With σ the surface tension (N/m) of the liquid and NQ the pumping number.

Alternatively an adjusted power number (Np.g) can be calculated:

With Qt the volumetric gas flow rate (kg/s).



Mass flow and type of feed(s)

Type of impeller

Impeller speed and residence

time

Tank volume

Power number and pumping

number

Impeller speed and residence

time information

Physicochemical properties of

mixture

User output

Tank volume

Residence time

UPR output

From technosphere:

Electricity use for the impeller

Example

Considering a fermentation tank with a total volume of 54.2m³ (with an excess volume of 10%).

The used diameter of the impeller is thus 1.37m. When taking the viscosity and density of water

as an approximate, and an impeller speed of 10 rpm for a default fermentation, the Reynolds

number is in the turbulent region (374908). For a turbine with 6 flat blades the power number is

6.3, meaning that the total power required becomes 143kWh/h.

66

The UPR α of the agitation of 1m³ fermentation medium during 1 hour is:

From technosphere:



In the BREW study (Patel et al., 2006) literature values for agitation have been collected ranging

from 0.1-12 kW/m3 of medium. The recommended value was determined at 0.5 kW/m3

2.3.6 Comminution

Comminution is a process which is applied to reduce the size of solids and is widely used in

industry (e.g. in food processing, minerals processing, ceramic industry and so on). The purposes

of comminution are to liberate certain compounds for concentration processes, to reduce the

size or to increase the surface area. More recent technologies result in the need to modify the

surface of solids, prepare composite materials and to recycle the useful components of industrial

waste. The energy efficiency of comminution is very low and the energy required for

comminution increases with a decrease in feed or produced particle size. In design, operation

and control of comminution processes, it is necessary to correctly evaluate the comminution

energy of solid materials (Kanda & Kotake, 2007). In this basic engineering module, the energy

for comminution is determined.

System boundary

The system boundary of this BUO includes the comminution of a feed in to a grinded product.

Electricity is required for this process. No additional processes are assumed in the system

boundary.


Several theories have been developed to estimate the energy requirement of size reduction

processes. They are in fact all based on the basic assumption that the energy required to produce

a change dL in a particle of typical size dimension L is a simple power function of L:

nLKdLdE /

Where dE is the differential energy required, dL is the change in a typical dimension, L is the

magnitude of a typical length dimension and K and n are constants.

The most widely applied are the theories from Kick, Rittinger and Bond. We will now discuss each

of them (Earle & Earle, 2004).

Kick’s law

Comminution Feed (M)

System boundary

System boundary

Grinded product

(UO)

Electricity (En)

67

Kick assumed that the energy required to reduce a material in size is related to the change in

particle diameter, i.e. to the ratio of the diameter of the particles before and after

comminution. In this case ‘n’ in the above equation is -1. The following formula is known as

‘Kick’s law’:

2

1lnL

LfKE cK

In which:

E = Energy for size reduction (J/kg)

KK = Kick’s constant (m3/kg)

fc = Compressive strength of the material (N/m3)

L1 = Diameter of the feed particles (m)

L2 = Diameter of the product particles (m)

Kick’s law is mainly used for comminution of coarse particles.

Rittinger’s law

Rittinger, on the other hand, assumed that the energy for size reduction is directly proportional

to the change in surface area and not to the change in length dimensions. This means that ‘n’ in

the above equation is -2, resulting in ‘Rittinger’s law’:

12

11

LLfKE cR

In which:

KR = Rittinger’s constant (m4/kg)

Rittinger’s law is mainly used for comminution of fine particles.

Bond’s law

Bond has suggested an intermediate course, in which he postulates that ‘n’ is -3/2. This leads to:

qLEE i

11

100

2

and

2

1

L

Lq

In which:

E = Energy for size reduction (kWh/tonne)

Ei = Work index (kWh/tonne)

L1 = Feed particle size ( m)

L2 = Product particle size ( m)

Bond defines work index Ei as the amount of energy required to reduce a unit of mass of the

material from an infinitely large particle size down to a particle size of 80% passing 100 m (Note

that therefore L1 and L2 have to be expressed in m as well!). Table 18 gives an overview of

typical values for the work index for various materials (Perry & Green, 1999).

Table 18: Work Index for various materials in kWh/tonne (Perry & Green, 1999)

68

In literature, data for Bond’s work index are readily available, while this is not the case for

Rittinger’s and Kick’s constants. Since Bond’s law is known to be a general law that is

intermediate to Rittinger’s and Kick’s law, we propose to use Bond’s law in comminution

calculations.


Input values required DATAPHYSCHEM DATABUO

Feed particle size

Product particle size

Compressive strength of the material

Rittinger’s constant of the material

Kick’s constant of the material

Work index of the material

-

User output

-

UPR output

From technosphere:


Example

In the first example, we assume Kick’s law for grinding particles of an unspecified substance from

45 mm down to 4 mm. The compressive strength of this material is 22.5 N/m2 and Kick’s constant

is 239 m3/kg. The energy use for size reduction, hence, is 13.0 kJ/kg.

In the second example we will assume Rittinger’s law for grinding limestone particles with a

diameter of 6 mm down to a size of 0.4 mm. The compressive strength of this material is 70 N/m2

and Rittinger’s constant is 0.013 m4/kg (Backhurst & Harker, 2002). Using Rittinger’s law, the

energy requirement for comminution is 8.95 kJ/kg.

69

In the third example, we assume Bond’s law for crushing the limestone from the previous

example. The work index for limestone is 11.61 kWh/tonne (See Table 18). Applying these values

in the equation for Bond’s law, yields an energy use of 4.3 kWh/tonne or 15.5 kJ/kg.

2.3.7 Fluidization

Fluidization occurs when a bed of granular material is converted from solid to a fluid or

suspended state through the velocity of a gas or liquid. It thus occurs at a certain velocity, where

a minimum fluidization velocity is necessary to achieve the fluidized state, higher velocities will

result in the transport of the particles, and thus in (pneumatic in the case of a gas) conveying.

Fluidization is often used in the process industries during cracking, toasting, roasting( pyrite.

lime. coffee), drying (grains, sugars), freezing, heating with sand baths, encapsulation and

agglomeration of particles, etc. (Van Der Meeren, 2004).

System boundary

This BUO includes the mechanical energy (electricity) to obtain a fluidized state. The relationship

is based on the pressure drop in the bed and is thus valid for fluidization and conveying the

solids. However, it only accounts for obtaining a static fluidized state. To really lift and move the

solids, as is the case in conveying, additional energy is required which is supplied by the fluid.

This additional demand should be calculated with the SUO ‘pumping’.

Fluidization

α System boundary

Fluid (M)

Fluidized bed (UO)

β System boundary

Pumping

Electricity (En)

Solid particles (M)


The basic equation to estimate pump power for fluidization and pneumatic conveying is (Van Der

Meeren, 2004):

With Pn = pump power output in W, Q the capacity in m³/s.

The pressure drop (Pa) over the bed can be calculated by (Richardson, Harker, & Backhurst,

2002):

ρs is the density of the particles and ρf is the density of the fluid (kg/m³). L is the length of the

bed (m), g is the gravity constant (9.81m/s²), whilst the value of ε represents the porosity. If this

70

value is not known, the user can calculate the minimum porosity for fluidization where ε = εmf

(Ibarz & Barbosa-Cánovas, 2003):

With dp the diameter of the particles in µm. This equation is valid for particle sizes going from 50

to 500µm.



Mass flow and type of feed(s)

Diameter of particles

- Densities

User output

-

UPR output

From technosphere:

Electricity use

Example

A water flow of 1m³/s holds a fluidized bed of m length with particles of 500µm and a density of

1500kg/m³. The minimal porosity becomes 40%. The pressure drop is 5886 Pa and the required

power 5.886kW.

The UPR α of maintaining the solids in a fluidized state by water for 1 h is thus:

From technosphere


2.3.8 Conveying solids

Conveying is a frequently used application to transport solids.

System boundary

This BUO includes the electricity to move solids over a certain distance with 4 types of conveyors:

Screw conveyors

Belt conveyors

Centrifugal-discharge buckets on belt

Continuous buckets on a chain

71

Conveying

α System boundary

Solids (M) Solids (UO)

β System boundary

Electricity (En)


Conveying solids requires a certain amount of power which can be found in the Tables 21.6-21.9

(Perry & Green, 1999) for different types of conveyors for certain capacity flow rates.

72

73



Mass flow

Type of conveyor Power of equipment -

User output

-

UPR output

From technosphere:

Electricity used

Example

10 tons/h wood chips are transported with a screw conveyor over a distance of 23 m. This

operation needs a motor of 2.76kW.

The UPR of conveying 1kg wood chips is:

From technosphere


2.3.9 Fans, Blowers & vacuüm pumps

Fans and blowers are used to create a driving force to supply a gas, usually air. Examples of

applications are ventilation and air blowing in a combustion chamber. A vacuum pump works

oppositely, by lowering the pressure.

System boundary

This BUO includes the electricity use of a fan or blower used to increase the pressure of the gas

or the electricity of a vacuum pump, to remove a gas from a certain space.

74

Fan / Blower / Vacuum pump

α System boundary

Gas (M) Gas (UO)

β System boundary

Electricity (En)


The power (P in W) needed can be calculated by (Perry & Green, 1999):

Where Q is the flow rate (m³/s), ΔP is the pressure difference in Pa, and eff is the efficiency of

the device.

Typical pressure differences for vacuum pumps, fans and blowers can be found in Table 19 (Perry

& Green, 1999; Brannan, 2002; Silla, 2003), whilst typical efficiencies can be found in Table 20

(Bureau of Energy Efficiency India).

Table 19: Typical pressure differences covered in vacuum pumps, fans and blowers

Type of device Typical pressure differences (Pa)

Vacuum pump 100000

Fans 7500

Blowers 50000

Table 20: Default efficiencies

Type of fan Efficiency

Centrifugal fan/blower

Airfoil, backward curved/inclined 81

Modified radial 75

Radial 72

Pressure blower 63

Forward curved 62

Axial fan

Vane axial 81

Tube axial 69

Propeller 47

Other

Other 80



Mass flow

Type of device

Typical pressure differences

Efficiencies -

75

User output

-

UPR output

From technosphere:

Electricity used

Example

A radial centrifugal fan (72% efficiency) used for the ventilation of a room displaces an air stream

of 1m³/s with a pressure increase of 7500 Pa. The power of the equipment is 10.4kW.

The UPR α for pumping 1m³/s is thus:

From technosphere:

0,0029kWh electricity, medium voltage

2.4 Utilities

2.4.1 Heating

In many industrial processes, liquid substances are heated to a certain temperature and kept at

that temperature for a given amount of time. These liquids can be pure chemicals, mixtures,

solutions or suspensions. Energy is needed to heat the liquid to the required temperature but also

to compensate for heat losses through the wall of the vessel in which it is heated. The latter is

specifically relevant when a certain temperature has to be retained for a longer period. In this

basic engineering module, we will estimate the energy required for heating a liquid in a vessel

for a given amount of time.

System boundary

The α system boundary for this process includes heating the liquid in a vessel. If, pumping or

mixing are required, the energy use can be estimated with the algorithms of those processes as

defined in the respective basic engineering modules for agitation or pumping. The β system

boundary includes the production of the energy carrier that is needed to heat the liquid. This

could for example be steam or natural gas.


For this process, energy is needed for heating the liquid up to a required temperature and

maintaining this temperature for a certain amount of time. The energy needed for heating to a

certain temperature depends on the specific heat of the liquid and can be calculated with:

Heating liquid in

Vessel

System boundary

System boundary

Heating

(En)

Feed

(M)

Heated feed

(UO)

Energy

carrier

76

0TTmcQ eph

In which:

Qh = Energy needed for heating (J)

cp = Specific heat of the liquid (J kg-1K-1)

m = mass of the liquid (kg)

T0 = Initial temperature of the liquid

Te = End temperature of the liquid

Vessels will have walls that are insulated with isolation material (e.g. polyurethane) to prevent

the loss of heat through the walls. However, insulation will never be 100% and some heat is lost

resulting in a decrease of the temperature of the liquid. Extra heating is required to compensate

for this heat loss. The required heat is determined with (Blok, 2007):

d

tATQr

In which:

Qr = Energy required for remaining the temperature at Te (J)

λ = Thermal conductivity of vessel wall material (W m-1K-1)

ΔT = Temperature difference across the wall (K)

A = Surface area of the vessel wall (m2)

t = Period of remaining temperature at Te (s)

d = Thickness of the vessel wall (m)

The heat required for the two processes just described is the theoretical minimum amount of

energy needed. There will be an efficiency loss from burning a fuel to transmission of heat. In

order to calculate the total amount of energy needed (Qt), we have to account for the heating

efficiency (η) :

rht

QQQ



Mass of the liquid Specific heat of the liquid Heating efficiency

End temperature of the liquid Thermal conductivity of vessel wall

material

Initial temperature of the liquid

Surface area of the vessel wall

Thickness of the vessel wall

Period of remaining Te

Temperature difference across the vessel

wall

User output

Heating energy required (in J) for a given amount of liquid

UPR output

From technosphere:

Amount of heat from, e.g., natural gas. For example: “Heat, natural gas, at boiler

atmospheric non-modulating < 100kW”.

77

Example

We consider the following situation; Water is heated in a vessel from room temperature up to 80 oC. It is kept at this temperature for 1 hour. The vessel has the dimensions H = D = 1 m. A

polyurethane layer with a thickness of 10 cm insulates the vessel. In this situation the following

parameter values apply:

Specific heat of water: 4.18 kJ/(kg.K)

Difference initial (20 oC) and final temperature: 60 K

Temperature difference across the vessel wall: 60 K

Mass of the water: 783.8 kg

Thermal conductivity of polyurethane: 0.014 (W m-1K-1)

Thickness of the vessel wall: 0.1 m

Surface area of the vessel walls: 4.71 m2

Period of remaining the temperature: 3600

With these parameter values the required heat for heating up the water (Qh) is 197 MJ. The heat

required to keep the temperature at 80 oC for one hour (Qr) is 0.143 MJ. If a heating efficiency of

95% is taken into account, the total amount of fuel needed (Qt) is 207.1 MJ.

2.4.2 Cooling

System boundary

Cooling calculates the amount of cooling medium required to cool/condensate a certain feed

stream. The electricity use for pumping the cooling medium should be added with the SUO

‘pumping’. This is not a separation process, thus in the case of a mixture, all components are

assumed to end in the same phase at the same temperature and pressure.

Cooling

α System boundary

Feed (M) Cooled feed (UO)

β System boundary

Pumping


The needed cooling energy (q) can be calculated with:

With λ the latent heat of the condensed feed stream, cp the mean heat capacity of the feed in

gas (g) and liquid (l) phase , Tb the boiling temperature of the mixture, Tf the temperature of the

feed, and Te the end temperature.

The amount of cooling medium can then be calculated by:

With cp the heat capacity of the cooling medium and ΔT the temperature difference of the

cooling medium before and after use.

78



Mass flow

In and output temperature -

Boiling temperature

Heat capacities and latent heats

User output

Amount of cooling medium required

UPR output

Electricity use is calculated with the SUO ‘pumping incompressible fluids’.

Example

Cooling a water stream of 1kg/s from 90 to 20 degrees requires 0,034 kg/s refrigerant R134a

which is heated 10°C.

2.4.3 Steam generation

Steam is used in many chemical processes as a heat source. To produce steam, water is

evaporated with heat coming from the incineration of a fuel such as natural gas. In this basic

engineering module we will determine the heat requirements for the production of steam and the

energy that can be obtained when using steam as heat medium.

System boundary

The system boundary of this BUO includes the evaporation of water by means of an externally

supplied heat source. The water is pre-treated with chemicals in order to remove impurities and

hence avoid foaming and scaling, resulting in lower boiler efficiency, more maintenance, lower

boiler life and other problems (Spirax-Sarco Limited, 2001). The required chemicals have not

been taken into account in the system boundary but are part of the system boundary. This

also holds for the pumping requirements for returning condensate.


The energy for heating water to its evaporation temperature, for evaporation of water as well as

the energy required to further heat steam depends on the pressure of the liquid and the steam.

The pressure at which a liquid at a certain temperature evaporates is called the ‘saturated

pressure’. In this basic engineering module, we will assume ‘saturated steam (i.e. at the boiling

temperature at saturated pressure)’ as a default.

Steam used for industrial purposes will mostly be applied in closed systems. Figure 10 is a

schematic representation of such a system; water is evaporated to steam in a boiler at a certain

Steam

generation

System boundary

Water

(M)

Heat (En)

Steam

(UO)

System boundary

Condensat

e pumping Chemical

additives

Feedwater

pretreatm

ent

79

pressure and temperature, the steam is transported to the process in which it is needed, it

condenses back to water transferring heat to the process and the condensate is returned to the

boiler where it is again evaporated to steam.

Figure 10: Closed steam system

In such a system, the water that is evaporated to steam comes from the returned condensate

that has a heat content of hf in case no heat losses are assumed. With heat losses, however, the

enthalpy of the water arriving at the boiler is referred to as h0 (see algorithms below). In the

boiler it is evaporated to steam with a heat content of hg. The energy needed for generating and

delivering a kilogram of steam (useful heat, i.e. 100% of the heat directly used in a process) is

the enthalpy of evaporation (hfg) at a certain temperature and saturated pressure, corrected for

the efficiency of the boiler (η1) and the efficiency of steam distribution and heat transfer (η2).

The amount of energy that can be obtained from the steam is equal to the condensation enthalpy

(which is identical with the evaporation enthalpy):

In which:

Eg = Energy required for steam generation (kJ)

m = Mass of the steam (kg)

hfg = Enthalpy of evaporation (kJ/kg)

η1 = Boiler efficiency (dimensionless, generally around 0.9)

η2 = Efficiency of steam distribution and heat transfer (dimensionless, generally around 0.9)

The values for hfg, hf and hg are obtained from steam tables, published by thermodynamic

handbooks. Table 21 gives an overview of enthalpies at different temperature- and saturated

pressure levels.

In case no closed system is assumed and water has to be heated from room temperature to the

evaporation temperature or in case heat losses in the returned condensate are taken into

account, the algorithm is as follows:

In which:

Ef = Energy needed for heating the water (kJ)

m = Mass of the water (kg)

hf = Enthalpy of the saturated water (kJ/kg)

Boiler Process requiring

heat

Steam with enthalpy hg (= hf+hfg)

Condensate with

enthalpy hf

Evaporation

enthalpy

(hfg)

Condensation

enthalpy (- hfg)

Efficiency boiler = Conversion efficiency

including heat losses in

transportation system =

x

80

h0 = Enthalpy of the water arriving at boiler (kJ/kg)

The value of h0 depends on the pressure and temperature of the water as it arrives at the boiler.

It can be determined with the help of a steam calculator. See for example:

http://www.steamtablesonline.com/steam97web.aspx

For water at atmospheric pressure and a temperature of 20 oC, the specific enthalpy (h0) is 84.01

kJ/kg. In some cases, returned condensate is expanded to lower pressure to generate flash

steam. This flash steam can then be used to recover part of the heat of the initial condensate.

After use of flash steam the condensed water has to be reheated to its initial saturated pressure

and temperature for regeneration to high(er) pressure steam. As a result, the energy balance of

the total system is equal with or without the use of flash steam (assuming no extra heat losses).

As a rule of thumb, returned condensate after the use of flash steam has a temperature of 80 oC.

(US Department of Energy, 2006) At atmospheric pressure, h0 then is 335 kJ/kg.

http://www.steamtablesonline.com/steam97web.aspx

81

Table 21: Steam tables

Enthalpy, kJ/kg Enthalpy, kJ/kg Enthalpy, kJ/kg

Temp,

T oC

Sat

press,

Psat kPa

Sat

liquid,

hf

Evap.,

hfg

Sat

vapor,

hg

Temp,

T oC

Sat

press,

Psat kPa

Sat

liquid,

hf

Evap.,

hfg

Sat

vapor,

hg

Temp,

T oC

Sat

press,

Psat kPa

Sat

liquid,

hf

Evap.,

hfg

Sat

vapor,

hg

0.01 0.6117 0.001 2500.9 2500.9 130 270.28 546.38 2173.7 2720.1 260 4692.3 1134.8 1661.8 2796.6

5 0.8725 21.02 2489.1 2510.1 135 313.22 567.75 2159.1 2726.9 265 5085.3 1159.8 1633.7 2793.5

10 1.2281 42.022 2477.2 2519.2 140 361.53 589.16 2144.3 2733.5 270 5503.0 1185.1 1604.6 2789.7

15 1.7057 62.982 2465.4 2528.4 145 415.68 610.64 2129.2 2739.8 275 5946.4 1210.7 1574.5 2785.2

20 2.3392 83.915 2453.5 2537.4 150 476.16 632.18 2113.8 2746.0 280 6416.6 1236.7 1543.2 2779.9

25 3.1698 104.83 2441.7 2546.5 155 543.49 653.79 2098.0 2751.8 285 6914.6 1263.1 1510.7 2773.8

30 4.2469 125.74 2429.8 2555.5 160 618.23 675.47 2082.0 2757.5 290 7441.8 1289.8 1476.9 2766.7

35 5.6291 146.64 2417.9 2564.5 165 700.93 697.24 2065.6 2762.8 295 7999.0 1317.1 1441.6 2758.7

40 7.3851 167.53 2406.0 2573.5 170 792.18 719.08 2048.8 2767.9 300 8587.9 1344.8 1404.8 2749.6

45 9.5953 188.44 2394.0 2582.4 175 892.60 741.02 2031.7 2772.7 305 9209.4 1373.1 1366.3 2739.4

50 12.352 209.34 2382.0 2591.3 180 1002.8 763.05 2014.2 2777.3 310 9865.0 1402.0 1325.9 2727.9

55 15.763 230.26 2369.8 2600.1 185 1123.5 785.19 1996.2 2781.4 315 10556 1431.6 1283.4 2715.0

60 19.947 251.18 2357.7 2608.9 190 1255.2 807.43 1977.9 2785.3 320 11284 1462.0 1238.5 2700.5

65 25.043 272.12 2345.4 2617.5 195 1398.8 829.78 1959.0 2788.8 325 12051 1493.4 1191.0 2684.4

70 31.202 293.07 2333.0 2626.1 200 1554.9 852.26 1939.8 2792.1 330 12858 1525.8 1140.3 2666.1

75 38.597 314.03 2320.6 2634.6 205 1724.3 874.87 1920.0 2794.9 335 13707 1559.4 1086.0 2645.4

80 47.416 335.02 2308.0 2643.0 210 1907.7 897.61 1899.7 2797.3 340 14601 1594.6 1027.4 2622.0

85 57.868 356.02 2295.3 2651.3 215 2105.9 920.50 1878.8 2799.3 345 15541 1631.7 963.4 2595.1

90 70.183 377.04 2282.5 2659.5 220 2319.6 943.55 1857.4 2801.0 350 16529 1671.2 892.7 2563.9

95 84.609 398.09 2269.6 2667.7 225 2549.7 966.76 1835.4 2802.2 355 17570 1714.0 812.9 2526.9

100 101.42 419.17 2256.4 2675.6 230 2797.1 990.14 1812.8 2802.9 360 18666 1761.5 720.1 2481.6

105 120.9 440.28 2243.1 2683.4 235 3062.6 1013.7 1789.5 2803.2 365 19822 1817.2 605.5 2422.7

110 143.38 461.42 2229.7 2691.1 240 3347.0 1037.5 1765.5 2803.0 370 21044 1891.2 443.1 2334.3

115 169.18 482.59 2216.0 2698.6 245 3651.2 1061.5 1740.8 2802.3 373.95 22064 2084.3 0.0 2084.3

120 198.67 503.81 2202.1 2705.9 250 3976.2 1085.7 1715.3 2801.0

125 232.23 525.07 2188.1 2713.2 255 4322.9 1110.1 1689.0 2799.1

82



Amount of steam

Temperature and saturated

pressure of the steam

Temperature and pressure of

water arriving at the boiler

Enthalpy of evaporation

Enthalpy of saturated water

Enthalpy of water arriving at

boiler

Boiler efficiency

Distribution and heat transfer

efficiency

User output

Amount of fuel needed for steam production

Available energy for heating processes

Impacts from steam generation (resulting from burning the fuel)

UPR output

Required amount of fuel, e.g.:

From technosphere:

Natural gas, burned in industrial furnace >100 kW, RER

Light fuel oil, burned in industrial furnace 1 MW, non modulating, RER

Example

For a certain process 1 tonne of steam is needed with a temperature of 165 oC. A closed system is

assumed. The efficiency of steam distribution and heat transfer is 92.5%. The boiler efficiency is

89%. The evaporation enthalpy of this type of steam is 2.0656 GJ/t. This means that 2.51 GJ of

fuel are needed to deliver 1 tonne of steam (useful heat).

Addendum: Analysis of natural gas

Natural gas can be used for delivering the heat. However, when calculating environmental

impacts from the use of natural gas it appears that different datasets report completely different

primary energy requirements. Therefore an analysis has been made of the datasets that are

incorporated in the Ecoinvent process ‘Natural gas, burned in industrial furnace >100 kW, RER’ in

Box 1 below.

If the Ecoinvent dataset ‘Natural gas, burned in industrial furnace >100 kW, RER’ is used, the ERE

(Energy Requirements for Energy) is 1.19 MJp/MJ. However, several different datasets have been

used as in input to this dataset. As can be seen, the country of origin determines to a large

extend the ERE with gas from Russia having the highest ERE, while gas from the Netherlands has

lowest ERE (1.07 MJp/MJ). Including the ERE in the calculation above (2.51 GJ/t steam) results in

a total non-renewable energy use (from cradle) of 2.99 GJ per tonne of steam (2.99 = 2.51 *

1.19).

83

Box : Analysis of fossil energy requirements of Ecoinvent dataset “Natural gas, burned in industrial furnace >100 kW/RER”

Amount Fossil energy

Natural gas, burned in industrial furnace 1 MJ 1.19 MJ

Natural gas, high pressure, at consumer 1 MJ 1.18 MJ

Natural gas, at long distance pipeline 0.0272 m3

1.16 MJ

m3

m3

m3

Natural gas, prod DE 0.05 x 0.0272 = 0.0014 0.055 MJ

Natural gas, prod DZ 0.16 x 0.0272 = 0.0044 0.190 MJ

Natural gas, prod GB 0.04 x 0.0272 = 0.0011 0.044 MJ

Natural gas, prod NL 0.24 x 0.0272 = 0.0065 0.257 MJ

Natural gas, prod NO 0.17 x 0.0272 = 0.0046 0.187 MJ

Natural gas, prod RU 0.34 x 0.0272 = 0.0092 0.432 MJ

---------------

1.165 MJ

MJ/m3

m3

Natural gas, prod DE 40.5 x 0.0272 = 1.10 MJ

Natural gas, prod DZ 43.65 x 0.0272 = 1.19 MJ

Natural gas, prod GB 40.6 x 0.0272 = 1.10 MJ Remaining fossil energy for:

Natural gas, prod NL 39.4 x 0.0272 = 1.07 MJ

Natural gas, prod NO 40.4 x 0.0272 = 1.10 MJ Process energy

Natural gas, prod RU 46.7 x 0.0272 = 1.27 MJ Transport

m3

Gas, natural, in ground 0.0272 1.04 MJ

84

2.5 Processing and use of materials

2.5.1 Electric manufacturing processes

To quantify electricity requirements for electric and electronic devices used in manufacturing is

not straightforward; many types of equipment perform completely different functions.

Nevertheless some generic relationships can be defined. In all manufacturing processes, the

energy requirement for the process can be divided into two components (Branham & Gutowski,

2010):

the base (auxiliary) power required to run all supporting process operations

the variable power needed to effect the physical transformation associated with the process.

Branham and Gutowski (Branham & Gutowski, 2010) illustrate this with the example of metal

casting; In metal casting, the base power would correspond to that required to heat the crucible

and operate mechanical components of the system. The variable power would correspond to the

incremental additional power required to heat and melt a unit of metal.

In this basic engineering module, we will estimate the energy use of electric manufacturing

equipment.

System boundary

The system boundary of this BUO includes the electric manufacturing equipment. This machine

might apply several processes, all of which are covered in the system boundary by the ‘electric

equipment’. We do not assume any additional processes in the system boundary.


As a first approximation, the total power consumption rate (P in kW) can be modeled as follows:

vkPP 0

In which:

P = Total power (W)

P0 = Idle / auxiliary power (W)

k = Constant (J/unit processed)

v = Rate of material processing (unit processed/s)

The idle / auxiliary power represents the amount of electricity needed to start-up and maintain

the equipment in the ‘ready position’. The constant k represents the extra energy required to

process the material and could be for example melting energy, cutting energy, heating energy

etcetera. It is specific to the process and type of material and depends on properties such as

Electric

equipment

System

boundary

System

boundary

Electricity (En)

Input materials

(M)

Processed materials

(UO)

85

material hardness, heat capacity, temperature and enthalpies of any phase changes that might

take place.

Dividing both sides of the above equation by the throughput v yields an expression for the

specific energy consumption (SEC) of a process:

kv

P

v

PEelectr

0

In which:

Eelectr = Specific electrical energy per unit of material processed (J/unit)



Idle / auxiliary power

Rate of material processing - Constant of material conversion

User output

-

UPR output

From technosphere:

Amount of electricity required, e.g. “Electricity, medium voltage, production UCTE, at grid”.

Example

Chemical vapor deposition occurs with equipment with a power of 16 kW. No additional energy

requirements represented by k are needed. The throughput is 15 wafers/h (Murphy et al., 2003).

The energy use per wafer, hence, is 3.84 MJ / wafer.


Several authors have estimated energy use of different types of equipment that could serve as a

rule of thumb. They are listed below. It should however be noted that energy use of electric

equipment completely depends on the type of equipment, the size, the brand and so on. It is

therefore impossible to provide rule of thumb values for a single process such as ‘injection

molding’. Nevertheless, the tables below (Table 22-26) give some indication.

Table 22: Energy use of different types of injection molding machines (Thiriez, 2005)

Injection molder type Material Power draw

(kW)

Throughput

(kg/hr) SEC (MJ/kg)

Parallel circuit PS 55.6 45.35 4.41

Sequential circuit PS 38.9 47.62 2.94

Fixed pump Nylon 6.7 10.31 2.35

Variable pump Nylon 3.55 10.69 1.20

Fixed pump ABS 23.4 31.75 2.65

Variable pump ABS 20.0 32.20 2.24

Servo pump ABS 11.5 32.47 1.27

Electric machine ABS 5.7 32.29 0.64

Variable volume pump Surlyn 18.3 27.57 2.39

Variable speed drive on

motor Surlyn 13.7 27.57 1.79

Electric Surlyn 6.3 28.53 0.80

Hydraulic PP 97.6 151.93 2.31

86

Injection molder type Material Power draw

(kW)

Throughput

(kg/hr) SEC (MJ/kg)

Hydraulic PP 65.7 99.32 2.38

All electric PP 51.4 154.20 1.20

Hydraulic TPP black 26.07 4.48 20.94

All electric TPP black 5.71 4.48 4.59

All electric TPP black 5.96 5.04 4.25

Full hydraulic HDPE 130.6 188.66 2.49

Electric screw drive HDPE 84.2 180.5 1.68

All electric HDPE 63.6 213.15 1.07

87

Table 23:Energy use of four different types of milling machines (Dahmus & Gutowski, 2004)

88

Table 24: Energy use for the production of a 0.13-µm metal microprocessor. The active area on the wafers is 261 cm2 consisting of 1 cm2 dies (Murphy et al., 2003)

Unit operation Throughput

(8-in. Wafers/h)

Power (kW)

Process Idle

Implant 20 27 15

Chemical vapor

deposition (CVD) 15 16 14

Wafer clean 150 8 7.5

Furnace 35 21 16

Furnace (rapid thermal

processor) 10 48 45

Photo (stepper) 60 115 48

Photo (coater) 60 90 37

Etch (pattern) 35 135 30

Etch (Ash) 20 1 0.8

Metallization 25 150 83

Chemical mechanical

polishing (CMP) 25 29 8

Table 25: Ductile iron induction melter energy usage per tonne shipped (Jones, 2007)

Process step Percentage of energy use (%) Energy use

(MJ/tonne shipped)

Melting 55 16079.24

Molding 12 3508.2

Core making 8 2338.8

Ladle transfer 4.5 1315.57

Heat treatment 6 1754.1

Finishing 7 2046.45

Other 7.5 2192.62

Total 100 29234.98

Table 26: Energy use of two different waterjet machines (Kurd, 2004)

Machine Power (kW) Cutting time

(min) Energy use (kWh)

Volume of material

removed (cm3)

OMAX-2652 12.25 1.21 0.247 0.98

OMAX-2626 4.87 1.416 0.115 0.79

Material requirements as a function of mechanical properties

For a given application, designers often can choose between numerous possible materials. The choice

is made based on material specific properties, such as costs, strength, weight, fire resistance,

tolerance to abrasion, permeability of gases etcetera. Even when a choice for a specific material has

already been made, new materials will often be developed to replace the initially chosen materials.

An example is the use of nanocomposites. Nanocomposites are polymers that are reinforced with

nanoobjects (i.e. particles with at least one dimension in the range of 1-100 nm) and could replace

conventional polymers or even steel or aluminium for a given application. If the material properties

of the novel material are different compared to the conventional material, the amount of material

needed will in many cases also differ. If a novel material is stronger, for example, less material will

be needed for the same function compared to a conventional material. Although the exact material

requirements should be determined with extensive technical material and product analyses, Ashby

(Ashby, 2005) defined material indices that could be used for a first estimate of material

requirements of a novel material compared to a conventional material. In this basic engineering

89

module, we will present the approach for determining material requirements based on the Ashby

material indices.

System boundary

The α system boundary for this BUO includes a structural application for which a certain amount of

material A is needed. This material A can be replaced by another material B to perform the same

function. The production of the material is included in the β system boundary and hence will not be

taken into account in this BUO. The amount of alternative material B for the same structural

application is the useful output (UO) of this BUO.


The Ashby method for determining material requirements is represented by the following equation:

2

112

k

kmm

In which

m1 = Material requirement material A (kg)

m2 = Material requirement material B (kg)

k1 = Ashby index of material A (unit depending on index chosen)

k2 = Ashby index of material B (unit depending on index chosen)

Ashby’s material index (k) is a function of material properties such as the Young modulus (E), tensile

strength σf) and density (ρ). Ashby developed equations for different functional requirements, such as

strength or stiffness. It is assumed that material requirements are directly (inversely) proportional to

the material indices. The larger the value for k is, the better the mechanical properties of the

product under consideration are and the less material is needed for a certain function. The material

indices developed by Ashby are based on design that is ‘stiffness-limited’ (the material should not

bend), ‘strength-limited’ (the material should not break), ‘vibration-limited’ (the material should be

tolerant to vibration), ‘damage-tolerant’ (the material should resist damage), ‘electro-mechanical’

(the material is designed for electrical operation) or ‘thermal and thermo-mechanical’ (the material

should resist heat). Within each category, the appropriate function and constraints are chosen

(functions could be e.g. ‘tie’, ‘shaft’, ‘beam’, ‘column’, ‘panel’ or ‘plate’; constraints could be e.g.

required specification of dimensions). Tables B1-B7 show the various Ashby indices for different

functional requirements.

Structural

application

System boundary

System boundary

Material A (M)

Structural

application

System boundary

System boundary

Material B (UO)

90

91

92

93

94

95



Material requirement conventional

material

Specify function and constraint

Young modulus

Density

Failure strength

Shear modulus

Energy content/kg

Hardness

Damping coefficient

Fracture toughness

Thermal conductivity

Thermal diffusivity

Specific heat capacity

Thermal expansion coefficient

Electrical resistivity

Yield strength

Endurance limit

Material cost/kg

Ashby index

User output

Material requirement of alternative material for a specified function/application (kg)

UPR output

From technosphere:

kg material needed

Example

The following situation is considered: for the covering of a tomato greenhouse, plastic sheet is used.

Normally, such a sheet is made from low-density polyethylene (LDPE). However, a novel material has

been developed consisting of polypropylene reinforced with 3 wt% nanoobjects, i.e. organophilic

montmorillonite (‘polypropylene nanocomposite’). The question is what the material requirements

are for the novel material compared to the conventional LDPE.

It is assumed that in this case, the application is “strength-limited” (the material should not tear

apart, but it is allowed to bend) and the function is ‘tie’ (the forces working on the sheet are parallel

to the surface of the sheet). For this situation, the appropriate Ashby index is:

fk

The total amount of LDPE required to cover a standard greenhouse (k1) with a surface area of 430 m2

is estimated to be 2.38 tonnes. Furthermore, the following values apply:

σf of LDPE = 24 MPa

ρ of LDPE = 0.923 t/m3

σf of PP-nanocomposite = 37.4 MPa

ρ of PP-nanocomposite = 0.928 t/m3

Applying the basic engineering module, the material requirements for the tomato greenhouse using

the novel polypropylene nanocomposite are 1.51 tonnes.

2.5.2 Fuel use of cars

In car design and manufacturing, the ultimate goal is to develop cars that use as little fuel as

possible. One of the options to reduce fuel use is bringing down the weight of the car by using

96

lightweight materials. Fuel use is to a certain extent weight-dependent. In this basic engineering

module, we will present the relationship between the fuel use of a car and its weight. When the

weight of a car is known as well as a ‘new’ weight resulting from lightweight materials, its fuel

consumption can be estimated.

System boundary

The system boundary for this basic engineering module includes the performance of a car with a

certain weight 1 and a car with an alternative weight 2. In other words, it comprises the amount of

driven kilometers at a certain amount of fuel consumed. The distinction between cars with two

different weights is made because as we will see in the next section, for one calculation algorithm

the fuel use of a car with weight 2 is related to the fuel use of a car with reference weight 1. There

are no extra processes included in the β system boundary.


Two sources have reported formulas for the calculation of fuel use of a car with a given weight. These

are Koffler and Rohde-Brandenburger (Koffler & Rohde-Brandenburger, 2010) and Maclean and Lave

(Maclean & Lave, 2000). We will first present the algorithm by Koffler and Rohde-Brandenburger.

Algorithm according to Koffler and Rohde-Brandenburger

In order to quantify the fuel consumption of any vehicle, the underlying driving cycle has to be

specified first. The driving cycle specifies speed and chosen gear as a function of time. In Europe the

driving cycle has been standardized in the New European Driving Cycle (NEDC). It consists of 780

seconds (s) in an urban cycle and 400 s in an extra-urban cycle. Its average speed is 33.6 km/h, the

maximum speed is 120 km/h and the total distance is 11 km. Figure 11 illustrates the NEDC.

Driving car with

weight 1

System boundary

System boundary

Fuel

(M/UO)

Kilometers

driven (UO)

Driving car

with weight 2 Fuel

(M/UO)

Kilometers

driven (UO)

97

Figure 11: Time and speed pattern of the New European Driving Cycle

Following the NEDC profile, a vehicle’s power train has to provide the traction force to overcome

three types of driving resistance:

Rolling resistance (in N): RR fgmF

Aerodynamic resistance (in N): AvFL

2

2

Acceleration resistance (in N): amFa

In which:

m = Vehicle weight (kg)

g = Gravitation constant (m s-2)

fR = Rolling resistance coefficient (dimensionless)

ρ = Air density (kg m-3)

v = velocity (m/s)

α = Air drag coefficient (dimensionless)

A = Front surface (m2)

The mechanical work W (J) can be obtained through their respective integration over the distance s

(m):

dsFW

Since the NEDC’s velocity profile cannot be expressed through a simple mathematical function, the

integral is calculated as the sum of all work increments in between t=0 and t=1180 s.

WRRR CfgmW

WLL CAW2

Waa CmW

98

The characteristic values CWR, CWL and CWa are constants that are independent of the respective

vehicle and specific to the driving cycle. Table 27 shows values for the European, the US and the

Japanese driving cycles.

Table 27: Characteristic values CWR, CWL and CWa for different driving cycles

NEDC (EU) Combined fuel

economy (US) 10-15 mode (JP)

CWR (m) 11,013 17,198 4,165

CWL (m3/s2) 3,989,639 6,341,415 699,767

CWa (m2/s2) 1,227 2,221 687

About 15% of the NEDC total distance of 11 km is deceleration phase. During deceleration, no energy

is needed. Because rolling resistance is virtually independent of the vehicle’s velocity, the related

work also decreases by 15%. The total energy demand becomes:

aLRsum WWWW 85.0

The degree of efficiency η of an internal combustion engine (ICE) depends to a large extent on its

point of operation concerning revolutions per minute (rpm) and engine output. In general it can be

characterized as follows:

totalin

totalout

totalP

P

,

,

In which:

Pin = Energy input in liters fuel per hour (l/h)=(kW)

Pout = Engine output in kilowatts (kW)

The unit could be expressed as l h-1kW-1, which equals to l/kWh or l/MJ.

The fuel consumption characteristics of an ICE can be depicted with the help of the Willans line

method (Ross, 1997). Especially for low output and low rpm (<4,000 min-1), which are typical for the

NEDC, the Willans lines run almost parallel. This is shown in Figure 12:

99

Figure 12: Willans lines and resulting trend lines of a 1.4 l TSI gasoline engine (90 kW) for low output and low rpm

If it is further assumed that 2% of the engine’s additional energy output is lost in the gearbox, fuel

consumption calculates to:

02.1sumNEDC WV

In which:

VNEDC = Fuel consumption in the NEDC (l/11 km)

Algorithm according to Maclean and Lave

Maclean and Lave (Maclean & Lave, 2000) have estimated the fuel use of a car empirically based on

fuel consumption of a car with reference weight 1. The relationship they have derived is represented

by the following formula:

72.0

2

112

W

Wff

In which:

f1 = Efficiency of car with reference weight 1 (km/l)

f2 = Efficiency of car with new weight 2 (km/l)

W1 = Reference weight 1 of car (kg)

W2 = New weight 2 of car (kg)

Comparison of the two algorithms

Both algorithms just outlined (section 3.2.1 and 3.2.2) can be used to calculate the car

efficiency/fuel use of cars at different weights. If car efficiency is plotted against car weight at

different car weights and a reference car efficiency of 20.4 km/l at a weight of 2000 kg, the lines

coincide rather well as is shown in Figure 13, although the lines start deviating more at lower car

weights.

100

Figure 13: Comparison of car efficiency results using the two available algorithms



Vehicle weight

Front surface area of car

Air density

Air drag coefficient

Gravitation constant

Constant CWL

Constant CWR

Constant CWa

Engine efficiency

User output

Car efficiency / fuel consumption

UPR output

The amount of fuel consumed by the car for a certain distance, e.g. “Petrol, unleaded, at

regional storage”.

Example

We consider a Mercedes Benz W140, type ‘sedan’. This car has a weight of 2000 kg. Furthermore, the

following parameter values apply:

Front surface area: 2.8 m2

Air drag coefficient: 0.39

Engine efficiency: 0.076 l/MJ

Rolling resistance: 0.01

Air density: 1.225 kg/m3

Gravitation constant: 9.81 m/s2

CWL: 3989639 m3/s2

CWR: 11013 m

CWa: 1227 m2/s2

WL, WR and Wa can now be calculated:

WL = 2.67 MJ

WR = 2.16 MJ

15.0

17.0

19.0

21.0

23.0

25.0

27.0

29.0

31.0

1000 1500 2000 2500 3000 3500

Weight of car (kg)

Car

eff

icie

ncy (

km

/l)

Koffler and Rohde-

Brandenburger

Maclean

101

Wa = 2.45 MJ

Wsum amounts to 6.96 MJ and, hence, VNEDC is 0.54 l/11 km which equals to 20.4 km/l

It should be noted that the results for fuel use/car efficiency are quite optimistic when using the

constants for the NEDC. If constants for the combined fuel economy (US) are used, this car would

drive only 12.3 km/l. If the weight of this car would be reduced with 100 kg, the related fuel use can

be calculated with the formula from Maclean and Lave (Maclean & Lave, 2000):

f1 = 20.4 km/l

W1 = 2000 kg

W2 = 1900 kg

Hence, the new car efficiency ‘f2’ equals to 21.1 km/l.

102

Part II: Validation of the engineering modules

103

1. Introduction

In the transition to a more sustainable society, Life Cycle Assessment (LCA) is considered as a valuable

assessment framework for the quantification of the environmental impact of products and services

(European Commission, 2003). For this purpose it is more and more included in the decision making

process at different levels of policy and industry. The LCA supported decision should then make a

choice between options by outweighing the positive and negative environmental effects of each

option. Obtaining meaningful results requires that all options are thoroughly known; LCA is indeed a

data intensive procedure (Mueller et al., 2004) that typically requires full material and energy

balances of the system under study. However, this might not be available for the different options

under development, which is a major shortcoming for the implementation of prospective

environmental sustainability assessments.

Especially in biorefineries this is an important issue. The transition from fossil based to biobased

refineries is seen as a big step in greening the economy by mitigating climate change and offering

new options to obtain renewable and thus more sustainable products and services (IEA Bioenergy,

2009b). Nevertheless, it is already shown that this transition is challenging as renewable resources

also have a production chain with significant environmental impact, e.g. through land and water use

(De Meester et al., 2011). Strategic choices should therefore be made before final implementation in

order to achieve the highest degree of sustainability with the biomass that is available.

The main problem to assess future biorefineries is obtaining a data inventory with a full mass and

energy balance of the new processes. For this purpose in part I, basic engineering modules were

developed that can be used as parameterized unit operations where raw data and formulas are used

to determine the data inventory, instead of using fixed values only (Cooper et al., 2012). This allows

the completion of the life cycle inventory by assembling material and energy balances of basic unit

operations (BUO; single process steps such as a distillation column) whilst being flexible and

straightforward in use and easily accessible to life cycle practitioners as the modules can be

implemented in LCA software such as OpenLCA. In this part of this report, the modules developed are

validated in a biorefinery case study to demonstrate the operability and accuracy of the proposed

approach.

2. The biorefinery case study

The basic engineering approach is tested in a biorefinery case study in which wheat, flour and sugars

are processed into specialty sugars, starch, gluten, ethanol and animal feed (Figure 14). The

conversion of the biofeedstock concerns typical processes of a biorefinery, starting with dry and wet

milling steps and further separations mainly with centrifuges and sieves. Afterwards the intermediate

flows are upgraded to final products, mainly by drying and evaporation. In this type of biorefinery,

not many ‘reactions’ occur as it is more the separation of the different molecules of the biofeedstock

as such, possibly after a hydrolysis step. There are two exceptions, namely the fermentation of wet

starch streams to bioethanol with a subsequent distillation section, and a combined heat & power

(CHP) engine where natural gas is converted into steam, hot water and electricity.

104

Figure 14: A process diagram of the case study

The case study is a complex system with a large number of single processes where an extensive

dataset was gathered as a year average of 2009 for many of these processes. Yet, it was not possible

to collect all data of all processes. Therefore a selection has been made of ten of the twenty-two

processes limited by confidentiality, availability of detailed data and availability of a specific BUO

that is applicable for the considered process. This validation effort is still valuable as it gives a first

indication on the accuracy of the results and it can serve as guidance for further work on the

quantification of life cycle inventories for prospective assessments by means of parameterized

modules. In total, ten of the twenty-two BUO approaches were tested, often based on several

subsamples within their own α boundary. This test is elaborated in Table 28, where also the sample

size is given. In total, 41 subsamples were tested, where identical processes (e.g. two centrifuges)

are only counted once.

105

Table 28:The elaborated BUO categories with case specific information and sample size

BUO Case specific process information Sample size

Comminuting The wheat grains are first dry-milled by using a complex

system of rolls and rotors

3

Agitation and mixing of

liquids and suspensions

Both the agitation in the fermentation tanks and the dough

mixer are studied

3

Sedimenting centrifuges Centrifuges are mainly used for wet separation of dough,

gluten, etc.

7

Evaporation After wet separation, the dry matter content of flows is

increased by evaporators with 1 or more effects.

3

Mechanical compression –

single stage

Recompression is used to upgrade vapor of evaporators 4

Fermentation Wet starch streams of the factory that are less suitable as

food or feed are fermented to bioethanol

1

Binary distillation The ‘beer’ solution obtained from fermentation is distilled to

purify the ethanol

1

Heating Several flows are preheated before evaporation, drying, etc. 5

Incineration for heat and

power

A CHP working on natural gas produces hot water, steam and

electricity to supply the factory with energy. Excess

electricity is sold to the local grid

1

Conveying solids Before the wet milling steps, biomass is mainly transported

and ensilaged by different types of conveyors

13

The results of the modeling of mass and energy balances within the α system boundary are validated

with the actual mass and energy balances of the factory. As the sample size (n) was limited for some

operations, no statistical uncertainty is elaborated. Instead, per calculation of a basic unit operation

within the α system boundary, an Accuracy Factor and a Relative Approximation Error (RAE) is

calculated, which are defined respectively as:

In this validation procedure, the products of the biorefinery are assessed in a life cycle perspective.

The parameterized BUO modules are used mainly to predict energy consumption of the different

processes. The accuracy of this calculation is then tested with the actual data obtained from the

factory. Afterwards, the results are linked to life cycle assessment by calculating a Carbon Footprint

based on the IPCC 2007 (Solomon et al., 2007) impact assessment method, relying on the ecoinvent

database for the datasets to model the γ system boundary.

106

3. Results and discussion

Within the aforementioned limitation, it was possible to calculate 27% of heat and 42% of the

electricity use of the factory. In the following, first two examples of BUO validations are presented.

Second, an overall overview on the tests is discussed and third, the application in a Life Cycle

Assessment is analyzed.

3.1 Validation examples

3.1.1 Mechanical compression – single stage

Most compressors in the factory are used for recompression of vapour in evaporator systems. One of

the compressors upgrades the vapour of 377K to usable steam of 439K and has an actual energy use of

2599kJ/kmol. Using the calculation procedure for this BUO with the values given in yields a predicted

energy use of 2829 kJ/mol (Table IV.1.7), obtaining a Relative Approximation Error of 9% for this

process. By applying this BUO approach, the output temperature and pressure are obtained, allowing

further calculations with the available heat of the steam, and the electricity use of the compressor is

known when linked to the flow rate. Coupling this to a life cycle database allows obtaining the carbon

footprint of the operation. Based on the Belgian electricity mix (medium voltage), the carbon

footprint of the compression of 100 kg steam according to the parameters in Table 29Fout!

Verwijzingsbron niet gevonden. results in an emission of 1.48 kg CO2-eq according to the

calculations, whereas the actual emission is 1.36 kg CO2-eq.

Table 29:Parameters in the calculation procedure of the BUO mechanical compression – single stage

DATA source Symbol Value Unit

INPUT pi 101325 Pa

INPUT Ti 377 K

INPUT Tc 439 K

DATAPHYSCHEM R 8.31 J/mol*K

DATAPHYSCHEM γ 1.31

DATABUO α 77 %

OUTPUT Pc 192769 Pa

OUTPUT pc/pi 1.90

OUTPUT W 2829 kJ/kmol

3.1.2 Evaporation

After the different wet milling and separation steps, different starch streams require higher dry

matter concentrations. One of the streams enters in an evaporator at a dry matter concentration of

2.8% and leaves at 4.9%. The actual energy delivered by the steam is 998kJ per kg incoming feed,

whereas the calculated value indicates 973kJ per kg (Table 30), resulting in a RAE of 3%. Using this

BUO thus allows to estimate the heat requirement of the evaporation process. Coupling this to a life

107

cycle database gives a Carbon Footprint of 2.65 kg CO2-eq according to the calculations, and 2.72 kg

CO2-eq based on the actual data (by using Heat, at cogen 1MWe lean burn, allocation exergy/RER).

Table 30:Parameters in the calculation procedure of the BUO evaporation

DATA source Symbol Value Unit

INPUT mf 1.00 kg

INPUT mo 0.56 kg

INPUT Ti 108.0 K

INPUT Tb 108.6 K

DATAPHYSCHEM cp 4.10 kJ/kg*K

DATAPHYSCHEM λ 2230 kJ/kg

DATABUO η 90 %

OUTPUT q 973 kJ

3.2 Overview of the validation

Figure 15 summarizes the Accuracy Factors obtained by the validation of the different samples, where

1 would be a perfect fit of the actual mass and energy balance of the factory. In this validation test,

an overall average relative approximation error of 22% is obtained.

108

Figure 15: A summary of the Accuracy Factors of the different BUO calculations. A value of 1 represents a perfect fit of the calculated result over the real value

The Combined Heat and Power (CHP) plant is the starting point and is accurately predicted with a

RAE of 0% for electricity and 2% for heat production. The standard efficiency factors for an efficient

natural gas-fired CHP producing electricity and heat are thus reliable in this case, which is essential

in the sustainability assessment of the factory as this CHP delivers energy to all other operations and

thus has a large influence on the final result. It should, however, be noted that the heat availability is

predicted accurately, but not necessarily all heat is required in the processes. The allocation factors

in LCA studies should thus be based on the real heat use instead of the potential heat use.

The calculations for milling (comminuting) result in a RAE of 7%. A good Bond Work Index should,

however, be found for the milled material, which might not be available in some cases. For wheat

grains however, a reliable value was found in literature (Das, 2005). It should also be noted that the

calculations are valid if the power of the mill is put directly on the particle. In case the particle size

is reduced by rotational forces causing friction between the particles, other calculation procedures

should be sought.

The deviation in the agitation model is larger; the validation highlights RAE values of up to 107% for

the fermentation and 27% for the dough mixer. As a clarification the used equations in the

parameterized module of agitation power (P) should be considered (McCabe et al., 2004):

for the turbulent region and

for the laminar region. With µ the viscosity, ρ the density and Np and KL flow regime dependent pump

numbers. The prediction of equipment power thus depends heavily on the impeller velocity (N) and

diameter (Da). As these parameters are in most cases approximated, small deviations from the real

value result in a large sensitivity of the final result. Furthermore, this calculation requires fluid

109

properties, which are approximated by the Power Law for ‘slurry’ type of flows, resulting in an

additional potential source of deviation. The same is true for pumping incompressible fluids in a

biorefinery. It is very difficult to obtain all exact parameters for the calculation (e.g. amount of

turns, length of pipes, etc.). This is more straightforward for conveyors where the module basically

requires only the type of conveyor, the flow rate and an approximation of the length. The calculations

based on 13 samples yield RAE of 23%, which is reasonable as a first estimate, knowing that this

operation is of a smaller relevance relative to the total power use in the factory.

The average RAE for sedimenting centrifuges is 25% if each type is counted once. This factor is lower

for the total absolute centrifuge power (10%) as the most accurately predicted centrifuges have the

largest contribution to total power use. In 4 of 7 samples the power was predicted accurately

(average RAE = 8%), but in the other samples a larger RAE was obtained (33 to 60%). This is the

disadvantage of the fact that this module is not based on a continuous equation, but on integer

ranges depending on typical combinations of liquid and solid throughput. The largest deviation occurs

when it is not obvious to which exact range the centrifuge should be assigned.

The fermentation section was predicted with a RAE of 3% for the fermentation yield and 4% for the

energy use of the distillation column. This module is however highly sensitive to the reflux ratio

applied in the column, as this is multiplied with the latent heat, causing the largest part of the

energy requirement. The (pre)heating steps are predicted with a relative approximation error of 4%

and also the heat use per evaporator is predicted fairly accurate (RAE = 8%) based on the

thermodynamic balance. The configuration of the different evaporators can however be complex,

with different effects and with intermediate compressors to upgrade vapor streams. As a result, the

total heat required is not the sum of each heat requirement separately. Instead the BUO modules for

heat consumption also require heat integration and consideration of valorization of ‘waste’ heat from

other processes and of the use of recompressors to upgrade vapor. The compressors themselves are

predicted relatively accurately (RAE = 6%). It should however be noted that two adjustments had to

be made; first one of the compressors is known to have a lower efficiency than ‘traditional’ current

equipment where a default value of 77% is assumed for centrifugal compressors. Second, it is possible

that in industry equipment is reused from a previous application and therefore not optimally sized for

their current task, which might lead to additional energy consumption, but which is sound from an

economic perspective.

3.3 Using the BUO approach in LCA

The carbon footprint (γ system) of the calculated modules of the factory is 96% of the carbon

footprint based on actual data when excluding the ‘milling’ steps based on rotational forces causing

friction and including only the optimally designed compressors (87% including these excluded

processes). This good score can be explained by the fact that the results of the parameterized

modules with the highest accuracy factor have a large share in the absolute carbon footprint of the

factory (Figure 16). Additionally, the obtained impact is the sum of energy use in the α system

boundary of the ten applied BUO approaches and has a better score than each BUO separately, which

can be explained by considering Figure 5, from which no bias of over- or underestimation can be

observed resulting in a compensation of higher and lower approximations. Furthermore, when

assembling the results of each BUO separately, we performed a similar heat integration over the

different modules compared to the real factory. This aspect requires care, as the sum of heat

calculated per module is much higher than real total heat use. The degree of heat integration

however, cannot be generalized as it depends on the specific process chain, availability of ‘waste

heat’ and company policy. Another difficulty is integration of the β system boundary in the

assessment. In the studied biorefinery these SUO operations are not negligible: approximately 22% of

total electricity use is used for pumping liquid fluids and as the biorefinery is situated in a densely

populated area, an elaborated system of controlling air streams by ventilation and aspiration is used.

This SUO system accounts for approximately 16% of the plant’s electricity use and was also not

validated because of its complexity. This system of SUO is difficult to generalize or to model

separately; information such as length and amount of turns in piping systems or the pressure drop in

ventilators is essential but needs detailed designing which is not straightforward. Another

110

shortcoming is the unavailability of the BUO drying, which accounts for almost all heat use that was

not incorporated in this validation. It is indeed a challenging task to find a suitable generic

engineering module for this process, as there is a large range of dryer types each with their specific

configuration. The energy demand of this process depends on this configuration and is a combination

of interlinked factors such as air humidity, temperature, velocity, surface area, equilibrium moisture

content, etc. A large range of company specific choices is possible impeding generic calculations,

such as the balance between more electricity to achieve a higher air flow rate over the material or

using hotter air to increase efficiency per volume of air used. On top of the parameterization of

energy consumption, also the mass flows in a studied system should be studied, as the impact of the

agriculture of the biofeedstock should also be allocated to the output products. In this work, the

fermentation yield was approximated with an relative approximation error of 3%. The other mass

flows are, apart from different hydrolysis steps, not chemical reactions, but are separations of the

different molecules of the plant (e.g. gluten, starch, …). The latter are user specific choices that are

rather straightforward and are therefore not included in this work. The use of utilities such as foam

inhibitors, silica, cleaning agents, buffers and enzymes is case specific and requires expert judgment

rather than generic parameterization. While the impact of the latter is relatively small

(approximately 6%), the combination of the different drawbacks might cause a significant increase in

the deviation of the total carbon footprint of a prospective LCA with the BUO approach.

Figure 16: The contribution of the tested modules to the final carbon footprint

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Calculated values Real values

Conveying solids

Heating

Binary distillation

Mechanical compression – single stage

Evaporation

Sedimenting centrifuges

Agitation and mixing of liquids andsuspensions

Comminuting

111

General conclusions

The proposed approach of using engineering calculations, rules of thumbs and default values to obtain

the mass en energy balance of a system by applying a modular BUO approach is identified as a useful

and reliable way to support prospective life cycle assessments. Engineering calculations allow to

obtain UPR for other production chains than those currently available in literature and databases and

offer the freedom to be adjusted for the assessment of future technologies. The approach chosen can

be used to simulate datasets in the foreground system, obtaining data compatible with life cycle

practices, which allows coupling the modelled system to a background system. It is possible to work

with different levels of details of input data in the models. Default values are available in many cases

as approximations, but different parameters can be adjusted if more detailed data is available. This

type of work can become a new source of data for Life Cycle Assessments and can become an

invaluable help in prospective assessments. Furthermore, these modules can be implemented in LCA

software such as OpenLCA in order to become more user friendly.

In total twenty-five modules were developed in five categories:

Reactions

Separation processes

Physical mechanical processes

Utilities

Processing and use of materials

Of these twenty-five parameterized modules, ten were tested in the case study. Apart from agitation,

most of these modules yield relatively good results compared to the real values with an overall

average relative approximation error of 22%. When these modules are used in the case study to

estimate the carbon footprint of a biorefinery, an accuracy of 96% was achieved. It should, however,

be noted that there is a compensation between over- and underestimation in the case studies

conducted. Furthermore, three major shortcomings were identified. Firstly, whereas the inventory of

the α boundary was obtained with an acceptable error, the supporting unit operations such as

pumping are very specific and difficult to obtain whilst not being negligible. Secondly, a factory is

complex and processes are not independent from each other; a successful application of the BUO

approach therefore requires a careful integration of heat use instead of a linear summation of heat

use of different processes. Thirdly the list of selected BUO is still limited to twenty-two, whereas

other operations also occur in industry. For example in biorefineries, operations such as cyclones and

dryers are frequently used. More parameterized modules should thus be developed in the future.

Nevertheless, while more case studies are necessary for confirmation of the results in this study, the

currently available modules produce relatively reliable results. The possibility of integration of these

modules as parameterized unit operations in LCA software can be of major value to obtain a detailed

data inventory necessary for prospective sustainability assessments. The modules should, however, be

used with care and with a realistic design of a production chain in mind.

112

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