Final report - Newcastle Universityresearch.ncl.ac.uk/pro-tem/components/pdfs/EPSRC_Thermal... · 2012-05-08 · EP/G060045/1 Final Report 3 Abstract A large amount of low-grade heat

EP/G060045/1 Final Report

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Final report Title of Research Project: Thermal Management of Industrial Processes Grant Reference: EP/G060045/1 Programme: Energy Multidisciplinary Applications Call: Thermal Management in the Process Industries Organisation: The University of Machester


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Part I: Advanced Process Integration for Low Grade Heat Recovery

Ankur Kapil, Igor Bulatov, Robin Smith, Jin-Kuk Kim

Centre for Process Integration School of Chemical Engineering and Analytical Science

The University of Manchester Manchester, M13 9PL, UK

Part II: Environmental and Socio-Economic

Issues

Patricia Thornley, Conor Walsh, Paul Upham

The Tyndall Centre for Climate Change Research The University of Manchester

Manchester, M13 9PL, UK


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Abstract

A large amount of low-grade heat in the temperature range of 30 oC and 250 oC

are readily available in process industries, and wide range of technologies can be

employed to recover and utilize low-grade heat. However, engineering and

practical limitations associated with the integration of these technologies with the

site has not been fully addressed so far in academic and industrial communities.

Also, the integration of non-conventional sources of energy with the total site can

be a cost-effective and promising option for retrofit, however, carrying out its

design and techno-economic analysis is not straightforward, due to variable

energy demands. One of the key performance indicators for the evaluation and

screening of the performance of various energy saving technologies within the

total site is the potential of cogeneration for the site. A new method has been

developed by Centre for Process Integration (CPI), School of Chemical

Engineering and Analytical Science, to estimate cogeneration potential by a

combination of bottom-up and top-down procedures. In this work, the

optimization of steam levels of site utility systems, based on a new cogeneration

targeting model, has been carried out and the case study illustrates the benefits

of optimising steam levels for reducing the overall energy consumption of the

site.

There are wide range of low-grade recovery technologies and design options for

the recovery of low grade heat, including heat pump, organic Rankine cycle,

energy recovery from exhaust gas, absorption refrigeration and boiler feed water

heating. Simulation models have been developed for techno-economic analysis

of the design options for each technology and to evaluate the performance of

each with respect to quantity and quality of low grade heat produced on the site.

Integration of heat upgrading technologies with the total site has been studied

and its benefits have been illustrated with a case study for the retrofit design.

Over-the-fence heat integration for district heating (DH) can be suggested to

utilize the low grade waste heat and therefore alleviate the carbon footprint of the

integrated energy system. The economic performance of the over-the-fence


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process integration depends on the cost of fuel, electricity and distance for the

transfer of waste heat to DH network. A new design methodology has been

developed to systematically evaluate economic benefit of such the integration of

low grade heat with local district heating networks. A site-wide analysis tool using

site composite profiles is incorporated in the developed design method in order to

identify the quality and quantity of low grade heat available from the site. The

developed optimisation framework identifies economically acceptable distance

for the over-the-fence heat recovery from the industrial site to local community,

subject to economic parameters and engineering constraints. A case study has

been carried out to demonstrate the developed design methodology, and the

results from the case study illustrates techno-economic and engineering barriers

in practice for the implementation of low grade heat recovery beyond the site.

The Tyndall Centre for Climate Change Research undertook in task 7 of this

project to quantify the benefits of different process efficiency options and analyze

barriers to their practical implementation. This was achieved by completion of the

following research:

Evaluation of the barriers to process efficiency improvements generally

and utilisation of lwo grade heat particularly - described in section 9

Assessment of the environmental and economic performance of different

low grade heat recovery options – described in section 10

Assessment of the social aspects of low grade heat recovery, including

responses of potential heat users – described in section 11

An important part of this task has also been engagement with key stakeholders and

dissemination of the findings of the work. This is therefore detailed separately in

section 12. The key research outputs published in peer reviewed journals are

provided in the report.

The report provides final results of research carried out by The University of

Manchester team during the project duration from 15/09/2009 to 14/12/2011. The

CPI research group included Dr Jin-Kuk Kim (Principal Investigator, later moved

to a university in Republic of Korea), Professor Robin Smith (Principal

Investigator, initially Co-Investigator), Dr Ankur Kapil (researcher, 100% of full


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time) and Dr Igor Bulatov (project officer, 25% of full time). The Tyndall Centre

research group included Dr Patricia Thornley (Co-Investigator), Dr Conor Walsh

(researcher 100%), Dr Paul Upham (researcher 10%). Grant total £ 336,803.


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List of contents

Part I System-wide Modelling and Optimisation with Advanced Process

Integration for Low Grade Heat Recovery

1 Introduction ................................................................................................................. 9 2 Cogeneration potential .............................................................................................. 11 3 Optimization of steam levels .................................................................................... 18

Case Study Cogeneration potential ............................................................................... 21

4 Low Grade heat upgrade ........................................................................................... 28 4.1 Available technologies ....................................................................................... 28

4.1.1 Vapour compression heat pump.................................................................. 28 4.1.2 Absorption Systems .................................................................................... 29 4.1.3 Boiler feed water (BFW) heating ................................................................ 33 4.1.4 Organic Rankine Cycle (ORC) ................................................................... 33

4.1.5 Thermo-compressor .................................................................................... 34 4.1.6 Drying ......................................................................................................... 35

4.2 Algorithm ........................................................................................................... 35 4.3 Case study .......................................................................................................... 36 4.4 Results and discussions ...................................................................................... 39

4.4.1 Integration of heat pump ............................................................................. 39

4.4.2 Integration of Organic Rankine Cycle (ORC) ............................................ 43 4.4.3 Integration of Absorption refrigeration ....................................................... 46 4.4.4 Boiler feed water heating Integration ......................................................... 48

4.4.5 Comparison of design options .................................................................... 49 5 Over the fence process Integration ........................................................................... 50

5.1 Design Methodology .......................................................................................... 52 5.2 Modelling of energy equipment ......................................................................... 54 5.3 Optimization formulation ................................................................................... 55

5.4 Case Study 1: Integration of industrial waste heat with district heating (DH)

systems .......................................................................................................................... 59

5.4.1 Waste heat available in an industrial site .................................................... 60

5.4.2 District heating (DH) systems ..................................................................... 62 5.4.3 Feasible distance of heat transfer ................................................................ 65 5.4.4 Optimization Results ................................................................................... 65

5.5 Case Study 2: Integration of waste heat with a local energy systems ................ 70 6 Conclusions & future work ....................................................................................... 72 7 References ................................................................................................................. 74 8 Appendix A ............................................................................................................... 77

8.1 Optimization framework .................................................................................... 77

8.1.1 Objective function ....................................................................................... 77 Optimization constraints ........................................................................................... 79 8.1.2 Electric balances ......................................................................................... 79

8.1.3 Mass balances ............................................................................................. 79 8.1.4 Heat balance ................................................................................................ 81

8.2 Equipments ......................................................................................................... 82


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8.2.1 Multi-fuel boilers ........................................................................................ 82 8.2.2 Gas turbines (GT) ....................................................................................... 83 8.2.3 Heat recovery steam generators (HRSG) .................................................... 84 8.2.4 Electric motors (EM) .................................................................................. 85

8.2.5 Steam turbines (ST) .................................................................................... 85

Part II Environmental and Socio-Economic Issues

9 Barriers to Process Efficiency Improvements and Low Grade Heat Utilisation ...... 90 10 Environmental and Economic Analysis .................................................................... 92

10.1 Organic Rankine cycle integrated into a coke oven. ...................................... 93 10.2 Condensing boiler applied to woodchip combustion. .................................... 95

10.3 Heat pump for desalination ............................................................................. 97 10.4 District Heating............................................................................................... 99

11 Social aspects: perceptions of heat users ................................................................ 100

12 Conclusions ............................................................................................................. 103 13 Appendix B: List of Published Outputs .................................................................. 105


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Part I

System-wide Modelling and Optimisation with Advanced Process Integration for Low Grade Heat Recovery


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1 Introduction The typical sources of low grade heat are listed in Table 1. The opportunity

includes the waste heat recovery from liquids and gases, CHP (combined heat

and power), drying, steam generation and distribution and waste heat utilization.

The industrial application of low grade heat recovery is relevant to process

industries, including chemical, petroleum, pulp and power, food and drink,

manufacturing, iron and steel, and cement industries.

Table 1: Sources of low grade heat[1]

Opportunity Areas Industry

Waste heat recovery from gases and

liquids

chemicals, petroleum, forest products

Combined heat and power systems chemicals, food, metals, machinery,

forest products

Heat recovery from drying processes chemicals, forest products, food

processing

Steam (improved generation,

distribution and recovery)

all manufacturing

Energy system integration chemicals, petroleum, forest products,

iron and steel, food, aluminium

Improved process heating/heat transfer

systems (improved heat exchangers, new

materials, improved heat transport)

petroleum, chemicals

Waste heat recovery from gases in metals

and non-metallic minerals manufacture

iron and steel, cement

To avoid unnecessary capital expenditure for oversized equipment and to

enhance controllability of the energy systems, dynamic feature of the energy

supply and demand along with integration with energy recovery technology must


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be incorporated into the energy study in a systematic and holistic manner. The

implementation of these integrated energy saving projects within or beyond the

plant may not be favoured, due to practical constraints, for example,

considerable civil and piping works required, legislative limitations, different

energy utilisation patterns between sources and sinks, etc. Therefore, it is vital to

quantify the economic benefits of employing low grade energy recovery and its

impacts on the industrial site.

The integration of waste heat from the site utility system in a process industry

with a DH network is schematically shown in Figure 1. The integration of waste

heat with an existing DH network is evaluated in this work. However, a new DH

system can also be designed taking into account the waste heat from industry.

The barriers to the design and installation of new DH network were discussed in

the work of Davies et al.[2]. The regulatory framework, for the installation of a

new DH network, a financial barrier for raising money for new DH design, and a

commercial barrier related to the competitiveness of DH in regards to other

technologies are the three main factors that heavily influence on the installation

of the DH network in UK[2]. The impact of low grade heat transfer with an

existing DH network is dependent on the distance between the DH network and

the process industry, part load performance of the energy production equipment

in DH, etc.


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Figure 1: Process integration

2 Cogeneration potential The extent of heat recovery and cogeneration potential is closely related to the

configuration of site energy distribution systems in an industrial site, in which

multiple levels of steam pressure are introduced, for example, VHP (very high

pressure), HP (high pressure), MP (medium pressure) and LP (low pressure).

Steam levels and its corresponding pressure is an important design variable as

they can be adjusted to either minimize the fuel requirement or maximise profits

by exploiting site-wide trade-off of heat recovery and power generation.

Optimization of levels of steam mains is based on the manipulation of targeting

models for the cogeneration potential for the site utility systems.

Fuel

Fuel

FuelPROCESS A PROCESS B

Fuel

POWER

HIGH PRESS

MED. PRESS

LOW PRESS

PROCESS C

COOLING WATER

COND

Air

W

REFRIGERATION

District heating

Waste heat integration with District

heating


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The performance of the system can be either optimized to obtain the best design,

or to obtain the optimum operating conditions for an existing design, considering

the part load performance of the equipment based on the optimum number of

steam levels and their pressure. The simulation and optimization of the utility

systems require an accurate and yet simpler model for each element of the

system. Accurate estimation of the cogeneration potential is vital for the total site

analysis as it aids in the evaluation of performance and profitability of the energy

systems. The overall cost-effectiveness of power and heat from the site is heavily

influenced by the optimum management and distribution of steam between

various steam levels. Furthermore, optimum import and export targets for

electricity can be obtained from steam levels, load and price of fuel and

electricity. Also, energy efficiency for the utilisation of low grade heat will be

strongly influenced by operating and design conditions of existing energy

systems. Therefore, the accurate estimation of cogeneration potential is essential

for performing a meaningful economic evaluation of the design options

considered for heat upgrading and/or waste heat recovery.

A number of methods are available in the literature for estimating the

cogeneration potential of utility systems. The ideal shaftpower is calculated as

the exergy change of the steam passing the turbine[3]. The exergetic efficiency is

considered to be independent of the load and inlet-outlet conditions, and is

assumed to be a constant value. The steam conditions are approximated by the

saturated conditions, but the superheat in the inlet and outlet steam conditions

are neglected[4]. There is a difference of up to 30% in cogeneration potential in

comparison with simulations based on THM (turbine hardware model) developed

by Mavromatis and Kokossis[5].

Salisbury[6] observed that the specific enthalpy of steam (i.e. enthalpy per unit

mass flow) is approximately constant for all exhaust pressure values[7]. There is

a linear correlation between specific power w (power per unit mass flowrate of


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steam) produced in the turbine and the outlet saturation temperatures. The

specific power corresponds to the area of the rectangle on a graphical

representation of the inlet and outlet saturation temperatures of the turbine with

respect to the heat loads of steam. This methodology is based on the following

assumptions: specific load (q) of steam is constant with variation in exhaust

pressure and specific power is linearly proportional to the difference of inlet and

outlet saturation temperatures.

Mavromatis and Kokossis[5] proposed a new shaftpower targeting tool called the

turbine hardware model (THM) based on the principle of Willans line. Willans line

approximates a linear relationship between steam flowrate and the power output.

THM has limitations as Varbanov addressed[8]: the effect of back pressure is not

taken into account, and modelling assumptions for part-load performance are too

simplistic, such that the model assumes a linear relationship over the entire

range of operation.

Sorin and Hammache[9] introduced a different targeting method based on

thermodynamic insights and Rankine cycle. The ideal shaftpower is a function of

outlet heat loads and the difference in Carnot factor between the heat source and

heat sink. The deviation of the actual expansion from the ideal expansion is

defined in terms of isentropic efficiency.

New Method

Cogeneration targeting in utility systems is used to determine fuel consumptions,

shaftpower production and cooling requirements before the actual design of the

utility systems[9]. The previous methods available in literature have the following

drawbacks. TH model does not consider the contribution of superheat in the inlet

and the outlet stream in the power generation. THM parameters are based on

regression parameters derived from a small sample of steam turbines, and

consequently are not applicable for all the possible sizes of turbines.


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In order to overcome shortcomings of previous methods, new method for

cogeneration targeting has been proposed in this work, and isentropic efficiency

is used in the new targeting method.

TH model for targeting does not include the superheat conditions at each level

which results in significant error for estimating cogeneration potential. THM

model uses an iterative procedure based on specific heat loads to calculate the

mass flowrate for the turbines. The calculation of flowrates in Sorin’s

methodology is based on the flow of energy. Power produced by the system is

estimated with the isentropic efficiency, available heat for power generation and

inlet and outlet temperatures of Rankine cycle. However, there is no justification

for the assumption that thermodynamic behaviour of all the steam turbines to be

used acts as that of the Rankine cycle.

The new algorithm calculates the minimum required flowrate from steam

generation unit (e.g. boiler) and the levels of superheat at each steam main

based on the heat loads specified by site profiles of heat sources and sinks.

The algorithm for the new procedure is given in Figure 2. The superheat

temperature calculation at each steam level is made, starting with a certain

superheat temperature of the steam from the boiler. The procedure is based on

the assumption that steam supplied to the site utility systems from a boiler is at

the superheated conditions required as VHP steam level. Figure 3 shows the

temperature entropy diagram for the process. The initial conditions of

superheated steam at higher pressure and temperature level are represented by

point 1. The steam at lower pressure level for an isentropic expansion is shown

as Point 2’ on the curve. Isentrotpic expansion with an efficiency of x% is used to

determine the enthalpy at point 2. It is assumed during targeting stage that all the

steam turbines are operating at their full load. The cogeneration potential of the

system is dependent on the expansion efficiency of x. This parameter is

dependent on the capacity of the turbine and detailed calculation is given below.


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Steam properties are calculated for the given entropy and pressure at the lower

steam level. If the degree of superheat in the resulting LP steam main is less

than required, then operating conditions of VHP is updated and then re-iterates

the procedure above until the acceptable superheated conditions for LP steam

main is met.

Figure 2: Algorithm for new method based on isentropic expansion

Given steam levels, inlet superheat of VHP steam, process load, BFW, Condensate temperature

Isentropic efficiency

Calculate superheat temperatures at subsequent lower steam level using isentropic efficiencies (Equation 2)

Starting from the lowest level, calculate the mass flow rates using Equation 1.

Add flow rates to determine the overall flow rates through each level (bottom up)

LP superheat temperature > LP

saturation temperature + T*

YES

STOP

Increase Boiler VHP superheat

NO


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Figure 3: Temperature Entropy diagram for change in level

In the bottom up procedure, the temperature of the lowest steam level pressure

is first used to calculate the steam mass flowrate for the expansion of steam

between the lowest steam level and the higher pressure next to the lowest one.

This procedure is sequentially repeated until the interval for highest steam

pressure level. Flowrates at the higher levels are determined from the flowrate in

the lower levels. The flowrate of steam for each expansion interval is a function

of the heat load at that level and the enthalpy change to the condensate

temperature at the given level. Superheated steam is condensed and supplied to

downstream processes at condensate temperature of the steam.

H

Qm

1

Where,

m = mass flow rate

Q = heat load for a given level

H = Enthalpy change from superheat conditions at the given level to

condensate conditions at that pressure

T

S

1

2’

2

P1

P2 Real

Isentropic


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Isentropic efficiency calculation

It is designer’s discretion to use the most appropriate value of isentropic

efficiency for the developed cogeneration targeting method presented in this

paper. On the other hand, information of isentropic efficiency available in the

literature can be also used. Mavromatis and Kokossis[5] developed a

thermodynamic model to estimate the isentropic efficiency of single and multiple

extraction turbines. Varbanov et al.[10] presented equations to determine the

parameters in terms of saturation temperature. Medina-Flores and Picón-

Núñez[11] modified the correlations of Varbanov et al.[10] to obtain the

regression parameters as a function of inlet pressure. The regression parameters

obtained by Varbanov et al.[10] from the turbine data of Peterson and Mann[12]

are shown in Table 2.

max,

max

is

isW

W

B

AWW is max,

max

satTbbA 10

satTbbB 32

2

Where,

is = isentropic efficiency

isH = isentropic enthalpy change

3210 ,,,,, bbbbBA = Regression coefficients

satT = Inlet pressure of the steam

Table 2: Regression coefficients for single extraction turbines[13]

Single extraction back pressure turbines

Wmax< 2 MW Wmax >2 MW

0b (MW) 0 0


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The results are investigated with STAR®, which is Process Integration software

for the design of utility systems for a single process or a group of processes

involving power (electricity) and heat (steam) generation, and associated heat

exchanging and distributing units. The design procedure of utility systems in

STAR® requires information about steam flowrates, heat supply and loads, VHP

(very high pressure) steam specification (e.g. VHP steam generation capacity

and temperature at the outlet of the boiler). At the initial targeting stage, some of

these design parameters are not known. The parameters, such as flowrate from

the boiler, steam level conditions, have to be specified for the detailed design in

STAR®. The information required for the calculation of cogeneration potential

from the utility systems is current flowrate of steam generated, maximum and

minimum flow rates of equipment, thermodynamic model and efficiency of steam

turbines, steam demand and surplus for each steam main, superheat condition of

steam generated from the boiler, etc. STAR® has two models isentropic and THM

model for the calculation of power generation of steam turbine in the detailed

design, while it uses TH and THM model for cogeneration targeting.

3 Optimization of steam levels As explained before, the choice of steam level in the design of site utility systems

are critical to ensure cost-effective generation of heat and power, and its

distribution in the site. In a new design, pressures of steam level can be readily

optimized. However, for the retrofitting of existing systems, opportunities for the

change of steam level conditions are limited. The mechanical limitation for the

steam mains limits a significant increase in steam pressure. However, long term

investment with a proper optimization of the steam levels may be economically

1b (MWoC-1) 0.00108 0.00423

2b 1.097 1.155

3b (oC-1) 0.00172 0.000538


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viable in spite of the fact that the short term investment can not be justified[14].

VHP steam generation in the boiler and hence the fuel costs in the utility boilers

can be decreased by increasing number of steam mains which increases the

heat recovery potential. Number of steam mains has a significant impact on the

cogeneration potential. Therefore, to minimise fuel cost with maintaining high

cogeneration potential, the design should be thoroughly investigated.

Optimization model

In this study, the optimisation framework for determining the cost-effective

conditions of steam mains for the site utility systems had been proposed with

incorporating new cogeneration targeting method proposed in the work. The

optimisation model is formulated in an NLP (non-linear programming) problem

and the details of models are as follows:

Objective Function

The objective function is to minimize the amount of hot utility to be supplied from

the steam generation unit (e.g. boiler). It should be noted that the method

presented in this paper is generic for taking different objective functions, for

example, overall fuel cost, operating profit, etc, as long as the relevant cost

parameters are available.

minimise VHPsourceheatVHPkshifted HH ,,sin

VHPkshiftedH ,sin Enthalpy of shifted heat sink for VHP

VHPsourceheatH , Enthalpy of heat source for VHP

Optimization Variables

iP Pressure at i Steam levels (VHP, HP, MP, LP)

Four steam mains are used in the current optimisation model, as this is most

common in the large-scale industrial plant, while different number of steam


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mains, for example, three levels (HP, MP and LP), can be considered based on

needs and operating characteristics on the plant.

Constraints

Total source and sink profiles are generated from stream data of the site. Design

procedure for manipulating stream data to generate the site profiles is not a part

of this study and those details can be found from Smith [14] and Klemes et al,

[15]. In order to maintain feasibility of heat recovery across steam mains,

constraint between sink and source site profiles is needed. First, the sink is

shifted until the enthalpy of heat source at either of steam levels is the same as

the enthalpy of heat source corresponding to the pinch point, and then enthalpy

difference at each steam levels is always greater than zero.

0,,sin isourceheatikshifted HH i Steam levels (VHP, HP, MP, LP)

Mass balance

The mass flow rate of steam between steam levels is given:

j

j

kjjiH

Qmm

Where,

jim Mass flow rate of steam through turbine between i and j steam levels

kjm Mass flow rate of steam through turbine between j and k steam levels

jQ Heat duty at j steam level

jH Enthalpy extracted by process from superheated steam at j level to reach

condensate conditions

Power is calculated base on the new design algorithm as shown in Figure 2.

Figure 4 shows the model for the determination of optimal steam pressure levels

for a site utility system. The change in the steam pressure levels shifts the site

sink and surplus profiles along with heat demand and supply. Cogeneration


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potential for the site composite is calculated from the new algorithm. The process

is repeated until optimum pressure levels corresponding to minimum value of

objective function are found for the site.

Figure 4: Flowchart to determine optimum steam pressure level

Case Study Cogeneration potential

An illustrative case study is used to test the different methodologies. The four

steam levels considered in this example are very high pressure (VHP), high

pressure (HP), medium pressure (MP), low pressure (LP) at 120, 50, 14 and 3

bar(a) respectively. The heat demand at HP, MP and LP steam levels is 50, 40

and 85 MW respectively. The efficiency of the boiler is assumed to be 100% for

the simplicity, which can be updated, according to boiler data available, and it is

supplying steam at a temperature of 575oC. Water supplied to the boiler and the

condensate returns are both assumed to be at a temperature of 105oC.

In this work, cogeneration targeting methods have been applied to the case

studies with only back pressure turbines. However, it can be easily extended to

condensing turbines. One of the additional constraints on condensing turbine is a

maximum wetness permitted at the exhaust. Wetness factor in the condensing

turbine can be controlled by adjusting the superheat in the steam mains, as

similary treated in the consideration of degree of superheat in LP steam.

New steam level pressure

Calculate shifted sink and source profiles & heat surplus or

deficit at each steam level

Cogeneration potential calculation from new algorithm

Minimum Utility requirement Optimum

Pressure

NO YES


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Table 3: Problem Data Parameters

VHP HP MP LP

Pressure (bara) 120 50 14 3

Saturation

Temperature (°C) 324.7 264 195.1 133.6

Heat Demand

(MW) 0 50 40 85

The isentropic efficiency was calculated as given in Equation 2, while the

mechanical efficiency was assumed to be 100%.

TH Model: The shaftpower targets from TH method are shown in Table 4. The

overall shaftpower calculated from TH model is 33.02 MW. The value of

conversion factor (CF) is assumed to be 0.00135.

THM Model: The targets for the three sections VHP-HP, HP-MP and MP-LP for

THM model are 9.4, 4.7 and 0 MW respectively (Table 4). The overall shaftpower

target from THM model was 14.2 MW.

Sorin’s Methodology: The work in the bottom section is used to calculate the

heat load in subsequent top section as described in the methodology in the

previous section. Shaftpower targets for VHP-HP, HP-MP and MP-LP of 18.2,

14.46 and 8.77 MW are shown in Table 4.

New Method

Table 4 shows the shaftpower targets for VHP-HP, HP-MP and MP-LP sections

of 14.99, 14.37 and 9.75 MW respectively. The main difference between the new

method and existing TH and THM model is the calculation of superheat

temperature for each steam main, as explained previously. Superheat

temperature of the outlet LP steam should be greater than saturation

temperature of LP steam to avoid condensation of vapour at the outlet of turbine


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and thereby reduced performance and efficiency. The amount of superheat in

VHP steam determines the superheat in LP steam. In the new algorithm, the

superheat in VHP steam from the boiler is a variable and is adjusted by trial and

error to ensure the superheat in LP steam.

Figure 5: Results of the new method

STAR® Simulation – Constant Isentropic Efficiency

Once the steam levels and the heat surplus and deficit are known, a detailed

design procedure is used for the optimal design of the utility systems or to find

out the optimum operating conditions for an existing design. However, as

discussed before, the detailed design requires some additional parameters such

as flowrates and superheat steam temperatures. These additional parameters

are specified by trial and error. STAR® was used to test the targeting potential

against the actual production from the steam turbine. The shaftpower was

calculated by the isentropic model with isentropic efficiency calculated as shown

in Equation 2. The utility systems consist of a boiler supplying VHP steam at

575oC. The steam is passed from the boiler to the higher pressure steam main to

lower pressure steam main, via a steam turbine. Any unused steam can be

passed through the vent. The process cooling and heating duty at each steam

VHP Supply

Qusage = 85 MW

Qusage = 40 MW

Qusage = 50 MW

Heat Demand (MW)

VHP

HP

MP

LP

Satu

rati

on

tem

per

atu

re (

C)

14.99 MW

14.37 MW

9.75 MW

248.29 t/h

185.89 t/h

130.7 t/h


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main level is specified as given in Figure 4. The overall turbine shaftpower is

39.12 MW.

Comparison of Cogeneration Targeting Results

Table 4 shows a comparison of cogeneration targeting results from Sorin’s

methodology, new method, TH and THM model in STAR®. A detailed design

simulation in STAR® with the constant isentropic method is used to compare the

shaftpower targets from the different methodologies. As shown in Table 4 the

total power target of 41.43 MW from Sorin’s methodology is significantly different

from the detailed design procedure of 39.0 MW with an error of 6.2%. The

shaftpower target obtained from TH model of 33.02 MW is 15.3% different from

the shaftpower obtained from the detailed design procedure. Similarly, THM

model target is 63.85% different from the actual shaftpower from the detailed

design procedure. These discrepancies in the shaftpower targets are due to the

assumptions used in these models. The shaftpower target obtained from the new

method of 39.12 MW is only 0.31% different from the detailed design procedure

in STAR®.

Figure 6: STAR® simulation isentropic efficiency


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Table 4: Comparison of cogeneration targeting results

Optimization of steam levels

Site data was taken from an example available in the literature [16]. Site sink and

source profile is shown in Figure 7. Four steam mains are available at very high

pressure (VHP), high pressure (HP), low pressure (LP) and medium pressure

(MP) respectively. Sink profile is shifted by the minimum of the enthalpy

difference between the source and sink, which identifies site pinch point for the

utility system.

0

50

100

150

200

250

300

-500 -400 -300 -200 -100 0 100 200 300 400

Enthalpy (MW)

Tem

pera

ture

(o

C)

Sink

Source

Shifted Sink Profile

Figure 7: Sink and source profiles for a given site

The site utility grand composite curve (SUGCC) plots the difference between the

hot and the cold composite curves as shown in Figure 8. The heat generation

and use at individual steam level is shown in Figure 8-Figure 11. Figure 9 plot the

Methodology Total

(MW)

VHP-HP

(MW)

HP-MP

(MW)

MP-LP

(MW)

Sorin’s methodology 41.43 18.2 14.46 8.77

New Method 39.12 14.99 14.37 9.75

TH Model in STAR® 33.02 14.35 11.62 7.06

THM Model in STAR® 14.1 9.4 4.7 0

STAR® Simulation – Constant

Isentropic Efficiency

39.0 14..85 14.78 9.37


26

cogeneration potential between different steam levels as expansion zones for

steam turbines. The power output for these zones for the optimized case, based

on the new algorithm, is found to be 7.69 MW.

0

50

100

150

200

250

300

350

400

0 20 40 60 80 100 120 140 160 180 200

Enthalpy (MW)

Tem

pera

ture

(o

C)

Figure 8: Site Utility Grand Composite Curve with the optimum steam levels

0

50

100

150

200

250

300

350

400

0 10 20 30 40 50 60 70 80

Enthalpy (MW)

Tem

pera

ture

(o

C)

Figure 9: Site Utility Grand Composite Curve with cogeneration areas

0

50

100

150

200

250

300

350

400

-500 -400 -300 -200 -100 0 100 200 300 400

Enthalpy (MW)

Tem

pera

ture

(o

C)

Sink

Source

Figure 10: Site profile targets for steam generation and steam usage


27

0

50

100

150

200

250

300

350

400

-450 -400 -350 -300 -250 -200 -150 -100 -50 0 50 100

Enthalpy (MW)

Tem

pera

ture

(o

C)

Figure 11: Site profile with cogeneration potential area

The objective function is the minimization of the utility cost. The hot utility is

supplied as VHP steam from the boiler. The optimization framework described in

previous section and the model calculations are performed in Microsoft Excel®.

The size of the model and the optimization problem is small and therefore solver

function in Microsoft Excel® can be effectively used for the minimization of the

utility cost. The number of steam levels has been assumed constant as four

corresponding to VHP, HP, MP and LP respectively. Steam pressures at each

level are the design variables. They affect both the level of heat recovery and the

cogeneration potential, via the steam turbine network[14].

Table 5 shows the base case conditions for the four steam levels. Optimum

steam level pressure and temperature along with heat load at each level is

shown in Table 6. The optimum pressure in the steam mains for the lowest utility

cost are 180, 46.55, 12.26 and 2.25 bar in the VHP, HP, MP and LP steam loads

respectively. The minimum VHP steam generation required from the boiler is

70.22 MW, while the VHP steam flowrate requirement from the boiler is 88.16

t/hr. Steam generation required at VHP mains has been reduced from 105.20

MW to 70.22 MW for the optimized case. However, the cogeneration potential

reduced from 8.8 MW for base case to 7.67 MW for the optimized case.

Therefore, increasing the heat recovery reduces the steam generation from the

boiler as well as the cogeneration potential for this particular example. If power

generation in the site should be increased, then additional VHP steam is

generated to pass through steam mains.


28

Table 5: Base case steam levels[16]

Pressure (bar) Temperature (oC) Heat Load (MW) Saturation temperature (

oC)

180 625 105.20 357.14

50 458.74 137.01 264.09

10 322.1 125.29 180.04

2 143.63 81.98 120.36

Table 6: Optimized steam levels

Pressure (bar) Temperature (oC) Heat Load (MW) Saturation temperature (

oC)

180 625 70.22 357.14

46.65 449.21 113.45 259.79

12.26 308.08 107.57 189.09

2.25 214.48 55.34 124.10

This optimisation framework can be extended to accommodate other economic

scenarios (e.g. to minimise the fuel costs with maintaining the same cogeneration

potential) or practical constraints (e.g. the number of steam levels allowed).

4 Low Grade heat upgrade

4.1 Available technologies

Low grade heat source can be very useful to provide energy to the heat sink by

upgrading low-grade energy (e.g. low pressure steam). The upgrade of low grade

heat can be carried out by heat pump, absorption refrigeration, thermo

compressor, etc, by recovering and/or upgrading waste heat from various

sources (e.g. gas turbine exhaust) and utilising them with the wide range of

applications (e.g. drying and boiler feedwater heating).

4.1.1 Vapour compression heat pump

Heat pump transfers the low grade heat at the lower temperature to higher

temperature heat by the compressor. Heat pump has been used in petroleum

refining, and petrochemicals, wood products, pharmaceuticals, utility system etc.

[17]. Figure 12 shows a typical closed cycle heat pump. The heat from lower

temperature source is transferred to the working refrigerant in the evaporator.

Electric or mechanical energy is used in the compressor to increase the pressure

of the vapour from the evaporator. High grade heat at higher temperature is


29

released from the condenser. Pressure of the vapour is reduced by throttle valve

to lower its temperature and convert it to liquid to exchange heat with low grade

heat source. The main issue with the utilization of the heat pump is that it uses

expensive external energy to convert low grade heat into high grade heat. In

general, one unit of high grade electrical energy can produce 2-4 units of high

grade thermal energy.

Figure 12: Heat pump cycle [18]

Co

E

Q

QCOP

3

Where,

COP = coefficient of performance

EQ = Heat received at low temperature by the evaporator

CoQ = Electric power supplied in the compressor

4.1.2 Absorption Systems

Low grade heat can be recovered by absorption with three different types of

equipments absorption refrigeration, absorption heat pump and absorption

transformers respectively. Iyoki and Uemura [19] compared the performance of

Condenser

Compressor Prime

Mover

Evaporator

Heat from lower temperature source

Throttle

valve

Mechanical

work input


30

absorption refrigeration, absorption heat pump, and absorption transformer for

water-lithium bromide zinc chloride calcium bromide system.

a) Absorption refrigeration – There has been extensive work in literature on

absorption refrigeration system, with both experimental [20] and simulation

studies [21, 22] to determine the performance of absorption refrigeration. A

schematic diagram of ammonia-water absorption refrigeration cycle is shown in

Figure 13. Ammonia vapour at high pressure transfers heat to neighbourhood in

the condenser. Liquid ammonia from the condenser is passed through an

expansion valve to reach the evaporator pressure. Heat is transferred from the

low temperature heat source to convert liquid ammonia to vapour state.

Ammonia vapour is absorbed by a weak solution of water and ammonia to form a

concentrated solution of ammonia-water at the bottom of absorber. This

concentrated solution is passed to the generator for the production of ammonia

vapour while the lean solution from the generator is passed back to the absorber

unit. Low grade heat is used in the generator for the production of ammonia

vapour. Lean ammonia solution from the generator exchanges heat with the high

concentration ammonia solution from the absorber.

Figure 13: Ammonia water absorption refrigeration cycle [20]


31

The coefficient of performance for an absorption refrigeration system is defined

as the ratio of heat removed from the evaporator to heat supplied in the

generator.

G

E

Q

QCOP

4

Where,



GQ = High temperature heat used in the generator

b) Absorption heat pump – A single stage absorption heat pump consists of a

generator, absorber, evaporator, condenser and heat exchanger. High grade

heat is supplied at higher temperature to the generator to separate the refrigerant

from the solution. Low grade waste heat is supplied to the evaporator, while

medium temperature heat is released from the condenser. Thermal energy at

higher temperature is used to convert low grade heat into high grade heat.

Coefficient of performance of an absorption heat pump is the ratio of heat

removed from the medium temperature heat removed form the absorber and

condenser to the high grade heat supplied in the generator.

G

CA

Q

QQCOP

5

Where,


AQ = Heat released by the absorber

CQ = Heat released by the condenser


c) Absorption heat transformer – The basic schematic diagram of absorption

heat transformer is shown in Figure 14. Absorption heat transformer consists of

the same units as absorption heat pump. However, the main difference is that


32

evaporator and absorber are maintained at a higher pressure, while in absorption

pump they are at a lower pressure. Low grade heat is used in the generator and

evaporator to produce heat at higher temperature in the absorber. The process

can be described briefly as follows: High pressure refrigerant vapour from an

evaporator is absorbed into the lean refrigerant absorbent solution in the

absorber. High pressure strong solution of refrigerant absorbent is passed via a

throttle valve to reduce the pressure. This solution exchanges heat with weak

solution from a generator, before it reaches the generator. Low temperature heat

in the generator is used to separate the refrigerant from the solution. Refrigerant

vapour from the generator is condensed in a condenser. The refrigerant is

subsequently pumped to higher pressure where it gains heat at low temperature

to convert into vapour.

Figure 14: Absorption heat transformer (Ammonia water)

The ratio of high temperature heat from the absorber to the low grade heat

supplied in the generator and evaporator is defined as the coefficient of

performance of absorption transformer.

EG

A

QQ

QCOP

6

Where,

Heat Exchanger

Absorber Evaporator

Generator Condenser


33


AQ = Heat released by the absorber

EQ = Heat consumed in the evaporator


4.1.3 Boiler feed water (BFW) heating

Low grade heat can be used to increase the temperature of make-up water to

reduce the fuel cost in the boiler. Additional heat exchanger capital cost is

required for exchange of heat between the boiler make up water and low grade

heat. The increase in temperature of make up water using low grade heat

decreases the fuel consumption in the boiler.

4.1.4 Organic Rankine Cycle (ORC)

A Rankine cycle for extracting electricity from waste heat sources is possible with

the use of organic fluids as working fluids. Efficiency of operation of Rankine

cycle depends on conditions of the cycle and working fluid. A typical organic

Rankine cycle consists of an evaporator, turbine, condenser and pump

respectively (Figure 15). Organic fluid such as benzene, toluene, p-xylene and

refrigerants R113 and R123 [23] have been used as working fluids in ORC.

Working fluid vaporises by exchanging heat with low grade heat in the

evaporator. Vapour is passed through turbine for generation of electricity. Vapour

is condensed in condenser at lower temperature and releases heat to the outside

atmosphere. Organic fluid is raised from lower pressure to high pressure in the

pump. The amount of energy consumed in pumping the fluid is considerably low.


34

Figure 15: Organic Rankine Cycle (ORC) Efficiency of ORC is defined as the ratio of power generated by the turbine to the

low grade energy supplied in the evaporator.

E

turbORC

Q

P

7

Where,

ORC = Efficiency of ORC


turbP = Electric power generated by the turbine

4.1.5 Thermo-compressor

Thermo-compressor uses high pressure steam to compress low or intermediate

pressure waste steam into medium pressure steam. Figure 16 shows a thermo-

compressor where high pressure steam enters as a high velocity fluid, which

entrains the low pressure steam by suction. The resulting mixture is compressed

and discharged as a medium pressure steam from the divergent section of the

thermo-compressor. The main advantage of thermo compressor is high reliability

and less compression power requirement.

Turbine

Pump

Condenser


35

Figure 16: Thermo compressor1

4.1.6 Drying

Biomass (wood, bagasse, grass, straw, agriculture residues, etc.) have

significant amount of moisture. This moisture reduces the theoretical flame

temperature as a part of heat of combustion is used in evaporation of moisture

from the biomass [24]. Calorific value and theoretical flame temperature from the

biomass fuels can be increased by drying. Effective use of industrial waste heat

in drying of biomass increases the overall efficiency of the process, leading to

significantly lesser amount of fossil fuel to be burned and hence much less green

house emissions.

4.2 Algorithm

Once the number of steam levels and their pressure has been determined by

optimization in total site profiles, the performance of the system can be either

optimized to obtain the best design, or to obtain the optimum operating

conditions for an existing design, considering the part load performance of the

equipment. The simulation and optimization of the utility systems require

accurate and yet simpler model for each element of the system. Varbanov [10]

and Aguillar [25] developed simple models for the equipments in the utility

systems. Models developed by Aguillar [25] have been adopted for the purpose

of optimization which determines the optimum design (i.e. the configuration of

utility systems) or operating conditions in this work (Appendix A).

1 (http://www.em-ea.org/Guide%20Books/book-2/2.8%20Waste%20Heat%20Recovery.pdf)


36

The algorithm for evaluation of integration of low grade heat upgrade

technologies with an existing site utility system is shown in Figure 17. The

characteristics of low grade energy such as available heat load at temperatures

for use in heat pump, ORC, and boiler feed water heating is obtained from total

site sink and source profiles. HYSYS simulation is used to obtain the

performance indicators such as COP, efficiency, purchase cost etc. for low grade

heat upgrade technology. Heat load is varied for the HYSYS simulation to

calculate the change in performance and purchase cost. This information is fed

to the optimization framework for calculating the overall annual cost with

integration of these design technologies. The optimization framework [25] is used

for minimization of overall annual cost or operating cost minimization for a

multiperiod operational, retrofit or grassroots design problem. Linear models

have been derived for all the energy equipments so that MILP optimizers can be

used for optimization to reduce the computational cost.

Figure 17: Algorithm for evaluation of low grade heat upgrade technology

4.3 Case study

The various design options for low grade heat upgrade are evaluated with the

help of a case study. The base case design is shown in Figure 18. The base

design consists of four boilers each with capacity of 40 kg/s. There are four back


37

pressure turbines for generation of electricity from VHP to HP and one back

pressure turbine between HP and LP steam levels. Two multistage turbines are

available for expansion of steam between HP-MP and MP-LP respectively. Four

mechanical pumps having a steam turbo driver and an electric motor supply the

feed water to the boiler.

Figure 18: Base case design [25] Site data for heat load, electricity demands, pump electricity demand,

condensate return and cooling water is shown in Table 7. The site operating

seasons are divided into two major categories summer and winter, with 67% of

year as winter. The ambient temperature, relative humidity, electricity natural gas

and fuel oil price is shown in Table 8. The total number of working hours for the

site is assumed to be 8600 hrs per year. The latent heat values for fuel oil and

natural gas are 45 and 50.24 MJ/kg respectively.

Table 7: Total site data - Requirements for the utility system

Units Winter Summer

Electricity demand MW 62 68


38

VHP steam demand

MW 116.36 110.82

HP steam demand MW 30.61 21.4

MP steam demand

MW 16.67 9.34

LP steam demand MW 88.54 73.62

Total steam demand

MW 252.17 215.17

Condensate return % 80 80

Power Pump 1 MW 5.2 5.0




Process CW demand

MW 200 300

Table 8: Site conditions

Season Units Winter Summer

Fraction of the year % 67 33

Ambient temperature

oC 10 25

Relative humidity % 60 60

Electricity prices Peak ($/kWh) 0.07 0.08

Off- Peak ($/kWh) 0.05 0.05

Peak hours /day Hrs 7 12

Fuel Oil price $/kg 0.19 0.19

Natural gas price $/kg 0.22 0.22

Raw water price $/ton 0.05 0.05

Grand composite curves (GCC) of the individual process are modified by

removing the pockets corresponding to additional heat recovery within the

process. These modified process GCC are then combined together to form the

total site sink and source profile (Figure 19(a)). Sink profile is shifted until the

source and shifted sink profile touch each other (Figure 19(b)) or the source and

the sink steam generation and consumption lines touch each other

corresponding to site pinch. Site utility grand composite curve (SUGCC)

represents the horizontal separation between the source and the sink. Steam

demand at VHP, HP, MP and LP levels are 110.8, 21.4, 9.3 and 73.6 MW

respectively. Power generation potential is represented as areas in SUGCC with


39

VHP-HP, HP-MP and MP-LP cogeneration potential of 79.8, 58.4 and 49.1 MW

respectively (Figure 19(c)).

-400 -300 -200 -100 0 100 200 300 4000

50

100

150

200

250

300

350

Enthalpy (MW)

Tem

pera

ture

(oC

)

-400 -300 -200 -100 0 100 200 300 4000

50

100

150

200

250

300

350

Enthalpy (MW)

Tem

pera

ture

(oC

)

(a) (b)

0 50 100 150 200 2500

50

100

150

200

250

300

350

Enthalpy (MW)

Tem

pera

ture

(oC

)

(c)

Figure 19: Site composite curves; (a) Site source and sink composite curve (b) Site source and shifted composite curve with the cogeneration potential area (c) Site utility grand composite curve (SUGCC)

4.4 Results and discussions

4.4.1 Integration of heat pump

HYSYS model heat pump

A model of heat pump has been simulated in HYSYS. It consists of four

equipments evaporator (E-102), compressor (K-100), condenser (E-100) and a

throttle valve (VLV-100). Refrigerant R112-a is used as a working fluid. Low

grade heat is supplied in the evaporator at the temp of 115oC. High grade electric

energy is used in the compressor to raise the pressure of the vapour. LP steam


40

is generated from the condenser at temperature of 150oC. Throttle valve is used

to reduce the pressure of the vapour liquid mixture from the condenser.

Figure 20: Vapour compression heat pump

Figure 21 shows the variation of COP for heat pump system with respect to

variation in the evaporator duty. COP varies within a small range from 3.24-3.31

and can be assumed to be constant for the refrigerant (R-112a) and the

corresponding heat pump cycle (Figure 20). COP of 3.3, means that 1 MW of

electric energy and 2.3 MW of low grade energy generate 3.3 MW of high grade

energy.


41

3.23

3.24

3.25

3.26

3.27

3.28

3.29

3.3

3.31

0 10000 20000 30000 40000 50000 60000 70000 80000 90000

Evaporator Duty

CO

P

Figure 21: COP with respect to evaporator duty

Purchase cost of heat pump

Purchase cost of heat pump is calculated as the sum of the cost of evaporator,

condenser, and compressor. Purchase cost of heat pump is approximated based

on a linear correlation between the cost and the evaporator duty.

BHAPC evapumpheat 8

Where,

pumpheatPC = Purchase cost heat pump

evaH = Evaporator duty (MW)

A, B = Regression coefficients

A = 0.1 MM$/MW

B = 1.15 MM$

Purchase cost

y = 0.0001x + 1.1491

0

2

4

6

8

10

12

0 10000 20000 30000 40000 50000 60000 70000 80000 90000

Evaporator duty (kW)

Pu

rch

ase C

ost

(MM

$)

Figure 22: Linear correlation between purchase cost and evaporator duty


42

The total site source and sink profile before and after integration of the heat

pump is shown in Figure 23 and Table 9. LP steam demand changes from 73.62

to 18.67 MW in summer and from 88.54 to 33.59 MW in winter. Low grade heat

is extracted from the site source only till 115oC corresponding to temperature diff

of 10oC in the evaporator of the heat exchanger. COP of heat pump as

calculated from HYSYS simulations is 3.3. Therefore, the external electricity

consumption from the site increases as shown in Table 10 from 68.82 to 85.42

MW in summer and from 62.2 to 78.8 MW in winter.

Table 9: LP steam demand before and after integration of heat pump

Summer (MW) Winter (MW)

Before heat pump 73.62 88.54

After heat pump 18.67 33.59

Table 10: Electricity demands before and after integration of heat pump




-400 -300 -200 -100 0 100 200 300 4000

50

100

150

200

250

300

350

Enthalpy (MW)

Tem

pera

ture

(oC

)

Figure 23: Site composite curve with heat pump integration

Annualized capital cost with operational optimization of the existing plant

Operational optimization of total site annual cost with the integration of heat

pump is shown in Table 10. External power cost increases from 22.67 MM$ to


43

35.03 MM$ after integration of heat pump, while fuel cost decreases from 93.08

to 82.09 MM$. Total annual cost increases to 118.67 MM$/yr from 116.32

MM$/yr after integration of heat pump. Therefore, with these costs of fuel and

electricity and the capital cost of heat pump it is not economic to set up a heat

pump.

Table 11: Annual costs before and after integration of heat pump

External Power (MM$) Fuel Cost (MM$)



4.4.2 Integration of Organic Rankine Cycle (ORC)

HYSYS model ORC

HYSYS is used to calculate the efficiency and the purchase cost function for

ORC. ORC set up consists of an evaporator (E-100), turbine (K-100), condenser

(E-101) and a pump (P-100). Benzene is used as the organic working fluid. Low

grade heat at 110 oC is used to vaporize benzene at high pressures (1.145 bar).

Benzene vapour is used to drive a turbine along with reduction in pressure (14.5

kPa). Vapour stream from turbine at low pressure condensed in the condenser

(27oC). Pump is used to pump the low pressure organic liquid stream to high

pressure (1.145 bar) before being fed to the evaporator.


44

Figure 24: Organic Rankine Cycle (ORC)

Efficiency of ORC

Figure 25 shows the variation of efficiency of ORC with respect to evaporator

duty. The efficiency of ORC is approximately constant around 11% with the

variation in evaporator duty.

0

2

4

6

8

10

12

0 2 4 6 8 10 12

Evaporator duty (MW)

Eff

icie

ncy

Figure 25: ORC efficiency with evaporator duty Purchase cost of ORC

Purchase cost of ORC is given as the total cost of equipments such as

condenser, evaporator and turbine. The cost of the evaporator and condenser is


45

obtained from the online database2, while turbine cost is obtained from Peters et

al. [26]. Purchase cost of ORC is approximated based on a linear correlation

between the cost and the evaporator duty.

BHAPC evaORC 9

Where,

ORCPC = Purchase cost ORC

evaH = Evaporator duty (MW)

A, B = Regression coefficients

A = 0.01 MM$/MW

B = 25.1 MM$

y = 1E-05x + 0.2506

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 10000 20000 30000 40000 50000 60000 70000 80000

Evaporator Duty (kW)

Pu

rch

ase C

ost

(MM

$)

Figure 26: Linear correlation between purchase cost and evaporator duty The total site source and sink profile after integration of heat pump is shown in

Figure 27. Low grade heat corresponding to 62.11 MW is saved corresponding to

a temperature of 105oC. Cold utility requirement is reduced by 62.11 MW. As

shown before with the efficiency of 11%, the amount of electrical energy is

reduced from 68.82 to 61.99 MW during summer and from 62.2 to 55.39 MW

during winter. Purchase cost of ORC corresponding to given evaporator duty is

17.13 MM$.

2 http://www.matche.com/EquipCost/Compressor.htm.

http://www.matche.com/EquipCost/Compressor.htm


46

-400 -300 -200 -100 0 100 200 300 4000

50

100

150

200

250

300

350

Enthalpy (MW)

Tem

pera

ture

(oC

)

Figure 27: Site composite curve with ORC integration

Table 12: Electricity demands before and after integration of ORC




4.4.3 Integration of Absorption refrigeration

HYSYS model of absorption refrigeration

Absorption refrigeration system (Figure 28) consists of absorber (T-101), pump

(P-100), heat exchanger (E-104), generator (T-103), evaporator (E-100), and

condenser (E-103). Heat is released at temperature of 32oC to the surrounding at

a pressure of 13 bar in the condenser (E-103). Ammonia vapours are passed

through a throttle valve (VLV-101) to reduce the pressure to 14.50 kPa before

they can absorb heat from the surroundings at low temperature (-5oC) as

refrigeration load in the evaporator (E-100). Ammonia vapour is absorbed with

the lean solution of ammonia in the absorber (T-101). Heat is released to the

surroundings from the absorber. Concentrated solution of ammonia water is

pumped from 14.59 kPa to 13 bar into the generator. Low grade heat is used in

the generator (T-100) to separate ammonia from the concentrated solution to

produce a lean solution of ammonia water. Heat is exchanged between outgoing


47

lean solution of ammonia water and incoming strong solution in exchanger T-

103.

Figure 28: HYSYS model of absorption Refrigeration Coefficient of performance

Low grade heat is used to provide the heat for refrigeration load for the system.

The low grade heat supplied in the generator is 265.4 kW, while 67.86 kW of

heat is removed as refrigeration load from the evaporator, with a COP of 0.26.


48

-400 -300 -200 -100 0 100 200 300 4000

50

100

150

200

250

300

350

Enthalpy (MW)

Tem

pera

ture

(oC

)

Figure 29: Low grade heat can be used for refrigeration load on site

4.4.4 Boiler feed water heating Integration

Low grade heat is used to raise the temperature of make up water to deaerator

from 25oC to 101.3oC. This reduces the cost of fuel consumed in the boiler. The

benefits of BFW heating depends on condensate recycling process and

condensate management. BFW heating doesn’t change the hot utility

requirement from the base case. However, the cost of fuel required to supply the

hot utility required decreases from 93.08 to 80.57 MM$/yr due to decrease in the

heating required for boiler feed water. The overall energy cost decreases from

117.83 MM$/yr in the base case to 107.63 MM$/yr.

Absorption Refrigeration


49

-400 -300 -200 -100 0 100 200 300 4000

50

100

150

200

250

300

350

Enthalpy (MW)

Tem

pera

ture

(oC

)

Figure 30: Temperature of make up water to deaerator is increased by low grade heat

4.4.5 Comparison of design options

Techno economic analysis

Table 13 shows the comparison between the various low grade heat upgrade

options. Heat pump decreases the hot utility requirement by reducing the low

pressure steam demand for the system. Hot and low utility cost in the system

decreases from 93.08 to 82.09 MM$/yr and 0.98 to 0.90 MM$/yr respectively.

However, heat pump increases the electricity import cost for the site from 23.77

to 36.06 MM$/yr. The overall operating cost increases from 117.83 to 119.06

MM$/yr with the introduction of heat pump. Therefore, heat pump is not

economic for the current case study with the given cost of electricity and fuel.

Integration of ORC decreases the cold utility requirement and therefore reduces

the total utility cost from 94.06 to 94.02 MM$/yr. Electricity produced from ORC

reduces the cost of electricity import from 23.77 to 20.21 MM$/yr. The total

energy cost decreases from 117.82 MM$/yr in the base case to 114.23 MM$/yr

for integration with ORC. Absorption refrigeration reduces the cold utility cost

from 0.98 to 0.90 MM$/yr. However, the main advantage of absorption

Boiler feed water heating


50

refrigeration is reduction in electricity cost in vapour compression refrigeration by

5.46 MM$/yr. BFW heating reduces the cost of hot utility requirement from 93.08

to 80.57 MM$/yr. Total energy cost decreases from 117.83 to 106.63 MM$/yr.

This corresponds to an annual savings of 9.51% in the operating cost. BFW

heating is the most economical options amongst the heat upgrade technologies.

However, benefits of BFW heating depends on the condensate recycle policy

and condensate management.

Table 13: Techno economic evaluation of low grade heat upgrade technologies

Options Hot utility (MW) Cold utility (MW) Hot utility cost (MM$/yr)

Cold Utility cost (MM$/yr)

Total utility cost (MM$/yr)

Electricity import (MM$/yr)

Total energy cost (MM$/yr)

Winter Summer Winter Summer

Base case 252.17 215.17 368 368 93.08 0.98 94.06 23.77 117.83

Heat Pump 197.23 160.23 344.11 344.11 82.09 0.90 82.99 36.07 119.06

ORC 252.17 215.17 344.11 344.11 93.08 0.94 94.02 20.21 114.23

Absorption refrigeration

252.17 215.17 344.11 344.11 93.08 0.90 93.98 23.77 117.75

BFW heating

252.17 215.17 368 368 80.57 0.98 81.55 25.08 106.63

5 Over the fence process Integration

There has been limited work in literature on the integration of waste heat with DH

network. Ajah et al. [27, 28] evaluated the technical, economic and environment

feasibility of integration of waste heat from pharmaceutical industry with DH

network in the Netherlands. In this work, low grade heat is upgraded by chemical

and mechanical heat pumps [27, 28]. Holmgren [29] studied the impact of

integration of waste heat from industries, and waste incineration into the DH

network. They studied the impact of the price of electricity on the performance of

CHP system. The waste heat from industry accounted for approximately 15% of

the overall heat demand. However, this work does not take into account the part

load performance of CHP and boiler after the integration of waste heat.

Svensson et al. [30] and Johnsson et al. [31] evaluated the tradeoffs for the

usage of excess heat in pulp mill to supply the internal units inside pulp mill and

external consumers (DH). They evaluated the integration of new energy-efficient


51

technology, such as process-integrated evaporation, drying, etc. identified by

Axelsson et al. [32, 33], in order to increase the surplus energy from the pulp mill.

This surplus heat can be either integrated within the process or exported to the

external consumers. The objective of their work was to study the techno-

economic impact of integration of excess heat with an external DH system.

However, their model does not account for the part load performance of CHP and

boilers. Heat load and electricity production in the DH system is variable for

different time of the day, seasons, etc. Hence, the performance of DH system is

dependent on the part load performance of CHP.

Carcasci and Cormacchione [34] analyzed the part load performance of gas

turbine CHP systems for providing heat to the DH network. They discussed

different strategies for part load operation of the gas turbine CHP plant. However,

their work was limited to data for part load operating strategies for gas turbines

and does not produce a generic model for CHP units. Furthermore, their work

does not consider the optimization of the performance of CHP based on the heat

demand profile for the locality. It is necessary to simultaneously account for heat

and electricity produced from the DH system to satisfy the varying heat demand

for DH system. The integration of waste heat from the process site with the DH

network reduces the heat produced by DH itself. This decrease in energy

production reduces the part load on the CHP and boilers which operate under

partial load. The economic feasibility of the integrated system is determined by

the part load performance in the DH systems. Therefore, it is imperative to study

the impact of integration of DH with the waste heat from the process industry.

The aim of this work is to: a) evaluate the impact of integration of waste heat on

part load performance of energy equipments in DH systems, and b) develop a

novel heat integration tool for the integration of DH and industrial total site

profiles.


52

The techno-economic impact of integration of the low grade heat from process

industry with DH systems is analyzed through a case study in this work. This

paper is organized as follows: the first section discusses the importance of waste

heat integration with DH and the lack of relevant literature in this direction. The

new design methodology proposed in this paper is discussed in second section,

followed by a case study. The results are discussed in the penultimate section

followed by conclusions and future suggestions for work.

5.1 Design Methodology

DH system provides a unique opportunity to produce heat and electricity by the

combination of boiler, combined heat and power (CHP), etc. There is a clear

incentive to identify low grade heat available from the industrial plant and to

facilitate waste heat recovery with DH systems as this can reduces the overall

energy cost of the system by reducing the fuel consumption as a whole.

However, there are interactions between power production, heat consumption

and part load performance depending on the relative cost of electricity and heat.

The electricity can be either imported or exported to the grid, depending on the

deficit or surplus of electricity produced by DH. The heat has to be supplied from

the DH system, including the waste heat from the industry. The waste heat from

the industry is not available during shut-down, etc. Therefore, additional

capacities of the DH supply should be provided to maintain the continuous supply

of heat, when waste industrial heat is not available. The CHP and the boiler in

the DH systems operate at part load, depending on heat demand. Therefore, the

part load performance of CHP and boilers should be evaluated for the

optimization of operating cost of DH.

The methodology used in this work is shown in Figure 31. The total site

composite [3, 35] is a process integration tool to integrate the heating and cooling

requirements of different processes within a total site. It gives target for the

steam generation and consumption, heat demand form the hot utility boiler and

the overall cold utility demand for the overall site [3, 7, 35, 36]. The amount of the

total low grade heat available from a total site is determined by total site


53

composite as the heat available above the practical working temperature for DH.

The practical working temperature is defined as the sum of the temperature at

which heat is supplied to DH and the minimum approach temperature

considered.

The temperature at which waste heat from industries can be supplied to DH is

dependent on the supply temperature for the DH network and minimum

temperature of approach. The heat distribution network of DH network consists of

pipes supplying heat at 90-120oC and pipes returning the used water from DH

network at 40-70oC[2]. The minimum approach temperature of 15oC is assumed

in this work. The DH system under consideration is taken from the work of

Rolfsman [37] and it consists of waste boiler, oil boiler, oil/gas CHP and mixed

fuel CHP. The details of the DH systems are described in the next section.

The annual operating energy cost of the DH networks is a function of the heat

sold to the DH network, electricity sold to the grid and the cost of the fuel

consumed in the DH network. The demand of heat in the DH network varies with

the time of the day and season. The performance of the DH network is

considered in two cases; with and without integration of waste heat from the

process industry. The optimization framework is formulated to minimise the

annual operating energy costs, subject to process conditions and design

constraints, which allows to identify the most cost-effective way of utilising waste

heat and integrating it with DH systems, as well as to systematically assess the

economic impact of such an integrated design. The mathematical model for

integrating DH systems is detailed in below, including part load performance of

units employed in DH systems, and the objective function for the annual

operating energy cost.


54

Figure 31: Methodology for techno-economic analysis of integration of waste

heat from total site with district heating design

The performance of DH is evaluated with two optimization frameworks with and

without integration of waste heat from process industry. The optimum results are

compared to evaluate the impact of waste heat integration.

5.2 Modelling of energy equipment

a) Combined heat and power (CHP) gas turbine

The power generated from CHP systems is obtained by the equation for the gas

turbine developed by Aguillar [25]. It is a function of the part load of a CHP unit,

heat output from the CHP unit, and the design work load from the CHP unit.

CWB

QfAW D

gt

CHP

CHPCHPCHP

10

CHPW = Power produced by a CHP unit, MW

A, B, C = Regression coefficients

CHPf = Part load fraction for a CHP unit

CHPQ = Heat produced from a CHP unit, MW

D

gtW = Design work from a CHP unit, MW

Total site composite

curves

District heating system

waste boiler

oil boiler

oil/gas CHP

mixed fuel CHP

Waste heat

Annual operating energy cost for district heating before and

after integration of waste heat from the total site


55

The heat from the CHP unit ( CHPQ ) is a direct function of part load ( CHPf ) and the

design heat produced from the CHP unit ( D

CHPQ ). The part load ( CHPf ) is a variable

for the optimization framework.

D

CHPCHPCHP QfQ 11

b) Boilers

Heat produced from a boiler is given as:

D

boiboiboi QfQ 12

Where,

boiQ = Heat produced from boilers

boif = Part load fraction of boilers

D

boiQ = Design heat output from boilers

5.3 Optimization formulation

The optimization problem formulation can be described in terms of the

optimization variables, an objective function, and constraints.

Optimization variables

The performance of the integrated system is influenced by the heat demand

varying with the time of the day, season, and weekday or weekend. The part load

percentage of the energy producers (i.e. boilers and CHP units) are positive

variables. The boilers and CHP units are not required to operate for all the time

duration. Hence, their functioning of boilers and CHP are described by binary

variables, so that operating below their part load limits can be avoided.

Objective function


56

The operating energy cost of the DH network is made up of the cost of fuel for

production of heat and electricity. The electricity sold to the grid and the heat sold

to the consumers is the revenue for DH.

heatelecfuelop CCCC 13

Where,

opC = Annual operating energy cost for the DH system, M$/y

fuelC = Annual cost of the fuel for a DH network, M$/y

elecC = Annual profit by exporting electricity to the grid, M$/y

heatC = Annual profit by selling heat to customers, M$/y

Model constraints

Fuel cost: The annual fuel consumption is dependent on the performance of

boilers and CHP units. The supply of heat in turn is related to the time of the day,

day of the week and the season of the year, etc. The fraction of each of the

variation defines the performance of the energy producing equipment. Fuel

consumption is determined from the heat supplied and the thermal efficiency for

individual energy generating equipment.

i j k

fueli

CHP

D

CHP

kjjki

CHP

i j k

fueli

CHP

D

CHP

kjjki

CHP

i j k

fueli

boi

D

boi

kjjki

boi

i j k

fueli

boi

D

boi

kjjki

boi

fuel

CswkfsQhpddpwf

CswkfsQhpddpwf

CswkfsQhpddpwf

CswkfsQhpddpwfC

4

22

,,

2

3

11

,,

1

2

22

,,

2

1

11

,,

1

/

/

/

/

14

Where,

i = season of the year (winter, summer or transition)

j = day of the week (weekend, weekday)

k = hours of the day (morning, afternoon, evening, night)


57

p = 1-4 represents boilers and CHP units

jdpw = days per week

kjhpd , = hours per day

ifs = fractions for each season in the year

ki

boipf , = part load fraction for a boiler p

D

boipQ = design heat supplied by a boiler p, MW

boip = thermal efficiency of a boiler p

fuelpCs = cost of fuel supplied to a boiler p, M$/MWh

ki

CHPpf , = part load fraction for a CHP unit p

D

CHPpQ = design heat supplied by a CHP unit p, MW

CHPp = thermal efficiency of a CHP unit p

fuelpCs = cost of fuel supplied to CHP p, M$/MWh

wk = number of weeks per year

Electricity cost: The electricity production from a CHP unit is determined by the

part load work from the CHP unit, depending on the heat production by the CHP

unit. It is assumed in this work all the electricity produced from the CHP unit can

be directly exported to the grid and there is no upper limit on the quantity that is

exported to the grid.

electi

i j k

kjkji

CHP

jelec CpwkfshpdWdpwC ,,,

1

15

Where,

elecC = Annual profit by exporting electricity to the grid, M$/y

electCp = Selling price of electricity to grid, M$/MWh



ki

CHPpW , = Power produced from a CHP unit p, MW


58


Heat cost: The heat demand is satisfied by the heat produced from the units. Any

excess heat is loss to the system and hence is minimized by the optimization

framework.

d

boi

kji

boi

d

boi

kji

boi

d

CHP

kji

CHP

d

CHP

kji

CHP

kji

demand QfQfQfQfQ 2

,,

21

,,

12

,,

21

,,

1

,, 16

Where,

i = season of the year (winter, summer or transition)

j = day of the week (weekend, weekday)

k = hours of the day (morning, afternoon, evening, night)

p = 1-4 represents boilers and CHP units

kji

demandQ ,, = Heat demand, kW

kji

pboif ,,

, = part load fraction for a boiler p

kji

pCHPf ,,

, = part load fraction for a CHP unit p

The revenue from heat generation is determined by the heat demand and the

unit price of heat supplied to the consumers. It is assumed that the unit price of

heat is independent of the variation of heat demand.

heati

i j k

kjjkji

demand

heat CpwkfshpddpwQC ,,,

17

Where,

kji

demandQ ,, = Heat demand kW

heatC = Annual profit by selling heat to customers M$

heatCp = Selling price of heat to customers $/kWh




jdpw = days per week


59

Operational limits: The minimum part load fraction of the boilers and CHP units is

greater than 20% and 60% of the design capacity respectively.

CHPCHPCHP xUfx 1*60*100

boiboiboi xUfx 1*20*100

18

where,

CHPx Binary variables for CHP units

boix Binary variables for boilers

CHPf Load percentage for CHP units

boif Load percentage for boilers

U A large number

DH system consists of interconnected units which ensure a constant supply of

heat under varying demand, prices and ambient conditions. There are multiple

demands and operating degree of freedom that can be utilized in the optimization

of DH. In this work, linear models have been used to describe the performance of

energy equipment. The binary variables characterize whether the equipments are

running during time duration. This model presents a novel methodology for the

optimization of DH energy systems under variable demands with and without

integration of waste heat. The optimization problem is modelled as mixed integer

linear programming problem (MILP). The optimization problem is solved using

CPLEX MILP solver in GAMS 2.0.13.0 IDE. The execution time is less than a

second.

5.4 Case Study 1: Integration of industrial waste heat with district heating (DH) systems


60

5.4.1 Waste heat available in an industrial site

Over-the-fence process integration is an interesting concept, where the excess

heat from process industry is supplied to the DH network. This reduces the fuel

consumption in DH systems. The additional costs would include the capital cost

of installation of the additional network to carry the heat from the industry to the

DH site and the operation cost for the supply network. However, another

important factor is the part load performance of the energy equipment of the DH

network and the additional revenue generated by the DH network by using

domestic and industrial waste as the fuel. The current work consists of a boiler

that uses domestic and industrial waste as a fuel. Since it utilizes the waste, the

cost of the fuel to this boiler is negative representing revenue due to the

consumption of industrial and domestic waste.

The economics of integration of process heat with the DH network is based on a

case study of industrial site utility systems and associated site-wide energy use

which was presented by Aguillar [25]. The total site sink and source profile for the

given case study is shown in Figure 32. The steam demand at VHP, HP, MP and

LP levels are 110.8, 21.4, 9.3 and 73.6 MW respectively (Figure 33). The power

generation potential is represented as areas in site utility grand composite curve

with VHP-HP, HP-MP and MP-LP cogeneration potential of 79.8, 58.4 and 49.1

MW respectively when a full steam recovery is made within the site utility

systems (Figure 33).


61

-400 -300 -200 -100 0 100 200 300 400 5000

50

100

150

200

250

300

350

Enthalpy (MW)

Tem

pera

ture

(oC

)VHP

HP

MPLP

CW

Figure 32: Site source and sink composite curve

0 50 100 150 200 250 3000

50

100

150

200

250

300

350

Enthalpy (MW)

Tem

pera

ture

(oC

)

VHP

HP

MP

LP

Figure 33: Site Utility Grand composite curve

The amount of low grade heat available at a temperature higher than 105oC is

62.11 MW as shown in Figure 34. The temperature of 105oC corresponds to a

temperature of supply of 90oC and minimum temperature of approach of 15oC.

Site source profile below 105oC is shifted by 62.11 MW to account for the


62

extraction of low grade energy and hence the CW requirement for the total site

decreases by 62.11 MW. The total site profiles and hence the waste heat from

process industry is variable with respect to different seasons. However, in this

work it is assumed that DH demand is variable, while low grade heat from total

site is uniform throughout the year.

-400 -300 -200 -100 0 100 200 300 400 5000

50

100

150

200

250

300

350

Enthalpy (MW)

Tem

pera

ture

(oC

)

VHP

HP

MPLP

CW

62.11 MW

Figure 34: Low grade heat available from the site profiles

5.4.2 District heating (DH) systems

The existing DH supply system has two CHP (combined heat and power) units; a

mixed-fuel CHP unit and alternative waste and oil CHP unit. The alternative

waste and oil CHP unit can use both waste and oil, depending on the availability.

The system has two boilers; a waste fired boiler and an oil-fired boiler as shown

in Figure 35. Gas turbine/HRSG (Heat Recovery Steam Generator) unit supplies

the electricity and the heat to the DH network and the grid respectively. The

detailed data for performance of the four units on the supply side for Linkoping

network is shown in Table 14 [37]. The negative cost of the waste fuel indicates


63

profit by burning of the waste. The part load heat and electricity output from each

unit is decided by optimization based on whether or not the waste heat from the

industrial process is integrated to the network. The fuel to the oil boiler is the

most expensive with a price of 51.9 $/MWh.

Oil

Waste

Waste Boiler Oil Boiler

-400 -300 -200 -100 0 100 200 300 4000

50

100

150

200

250

300

350

Enthalpy (MW)

Tem

pera

ture

(oC

)

Heat

Electricity

Gas turbine /

HRSG

Fuel

Air

Electricity

Figure 35: Existing district heating system [37]

Table 14: Existing plant data in Linkoping [37]

Plant Heat

Max

(MW)

Electricity

Max

(MW)

Heat

Min

(MW)

Electricity

Min

(MW)

Fuel

Price

($/MWh)

Efficiency Electricity /

heat ratio

Waste

boiler

80 - 16 - -11.25 0.9 -

Oil

boiler

360 - 72 - 51.9 0.85 -


64

Alt.

waste

and oil

CHP

90 47 72 37.6 7.2 0.82 0.52

Mixed

Fuel

CHP

201 59 160.8 47.2 26.7 0.92 0.29

The demand data for residential sites is required to evaluate the performance of

the DH system before and after integration of waste heat. Figure 36 shows a

demand duration profile for a DH system with total heat demand of 1.22 TWh/y.

The heat is sold to DH at 115 $/MWh [2], while electricity is sold to the grid at

140.2 $/MWh [2]. The DH system has a maximum winter load of 175 MW and a

minimum load of 133 MW in summer. The heat produced from DH is more than

the energy demand as shown in Equation 16 for each time interval,

corresponding to time of the days, weeks, seasons , etc.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000135

140

145

150

155

160

165

170

175

Duration (hours/year)

Heat

Load (

MW

)

Figure 36: DH system demand duration profile


65

5.4.3 Feasible distance of heat transfer

The physical distance for the transfer of low grade heat available in an industrial

site to the potential consumers is first evaluated. It is assumed that all the low

grade heat (62.11 MW) from the site can be supplied directly to the potential

consumer. It is also assumed that the available low grade heat is sold to the

consumers at 80 $/MWh and the capital cost for the DH network including pipe

and related equipments is 1460 $/m on an average [2]. The operating cost for

pumping has not been considered in this simple calculation for the feasible

distance of heat transfer. It is assumed that 1% of the heat is lost in

transportation for the every km of distance from the source to the DH network.

The break even point for economic distance to heat transfer is 86.52 km from the

given data.

.

f

D

heat

waste

feasibleAtpm

revenuehpddpyQD

feasible

cos

99.0

19

Where,

feasibleD Feasible distance for heat transfer (km)

wasteQ Waste heat (MW)

dpy Days per year (365)

hpd Hours per day (24)

heatrevenue Revenue generated from selling of electricity (£/MWh)

tpmcos DH installation cost per m (1460£/m)

fA Annualization factor (0.13)

5.4.4 Optimization Results

The effect of waste heat integration on an existing DH network with the part load

performance is considered in this section. The DH network consists of boilers

and CHP systems as energy producers. The reduction in heat demand after

integration with waste heat from an industrial plant decreases the load on boilers

and CHP system. It is assumed in this work that all the waste heat is used by the

DH network. The integration of waste heat changes the amount of heat and


66

electricity produced from the DH systems. Hence, CHP units and boilers in the

DH system are running at part load. This reduces the efficiency in heat and

electricity generation of DH system.

The economic impact of integration is evaluated with the help of a case study.

The objective is to reduce the total operating energy cost under the constraint of

satisfying the overall heat demand for the DH system. The heat generated by the

combination of CHP units and boilers is reduced with the integration of waste

heat. The quantity of heat required from the CHP units interacts with the part load

fraction of CHP as shown in Equation 11. The corresponding power output is

calculated from Equation 10. The revenue for DH system is generated by selling

electricity to the grid, heat to DH consumers and from the consumption of waste,

while the consumption of fuel (oil, and coal) is the expenditure of the system.

The results after optimization for minimising overall annual operating energy cost

are shown in Table 15. The revenue from the generation of electricity that can be

exported to the grid is 56.58 M$/y. Waste heat boiler generates a revenue of 6.27

M$/y, while the cost for using other fuels is 8.97 M$/y. The overall annual

revenue is 202.53 M$/y. The integration of waste heat (62.11 MW) with DH

decreases the amount of the thermal load. This, in turn, reduces the part load on

CHP units, which leads to the decrease in the annual electricity production.

Therefore, the electricity revenue decreases from 56.58 M$/y to 49.26 M$/y. The

details of the part load of CHP units and boilers for the weekend and weekdays

of three seasons are shown in Table 16-Table 19.

The integration of waste heat reduces the heat supplied from CHP units and

boilers. Therefore, the fuel requirement decreases with the integration of waste

heat. However, due to the revenue generated (11.25 $/MWh) from the

consumption of waste fuel in boiler 1 (Table 14), the annual cost of fuel use

increases from 1.7 M$/y to 6.84 M$/y, due to the decrease in the utilization of the

waste fuel to produce steam from the boiler. The overall annual operating energy


67

revenue from the DH system decreases from 202.53 M$/y to 193.26 M$/y.

Hence, the integration of waste heat with the DH network is not economically

viable in the given case study.

0 1000 2000 3000 4000 5000 6000 7000 8000 900060

80

100

120

140

160

180

Duration (hours/year)

Heat

Load (

MW

)

Without integration

With integration

Figure 37: Heat supply with and without integration of waste heat

Table 15: Effect on annual DH cost by integration of waste heat

Revenue

(Electricity)

(M$/y)

Revenue

(Heat)

(M$/y)

Revenue

(Waste Fuel

consumption)

(M$/y)

Cost (Fuel

consumption)

(M$/y)

Annual

operating

energy

Revenue

(M$/y)

Before

integration

56.58 151.20 6.27 1.70 202.53

After

integration

49.26 151.20 0.84 6.84 193.26


68

Table 16: Part load percentage for Boiler 1

Weekday

Morning Afternoon Evening Night

before after before after before after before after

Winter 20.00 28.6

1

82.50 20.00 86.25 20.00 66.25 0

Summer 70.00 0 61.25 0 63.75 0 56.25 0

Transition 83.75 20.0

0

70.00 0 72.50 0 60.00 0

Weekend



Winter 96.25 20.0

0

82.50 20.00 85.00 20.00 66.25 0

Summer 68.75 0 62.50 0 65.00 0 56.25 0

Transition 80.00 20.0

0

70.00 0 72.50 0 60.00 0

Table 17: Part load percentage for Boiler 2

Weekday



Winter 20.00 0 0 0 0 0 0 0

Summer 0 0 0 0 0 0 0 0

Transition 0 0 0 0 0 0 0 0

Weekend



Winter 0 20 0 20 0 20 0 0

Summer 0 0 0 0 0 0 0 0

Transition 0 20 0 0 0 0 0 0

Table 18: Part load percentage for CHP1

Weekday



Winter 96.67 100.00 100.00 86.54 100.00 89.88 100.0

0

89.88

Summer 100.00 93.21 100.00 85.43 100.00 87.66 100.0

0

80.98

Transition 100.00 87.66 100.00 93.21 100.00 95.43 100.0

0

84.32

Weekend


69



Winter 100.00 98.77 100.00 86.54 100.00 88.77 100.0

0

89.88

Summer 100.00 92.10 100.00 86.54 100.00 88.77 100.0

0

80.99

Transitio

n

100.00 84.32 100.00 93.21 100.00 95.43 100.0

0

84.32

Table 19: Part load percentage for CHP 2

Weekday



Winter 0 0 0 0 0 0 0 0

Summer 0 0 0 0 0 0 0 0

Transition 0 0 0 0 0 0 0 0

Weekend



Winter 0 0 0 0 0 0 0 0

Summer 0 0 0 0 0 0 0 0

Transition 0 0 0 0 0 0 0 0

Comparison of the integration of waste heat with DH system and internal

use

The integration of low grade heat upgrade methodologies such as heat pump,

ORC (organic Rankine cycle) , absorption refrigeration, and BFW (boiler feed

water) heating was evaluated in the previous work [38]. The impact of waste heat

upgrade and its utilization within the site is shown in Table 20. The integration of

absorption refrigeration, ORC and BFW heating decreases the overall energy

cost for the total site. However, waste heat upgrading by heat pump compression

increases the overall cost due to the increase in electricity consumption. When

the integration of waste heat with DH systems is compared with integrated

options shown in Table 7, it can be seen that the utilisation of waste heat within

the site, for example, low grade heat recovery and integration for BFW heating, is

more economic, than employing over-the-fence process integration options,


70

based on economic parameters and energy system configurations considered in

this case study.

Table 20: Integration of waste heat within a total site[38]

Options Hot

utility

cost

(M$/yr)

Cold

Utility

cost

(M$/yr)

Total

utility

cost

(M$/yr)

Electricity

import

(M$/yr)

Total

energy

cost

(M$/yr)

Base case 93.08 0.98 94.06 23.77 117.83

Heat Pump 82.09 0.90 82.99 36.07 119.06

ORC 93.08 0.94 94.02 20.21 114.23

Absorption

refrigeration

93.08 0.90 93.98 23.77 117.75

BFW

heating

80.57 0.98 81.55 25.08 106.63

5.5 Case Study 2: Integration of waste heat with a local energy systems

The total site profiles from process industry are now attempted to combine with

the local energy systems for analysing the potential for the integrated system.

The data for local energy systems is based on the information studied by Perry et

al. [36]. The system under consideration consists of a large hospital complex.

The hospital complex consists of 11 streams with temperature ranging from 18oC

to 121oC (Table 21).

Table 21: Process steam data for hospital complex site C[36]

Stream Name Tsupply (oC) Ttarget (

oC) DH (kW) CP (kW/

oC)

1 Soapy water 85 40 23.85 0.53

2 Condensed

steam

80 40 96.4 2.41

3 Laundry

sanitary water

25 55 17.7 0.59

4 Laundry 55 85 77.4 2.58

5 Boiler feed

water

33 60 7.2 0.24


71

6 Sanitary water 25 60 77 2.2

7 Sterilization 30 121 12.74 0.14

8 Swimming pool

water

25 28 151.68 50.56

9 Cooking 30 100 59.5 0.85

10 Heating 18 25 100.8 14.4

11 Bedpan washers 21 121 5 0.05

Hot water and district heating requirements for the locality is given in Table 22.

DH hot water is supplied at 60oC and 80oC for hot water supply and residential

heating respectively.

Table 22: Process data for industrial and residential complexes D[36]

Stream Name Tsupply (oC) Ttarget (

oC) DH (MW) CP (kW/

oC)

1 District

heating

15 60 6.00 133.33

2 Hot water 15 80 5.00 76.92

Site profiles from the industrial site in Figure 32 have been combined with the

process data for district heating Table 21 and Table 22 to produce the overall

composite curve for the integrated system. The total site sink and source

composite curves are shown in Figure 38. The overall heat requirement from the

boiler to supply VHP steam decreases from 110.8 MW for the standalone

process industry site to 83.13 MW for the integrated system. This corresponds to

a saving of 25% in heat for the integrated system. The total site profile is a

simplified analysis tool for analysis of the possibility of integration between

process industry and local heating system. However, for a real integration, there

would be considerable cost for the construction of heat recovery and supply

system including heat exchangers, pipes, etc.


72

-400 -300 -200 -100 0 100 200 3000

50

100

150

200

250

300

350

Enthalpy (MW)

Tem

pera

ture

(oC

)

Figure 38: Site source and sink composite curves after integration of local

heating

6 Conclusions & future work

The selection of steam level conditions is important as this significantly affects

heat and power management for the industrial site. A new cogeneration targeting

model has been developed in this work, as existing models have been shown to

give misleading results, compared to detailed design procedure. This new model

is based on isentropic expansion and the results obtained from the new model

have been shown to agree well with the results from the detailed isentropic

design method simulated in STAR®. The new method has been incorporated in

the optimisation study which systematically determines of the levels of steam

mains at minimum utility requirement.

Multiple options such as heat pumping, CHP, integrated gas turbines, absorption

refrigeration, drying, etc, are available for upgrading low grade heat. Heat pump

can reduce the LP steam requirement and subsequently the fuel consumed in

the boiler. However, electrical consumption in the site increases with the

integration of heat pump. The overall operating cost increases with the heat


73

pump for the current case study for the current ratio of fuel to electricity price.

ORC decreases the annual operating cost for the total site by reducing the

electricity demand from the site. Absorption refrigeration only reduces the

demand of cold utility. However the major savings comes from reduction in the

electricity demand for an existing vapour compression refrigeration system on the

site. Heating of boiler feed water decreases the fuel consumption in boiler and

hence the overall operating cost of the site. In conclusion BFW heating the

optimum option for integration with the total site in this case study. However, the

best heat upgrade technology is dependent on the site fuel and electricity cost,

condensate management system, and characteristics of low grade heat (quality

and size).

The distance of a DH centre from the process site is evaluated to determine

whether economic benefits for transferring of waste heat from process industry to

the DH centre is realistic and practical. This maximum distance of the economic

transfer of heat is calculated based on the assumption of the constant rate of

heat supply and no variability on the supply side of the DH network.

The integration of waste heat with an existing DH network decreases the heat

production from the existing supplying units. The economic feasibility for the

integration of waste heat with DH systems is case-specific, as the performance of

such an integrated system is heavily dependent on the part load performance of

the energy equipment and the cost of heat and electricity.

In this work, a case study for the integration of waste process heat to the DH

network has been evaluated with respect to economic impacts on the DH

network. The case study consists of two boilers and two CHP units using

different fuels for the generation of heat. It was shown here that the utilization of

waste heat is not economically beneficial to the DH network. However, it should

be noted that it strongly depends on the design and operating conditions of

energy infrastructure and economic parameters, for example, the prices of heat

and electricity in the market.

The developed methodology will be applied to further extended to other case

studies. Integration of renewable energy sources such as solar, wind, geothermal


74

etc to the total site will be considered in future work. The variation in renewable

energy sources will be incorporate to the framework.

Acknowledgement

Financial support from Research Councils UK Energy Programme

(EP/G060045/1; Thermal Management of Industrial Processes) is gratefully

acknowledged.

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Chemical Engineers: McGraw-Hill 2003.

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a robust and reliable waste-heat district heating system. Applied Thermal

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[32] Axelsson E, Olsson MR, Berntsson T. Heat integration opportunities in

average Scandinavian kraft pulp mills: Pinch analyses of model mills. Nordic Pulp

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[33] Axelsson E, Olsson MR, Berntsson T. Increased capacity in kraft pulp

mills: Lignin separation and reduced steam demand compared with recovery

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design methodology for reduction of fuel, power and CO2 on total sites. Applied

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reduce the carbon footprint of locally integrated energy sectors. Energy.

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with a New Cogeneration Targeting Chemical Engineering Research and Design.

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8 Appendix A

8.1 Optimization framework

8.1.1 Objective function

The present work used overall operating cost along with annual capital cost for

the new design heat upgrade technology as the minimization function.


78

FixOpCstFEmmCstWatCstPowCstFuelCstOpCst op

cst 20

Where,

op

cstF Factor to increase operating cost by a percentage (fraction)

OpCst Overall annual operating plant cost (MM$/yr)

FuelCst Overall fuel cost for the site utility system (MM$/yr)

PowCst Overall electricity cost for the site utility system (MM$/yr)

WatCst Overall water cost for the site utility system (MM$/yr)

EmmCst Overall emission cost for the site utility system (MM$/yr)

FixOpCst Fixed charge for operating cost (MM$/yr)

Capital cost for any additional unit is defined as a function of the purchase cost

( nPurCst ) for each piece of equipment.

fix

n

ninstcepci CapPurCstFFCapCst

21

Where,

cepciF Chemical engineering plant cost index

instF Installation factor to consider other plant expenses

fixCap Fixed capital cost for the whole plant (MM$)

nPurCst Purchase cost for n equipment unit (MM$)

Total cost for the whole site is given by the following expression

annFCapCstOpCstTotCst 22

Where,

TotCst Total annualized cost (MM$/yr)

Fann Annualisation factor


79

Optimization constraints

8.1.2 Electric balances

The cost of electricity consumed or produced on a site on the overall annual

operating cost is calculated by the electrical balance between site sources and

sinks.

impgenlossauxdem WeWeWeWeWeWe exp 23

demWe Total electricity demand of the process in each period (kWe)

expWe Electricity exported by the utility system in each period (kWe)

auxWe Electricity consumed by auxillary units including boiler fans, pumps,

cooling fans, motor drivers (kWe)

lossWe Distribution and control electricity loss (kWe)

genWe Electricity generation from the site utility system (kWe)

impWe Electricity imported by the site (kWe)

8.1.3 Mass balances

The mass balance at each node is

outin MM 24

Mass flow of steam into the deaerator where water is scrubbed with LP steam

before it is delivered to the boiler as saturated water.

vnt

dea

bfwmkupcondretstm

dea MMMMMM 25

Mass balance for the steam header is based on the mass flow from producers

(boilers, HRSG), receive or deliver steam to and from steam turbines, let down

valves or process etc.

k k k

vnt

k

outlet

k

k

outST

k

cons

k

k

dsh

k

k

inlet

k

k

inST

k

k

gen

k

k

HR

k

k

boi

k

MMMM

MMMMMM

26


80

Make up water is equal to the condensate lost by the process, along with the

losses in the utility plant including losses in boiler, HRSG, vent, gas turbine, and

process etc.

vnt

loss

vnt

dea

ret

k

gen

k

cons

k

k

vnt

k

inj

GT

HR

bldwn

boi

bldwn

mkup MMMMMMMMMM 27

Where,

k Steam header index in the utility plant

inM Mass flow into a mixing node (kg/s)

outM Mass of steam out from a mixing node (kg/s)

stm

deaM Mass flow rate of steam into deaerator (kg/s)

retM Returning condensate from the process (kg/s)

condM Mass flow rate of the condensate (kg/s)

mkupM Water make up for the utility system (kg/s)

bfwM Mass flow of water to the boiler (kg/s)

vnt

deaM Vented steam from the deaerator (kg/s)

boi

kM Steam delivered by boiler to header k (kg/s)

HR

kM Steam delivered by HRSG to header k (kg/s)

gen

kM Steam generated by process and delivered to the header (kg/s)

inST

kM Discharge from steam turbine into header k (kg/s)

inlet

kM Letdown steam entering header k (kg/s)

dsh

kM De-superheating boiler feed water injected into header k (kg/s)

cons

kM Steam consumed by process at header k (kg/s)

outST

kM Steam release by steam turbine to header k (kg/s)

outlet

kM Steam leaving header k by letdown (kg/s)

vnt

kM Vented steam for header k (kg/s)

mkupM Water make up for utility system (kg/s)

boi

bldwnM Blowdown for all boiler in utility system (kg/s)

HR

bldwnM Blowdown from all HRSG in utility system (kg/s)


81

inj

GTM Steam injected to all gas turbine in utility system (kg/s)

vnt

kM Steam vented from header k (kg/s)

8.1.4 Heat balance

Heat balance for two streams in adiabatic mixing is shown in Equation 28-29.

outin QQ 28

outoutinin MhMh 29

Enthapy balance for the deaerator is given by Equation 30. The enthalpy balance

for a steam header consists of heat from the generator (boiler and HRSG), both

production and consumption from steam turbine, let down, and process

(Equation 31-32).

vnt

dea

g

dea

bfw

dea

f

dea

mkupmkupcondcondretretstm

dea

stm

dea MhMhMhMhMhMh 30

k k k k k k

vnt

k

dsh

k

outST

k

gen

k

bHR

k

boi

k

hdr

k

k

vnt

k

hdr

k

k

dsh

k

bfw

k

k

inlet

k

inlet

k

k

inST

k

inST

k

k

gen

k

gen

k

k

bhr

k

hdr

k

k

boi

k

hdr

k

MMMMMMh

MhMhMh

MhMhMhMh

31

k k

dsh

k

inlet

k

inST

k

k

gen

k

hdr

k

dsh

k

bfw

k

k

inlet

k

inlet

k

k

inST

k

k

gen

k

gen

k

MMMMh

MhMhQMh

32

Where,

k Steam header index

inQ Heat entering a mixing node (kW)

outQ Heat leaving a mixing node (kW)

inh Specific enthalpy of heat entering a mixing node (kJ/kg)


82

outh Specific enthalpy of heat leaving a mixing node (kJ/kg)

f

deah Enthalpy of saturated steam at deaerator pressure (kJ/kg)

g

deah Enthalpy of saturated vapour at deaerator pressure (kJ/kg)

bfw

kh Enthalpy of feed water needed to de-superheat steam (kJ/kg)

stm

deah Enthalpy of stripping steam to the deaerator (kJ/kg)

reth Enthalpy of returning condensate from the process (kJ/kg)

condh Enthalpy of condensing water entering the deaerator (kJ/kg)

mkuph Enthalpy of make up water (kJ/kg)

gen

kh Enthalpy of steam generated by the process (kJ/kg)

hdr

kh Enthalpy in steam header k (kJ/kg)

inlet

kh Enthalpy of let down steam header k (kJ/kg)

inST

kh Enthalpy of discharge from steam turbines at header k (kJ/kg)

8.2 Equipments

8.2.1 Multi-fuel boilers

In a boiler the chemical energy of the fuel is extracted to heat the condensate

or feed water to generate steam at the required temperature. There are

numerous types of boilers and control schemes along with different unit size

and actual load. This results in different performance trends. Aguillar [25]

assumed a linear relationship between fuel consumption and steam

production as shown in Equation 33.

boi

boi

boi

fboi

stm DB

QQ

33

Where,

boi

fQ Net heat from the fuel consumed inside the boiler (kW)

boi

stmQ Actual heat added to the water/steam inside the boiler (kg/s)

boiB , boiD Regression parameters


83

With the assumptions that boiler blowdown is extracted at saturated

conditions and as a fixed fraction of boiler steam output, the heat supplied to

the water/steam cycle can be expressed as:

boi

T

boi

bid

boi

ecoboi

T

boi

stm

boi

stmh

FhhMQ

.1..

34

Where,

boi

stmM Actual steam output from the boiler (kg/s)

boi

Th Enthalpy difference between feedwater and outlet steam conditions

(kJ/kg)

boi

ecoh Enthalpy difference across boiler economiser (kJ/kg)

boi

bldF Boiler blowdown fraction taking as reference the outlet steam

flowrate (kg blowdown/kgsteam)

Equation 35 is obtained by rearranging Equations 33 and Equation 34. The

coefficients for this boiler model are obtained by regression from operating or

design data.

boi

T

boi

bid

boi

ecoboi

T

boi

stm

boi

boi

boi

f

h

FhhMD

B

Q .1..

35

Here, boiB & boiD are regression coefficients.

8.2.2 Gas turbines (GT)

Gas turbines convert the chemical energy of fuels into electrical energy via a

three step process

Compression: The inlet pressure and temperature of the ambient air is

increased by the compressor.

Combustion: Heat is added at high pressure by fuel ignition.

Expansion: The hot combustion gases are expanded through the

turbine to drive the compressor and to provide power (electricity).


84

The relationship between power output from the gas turbine to the required

heat input is approximated by a straight line which is known as the Willans

line.

gtgtgtgt WQCW int 36

gtW Gas turbine power output (kW)

gtQ Gas turbine fuel input (kW)

gtC , gtWint Regression parameters

8.2.3 Heat recovery steam generators (HRSG)

HRSG utilize the waste heat from the gas turbine to produce steam which can

be further used to generate power or provide heating to consumers. HRSG

can be further classified into the following types:

a) Unfired units: Steam production is limited by the temperature and

available energy in the exhaust gases.

b) Supplementary fired units: The remaining oxygen in the exhaust gases is

used to burn fuel to boost steam generation.

c) Fully fired units: Additional quantity of air is supplied for further

consumption of fuel and hence increases production of steam.

Aguillar [25] derived a simple equation for the steam production from a gas

turbine based on the mass and heat balance for the unfired HRSG.

gtgt

D

gtgt

D

gthr

m

hr

satgt

D

gtgt

Dgtgt

Dhr

eva

hr

sh

hr

exh

hr

radhr QQQTTQ

QQkTexh

hh

CpFM

1

37

Where,

hrM Maximum HRSG steam production from GT exhausts (kg/s)

hr

radF Radiation losses factor for the HRSG


85

hr

exhCp Average specific heat for the exhaust gases (kJ/kg-°C)

hr

shh , hr

evah Steam enthalpy difference across HRSG superheater, evaporator

(kJ/kg)

hr

mT Minimum temperature difference between gas and steam/water

profiles (°C)

gt

DTexh Design temperature at the exhaust of the gas turbine (oC)

hr

satT Saturation temperature for the steam produced in the HRSG (°C)

gtk , gt , gt Regression coefficients

gt

DQ Design heat from the gas turbine

gtQ Actual heat from the gas turbine

8.2.4 Electric motors (EM)

Electric motors are devices that convert electricity into shaft power by

inducing electromagnetic forces in its rotational wounding (i.e. rotor). The

units are broadly classified into synchronous, direct current, three phase

induction and single phase [25].

Willans line describes the part load performance of the electric motors in

terms of regression parameters with the full load performance of the motor.

emem

D

emem

D BWAWe 38

em

DWe Design electric consumption of the motor (kWe)

em

DW Design motor power output (kW)

emA , emB Regression parameters

8.2.5 Steam turbines (ST)

Steam turbines convert energy from steam into electrical energy by expanding to

lower pressure. They can be classified as single or multiple extraction turbines


86

according to number of equipments attached to the shaft. The back pressure

steam turbine expands steam to a lower pressure, while steam is expanded to

liquid water in a condensing turbine.

Single stage steam turbine

Aguillar [25] developed linear models to describe the performance of steam

turbines. The design steam flow rate ( st

DM ) in steam turbine is a function of

isentropic enthalpy change ( st

ish ) and the design capacity of the unit ( st

DW ).

st

D

stst

st

is

st

D WBAh

M

1

st

sat

st TaaA 10 st

sat

st TaaB 32

39

Here a0, a1, a2, a3 are regression coefficients,

st

satT is the saturation temperature difference across the turbine.

The power of the unit ( stW ) is proportional to the steam mass flow rate ( stM ) and

the ordinate intercept of the Willans line ( stWint ).

stststst WMnW int 40

The actual shaft power from a single stage turbine is a function of maximum

output size ( st

DW ), actual steam flow ( stM ) and inlet and outlet conditions of the

steam in the turbine.

st

ststst

D

stst

st

stst

is

st

B

ALWLM

B

LhW 1

1

st

sat

st TaaA 10 st

sat

st TaaB 32 st

satLL

st TbaL

41

Here a0, a1, a2, a3, aL, bL are regression coefficients,


87

st

satT is the saturation temperature difference across the turbine.

Multi-stage steam turbine

Muti-stage turbine discharges steam at different outlet steam levels. A multi-

stage steam turbine can be decomposed into several single stage turbines

connected in series. Aguillar [25] developed linear models to evaluate the

performance of multi-stage turbines. Isentropic enthalpy difference across the

downstream stages is evaluated by assuming a typical isentropic efficiency

across all the stages. The enthalpy for a three stage turbine is calculated by the

following equations:

mst

sP

mst

in

mst

is hhh 0,1010

mst

is

mst

sP

mst

in

mst

is hhhh '

321,2121

mst

is

mst

sP

mst

in

mst

is hhhh '

322,3232

mst

sP

mst

in

mst

is hhh '1,2'1

'

21

mst

sP

mst

in

mst

is hhh '2,3'2

'

32

mst

is

mst

is

mst

in

mst

in hhh 10

'

0'1 mst

is

mst

is

mst

in

mst

in hhh '

21

'

'1'2

42

Where,

0,1,2,3 Sub indexes for steam conditions at inlet, first, second and third

extractions of the turbine.

1’,2’,3’ Sub indexes indicating approximate steam conditions at first,

second and third outlet of the turbine.

mst

iiish )1( Isentropic enthalpy difference for the i stage of steam turbine (ie.

between i and (i+1) respectively) (kJ/kg)

mst

inih Enthalpy of steam entering stage i (kJ/kg)

mst

inih ' Approximate enthalpy of steam entering each stage i

mst

is

' Isentropic efficiency value to approximate steam conditions in

downstream stage


88

The shaft power equation for single stage steam turbine is extended to each of

the stages of the multi-stage turbine. The overall shaft power for a multi stage

steam turbine ( mst

totW ) is calculated as:

mst

mstmstmst

D

mstmst

mst

mstst

is

mst

B

ALWLM

B

LhW

1

11111

1

1101 1

1

mst

mstmstmst

D

mstmst

mst

mstst

is

mst

B

ALWLM

B

LhW

2

22222

2

2212 1

1

mst

mstmstmst

D

mstmst

mst

mstst

is

mst

B

ALWLM

B

LhW

3

33333

3

3323 1

1

mstmstmstmst

tot WWWW 321

43

Where,

mst

iW Shaft work from i stage of the multi-stage steam turbine

mst

iA , mst

iB Regression coefficients for i stage of multi-stage steam turbine


89

Part II

Environmental and Socio-Economic Issues


90

9 Barriers to Process Efficiency Improvements and Low Grade Heat Utilisation

Barriers to process efficiency improvements and low grade heat recovery were

investigated by a literature review, a workshop with over 50 attendees from

industry and academia, canvassing viewpoints of key stakeholders and analytical

mapping techniques. The detailed results from these have been shared with

stakeholders in 2 detailed, freely accessible reports, a conference paper and

published journal paper, as well as discussions with groups such as the

Committee on Climate Change. A summary of key points is given below:

The recovery of low grade heat (LGH) has potential to increase the energy

efficiency of process industries, but there are many barriers to its utilisation. The

main technical barrier is generally the lower temperatures involved. This often

results in recovery technologies operating at lower efficiencies than for other

energy provision systems. Other factors include the potential condensation of

corrosive or fouling elements. These and other issues may result in process

disruption and maintainability.

Non-technical barriers to the uptake of LGH are diverse and involve numerous

actors. Consultation with industrial and academic stakeholders in the UK

established that cost, return on investment and technology performance were

key barriers to process industry energy efficiency improvements. However, for

low grade heat utilization, stakeholder engagement and strategic mapping found

that location, cost and the availability of infrastructure were the most significant

barriers. This is augmented by a number of institutional issues relating to

company strategy and priority, specifically, instances where energy efficiency is

not perceived as a practical concern, or directly related to productivity.


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Discussion with stakeholders reinforced the fact that there is, at present, little

commercial appetite in this area. A key technology differentiator was perceived to

be the ability to extract/remove an energy efficiency measure during and after

installation. Process interruption risk is a major disincentive to implementing

energy efficiency measures in the process industries.

Interestingly capital cost seems to be a more significant barrier than project rate

of return, again suggesting support with infrastructure development is central to

developing the sector. In terms of policy there is a need to incentivise the use of

low grade in the earliest planning stages. Unlike the use of heat at higher

temperature the efficient use of LGH may be contingent on the availability of

“over the fence” options and an external demand for recovered heat. In that

regard regional heat load mapping may be an effective method of intervening

between suppliers and potential users. A key message appears to be that, at

present, EEMs are not sufficiently important (or visible) to management and that

purely fiscal measures will not create momentum in this area, since many

measures have been economical for some time, but have not been implemented.

Therefore additional levers or incentives must be identified in order to incentivise

dedicated finance options such as risk sharing which may complement or

accentuate any associated financial benefits. Such measures must also address

infrastructural issues which may impede “over the fence” options to utilize

recovered heat.


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10 Environmental and Economic Analysis

The varied forms in which LGH can be recovered from process industries and the

distinct technological options for its recovery, preclude a generic account of the

potential environmental benefits of its utilisation. Therefore in order to evaluate

the environmental benefits (and costs) of low grade heat recovery a life cycle

assessment approach was applied to a number of case studies which were

agreed with academic and industrial partners as representative of potential future

development options. A key objective of this work was to quantify greenhouse

gas savings for different low grade heat recovery options. However, the analysis

also extended beyond this to cover other environmental impact areas (such as

eco toxicity) to provide a more nuanced (and realistic) appraisal of the benefits

(and trade-offs) of LGH recovery. Since different aspects of economic viability

had been highlighted in the barriers work, assessments were also carried out of

the economic performance of different process options, using simple discounted

cash flow techniques.

The three case studies were chosen to represent different industries and means

of LGH recovery, as well as different degrees of “over the fence” interactions, but

each case study is “internally consistent” in that it uses technology appropriate

for that application. The case studies are:-

Waste heat from a coke oven flue gas stream is recovered as electricity

through the operation of an organic Rankine cycle (ORC).

Latent (and sensible) heat from woodchip boiler flue gas is recovered

through a condensing boiler to preheat district heating return water.

Hot wastewater from a paper mill is used in conjunction with a heat pump

to provide thermal energy for a multi effect desalination (MED) process.


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The first case study produces a co-product which may be used directly on or

offsite. The second case study increases the energy efficiency of a closed loop

system whereby the energy contained in return water is augmented by LGH

recovered onsite. In contrast the final case study demonstrates complete

disassociation between the LGH source and the eventual product, in this case

potable water for human consumption. Each augmented process has been

assessed by project partners from a thermodynamic perspective. This data

assists in estimating the direct emissions and lifecycle impacts associated with

each process as well as distinguishing the lifecycle benefits from LGH recovery

through comparison of two scenarios, one in which LGH is recovered and one in

which it is not. In each case the lifecycle impacts are allocated to a rational

reference unit; 1 kg of coke, 1 MWh heat and 1 m3 of potable water. For each

case study the resources, energy and emissions associated with each discrete

stage are quantified. These are aggregated and assessed using proprietary

lifecycle assessment (LCA) software [1] to expresses the overall environmental

impact in terms of consolidated categories.

Each of these case studies has been reported in detail in conference and journal

papers. However, a summary of project results is given below:-

10.1 Organic Rankine cycle integrated into a coke oven.

Project partners [2] have identified the flue gas emitted from a coke oven within

an integrated steelworks as a viable source of LGH. This was estimated to yield

21 MW of recoverable energy. Given its continual production cycle, an organic

Rankine cycle (ORC) was chosen as a suitable recovery mechanism due to its

unobtrusive interaction with the process. The ORC effectively mimics a traditional

steam cycle but this configuration is unsuitable for temperatures under 370 °C [3]

necessitating a organic working fluid. Additional consultation with project partners

suggested an ORC efficiency of 11% resulting in an approximate electricity

generation estimate of 2.31 MW [4]. Based on the UK electricity generation mix

this is estimated to negate the emission of 10,927 t CO2 annually. Direct


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emissions due to coke production are calculated based on the estimated coke

yield and the gaseous fuels used in the coke production process. It is estimated

that ORC operation reduces the carbon intensity of the coking process by 1.39%,

although it allows for a surplus of electricity at the oven itself.

The lifecycle emissions associated with coke production are based on 3 main

lifecycle stages, the production of coal, its transportation and the coking process

itself. The data obtained on coal production was based on environmental reports

from a number of Australian coal mines (representing the main source of coking

coal arriving in the UK) [5]. The lifecycle impacts associated with each stage of

the coking process have been estimated, assuming different types of coal mines:

underground mines with varying levels of methane emissions and surface mines

with varying degrees of electricity demand. The results for a number of very

distinct environmental impact categories were calculated and reported, including

global warming potential, acidification potential, human toxicity etc. However,

normalised, weighted results are commonly used to express the overall/net

environmental impact of processes. This approach has its shortcomings,

particularly since normalisation factors inevitably introduce some degree of

subjectivity. However a well established and recognized normalisation approach

was used in this work, which allowed us to compare the net environmental

impact of different elements of two coke production systems: one with an ORC to

recover low grade heat and one without. The results in table 1 are expressed in

units of millipoint (mPts), which is an aggregate measure that represents the

environmental impact of an average European during a single year.

Table 23: Normalised results for 1 kg of Coke expressed in millipoints (mPTs) [6].

Underground Surface

Process/Activity Gassy Non Gassy High Elect. Low Elec.

Emissions at Coke plant 67.9 67.9 67.9 67.9

Hard Coal Coke production plant 1.11 1.11 1.11 1.11

Hard coal Mix 171 169 158 117

UK ORC Electricity 0.5 0.5 0.5 0.5


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Water and Chemical Inputs 0.01 0.01 0.01 0.01

Freight Rail 0.01 0.01 0.01 0.01

Blast furnace gas 1.4 1.4 1.4 1.4

Ocean Freight 29.6 29.6 29.6 29.6

ORC components 0.004 0.004 0.004 0.004

Recovered electricity -0.64 -0.64 -0.64 -0.64

Total (with recovery) 271 269 258 217

As can be seen from Table 1 above the ORC operation has a negligible effect on

the lifecycle impacts of coke. Indeed the choice of coal has a more pronounced

influence. This is a reflection of the high level of carbon intensity of the coking

process, which cannot be significantly offset by low grade heat recovery through

ORC operation.

However a techno-economic evaluation suggests that this might still be a

financially viable method of achieving some greenhouse gas reductions in a

“hard-to-decarbonize” industry sector. A discounted payback (DPP) period of

between 2.8 and 6.3 years was calculated under different economic

assumptions, which was within the bounds considered attractive by stakeholders

at the project workshop described above.

10.2 Condensing boiler applied to woodchip combustion.

One of the most common means of improving thermal efficiency is the

introduction of a condensing boiler to recover latent heat from the waste gas

steam. This benefit is more pronounced for raw biomass systems, where the

higher moisture content means that up to half of the calorific value of the fuel is

recoverable. Both for this reason and its lack of process disruption, Chen et al.

[8] have examined the impact of a condensing boiler on a Finnish woodchip

fluidized bed boiler which provides heat for a district heating system. The

woodchip plant demonstrates a basic (pre-condenser) output of 40 MW. The

heating systems served by this plant generally consist of water radiators whereby


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the return water temperature is between 35 and 40°C. The return water

temperature is preheated in the condenser using both the recovered latent heat

of water vapour and the sensible heat of the flue gas. It is estimated that the

condenser increases the thermal output to 52 MW. An examination of the

enthalpies of the various process streams suggests that the system has a

thermal input of 44 MW. Comparing thermal efficiencies suggests a fuel saving of

22%. Based on the fuel consumption rate and the carbon content of the

woodchips, this equates to offsetting 36,059 t direct CO2 annually. It must be

clarified that the operation of a condensing boiler will not result in a decrease in

the actual amount of carbon emitted from the facility rather it allows for an

increased district heating capacity without necessitating additional woodchip

inputs. The Lifecycle assessment (See Table 2) highlights the impact of the

condensing boiler and incorporates data from a number of distinct lifecycle

stages such as forest nursery, tree cultivation, felling, as well as boiler operation.

The operation of a condensing boiler (and associated woodchip savings) reduces

the lifecycle impact estimate for most categories [9]. However, it is noted that

one of the key impact categories on which this work was focused is reduction of

greenhouse gas emissions and the global warming potential is reduced by only

6.8%, compared to larger reductions of e.g. 22.49% for photochemical oxidation.

This is largely because the heat recovery application here is effectively reducing

the amount of fuel required and, since that fuel is wood, it has significant

land/cultivation related impacts, which are being reduced correspondingly.

Table 24: Lifecycle impact of producing 1 MWh of district heat using CML midpoint

assessment.

Impact category Unit No Recovery Recovery % Impact

Abiotic depletion kg Sb eqv. 0.15 0.15 1.48 %

Acidification kg SO2 eqv. 1.33 1.03 -22.05 %

Eutrophication kg PO4 eqv. 0.37 0.29 -22.08 %

GWP100 kg CO2 eqv. 38.79 36.16 -6.80 %

Ozone depletion kg CFC-11 eqv. 0.0002 0.0002 -22.95 %

Human toxicity kg 1,4-DB eqv. 82.33 65.75 -20.15 %


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Fresh water ecotoxicity kg 1,4-DB eqv. 9.98 10.26 2.82 %

Marine ecotoxicity kg 1,4-DB eqv. 15967.14 15365.26 -3.77 %

Terr. Ecotoxicity kg 1,4-DB eqv. 0.57 0.49 -14.61 %

Photochem. Oxidation kg C2H4 0.10 0.08 -22.49 %

The overall lifecycle impact reduction may seem disappointing considering the

associated fuel reduction, however the requirement to maintain flue stream

buoyancy after condensation means that the net electrical demand at plant (per

MWh) is increased when this supplementary requirement is included. The costs

associated with the condensing boiler are based on the choice of material as well

as the ancillary costs associated with fan operation. Chen et al. [8] estimate the

discounted payback period to range from 1.7 years for system produced using

carbon steel at a discount rate of 5% to 6.93 years for a system constructed

using stainless steel with a discount rate of 15%. The savings are based on

reducing the required quantity of woodchips. However, the scale of annual

woodchip consumption makes the DPP sensitive to woodchip price. If it is

assumed that the woodchips are produced onsite (increasing costs by 15%), this

reduces the DPP by 23% on average.

10.3 Heat pump for desalination

Project partners [10] have examined the thermodynamic properties of using a

heat pump (HP) to recover LGH from a wastewater stream thereby negating the

use of a fossil powered boiler within a multiple effect distillation (MED)

desalination system. MEDs have have been used for facilitating the production of

freshwater through seawater evaporation. Within a MED, the steam generated

from the previous (or first) stage becomes the source of heat for the subsequent

stage. The heat pump is used to essentially upgrade the LGH to a point where it

can be used to evaporate seawater. In estimating the fossil energy requirements,

the energy of seawater evaporation is taken to be 2333.8 kJ/kg. For the MED in

question, a gain output ratio (GOR) of 10 is assumed, (representing the ratio


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between the amount of water produced per unit mass of dry saturated steam

supplied [11]) suggesting that the fossil energy requirement of a desalination

plant (without a HP) becomes 233 kJ/kg [11]. Based on an assumed boiler

efficiency of 80% this is estimated to offset 14.56 kg of coal or 7.52 m3 of natural

gas (NG) per treated m3, effectively avoiding 9,924 or 3,150 tonnes of direct CO2

respectively. However the impact of integrating a heat pump will necessitate

additional material (due to the increased heat exchanger areas) but will increase

the relative electricity demand 4 fold, mostly due to the increased pumping

requirement. The lifecycle assessment results for desalination using a heat

pump (MED HP), natural gas fuel (MED NG) and coal fuel (MED Coal) are

summarised in Table 3 below.

Table 3: Lifecycle impact of producing 1 m3 of potable water using CML midpoint

assessment.

Impact category Unit MED HP MED NG MED Coal

Abiotic depletion kg Sb eqv. 0.023 0.025 0.520

Acidification kg SO2 eqv. 0.049 0.167 0.276

Eutrophication kg PO4 eqv. 0.008 0.005 0.028

GWP100 kg CO2 eqv. 6.428 18.538 60.703

Ozone depletion g CFC-11 eqv. 0.0002 0.00213 0.0001

Human toxicity kg 1,4-DB eqv. 0.001 0.002 0.023

Fresh water ecotoxicity kg 1,4-DB eqv. 0.022 0.008 0.037

Marine ecotoxicity kg 1,4-DB eqv. 3715.766 1957.008 9230.746

Terr. Ecotoxicity kg 1,4-DB eqv. 2.035 0.647 4.266

Photochem. Oxidation kg C2H4 2.143 1.371 7.033

As can be seen from Table 3 above, the use of coal within the MED results in the

highest environmental impact in all categories with the exception of o-zone

depletion. In this instance, the production of natural gas (with its associated

fugitive emissions of o-zone depleting compounds) results in a significantly

higher impact value. However as a considerable portion of the UK‘s electricity is

generated through nuclear power and coal combustion the water produced in

conjunction with heat pump operation is seen to have higher toxicological


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impacts than water produced by the gas fired MED. It may be argued by some

that treating recovered LGH as being effectively “free” from embodied impacts is

a misrepresentation. By way of example, the lifecycle impacts of the potable

water are reassessed whereby the LGH containing stream is allocated emissions

based on an exergy allocation scheme. In this example 0.3% of the impacts

associated with producing 1 kg of kraft paper are allocated to 13 litres of LGH

containing wastewater [12]. This alters the relative impact of LGH recovery and

demonstrates a higher impact than an MED powered through natural gas in the

toxicological and eutrophic impact categories, questioning the implication that

LGH recovery is inherently beneficial in all instances. Project partners have

estimated the payback period to range from 4 to 7 years depending on MED

configuration [10].

10.4 District Heating

Analysis was also carried out as part of this project of a heat pump used to

recover low grade heat for a district heating scheme. The results of this have not

been reproduced here as they essentially confirmed the observations noted

above for heat pumps applied to desalination, namely that if the heat is treated

as waste heat, which has no associated environmental impact then there are

significant positive impacts in most environmental categories, but the additional

consumption of electricity increases toxicity impacts. It should be noted that this

result is obviously related to the assumptions made about the national mix for

power generation plant and that a future high renewables generating scenario

would have lower toxicity impacts, though high levels of nuclear power could

increase the toxicity impact.


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11 Social aspects: perceptions of heat users

The main objective in this work was to add to the very limited empirical evidence

on UK citizen and consumer opinion on the use of waste process heat for district

heating. Projects aiming to make use of waste process heat for domestic and

commercial space and water heating will likely be more difficult, if not impossible,

without consumer and citizen support. This part of the project first conducted two

focus groups on district heating with older, potential users in Newcastle. While

this involved participants’ consideration of hypothetical installation, all engaged

closely with the issues. The second part of the project elicited resident opinion on

the prospective use of waste process heat for district heating in the local

authority area of Neath Port Talbot in Wales, where waste process heat from

Corus, a local integrated steelworks, could potentially be used to provide local

buildings with space and water heating. This was a relatively ‘live’ context, in

which opinions may be more actualised than in hypothetical consideration. While

at the time of the study there were no definite plans for district heating, this was

being given serious consideration by local agencies.

Both the qualitative and quantitative results provide an insight into the end-user

(consumer) criteria that retro-fitted district heating will need to meet. Focus

groups with members of the public for whom heat is particularly salient, and a

public questionnaire survey in a locality where a district heating scheme using

waste process heat is plausible, both indicated that while ‘citizen-consumers’ are

favourable to the idea, this support is conditional on a range of conventional

purchasing criteria being met, including acceptable cost, reliability and flexible

contractual arrangements. The ‘practice’ literature from science and technology

studies also implies that district heating will need to ‘fit in’ with the existing

routines and habits of users, if lock-in of a new system is to be achieved.

While there is a need to be cautious when generalising to other cases and

localities, our findings suggest that gaining UK consumer support for a change


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from existing, individualised heat systems to a communal district system based

on process waste heat may stand most chance of acceptance where the heat

supply can be guaranteed at lower than market cost. In this regard, business

vulnerability to market conditions clearly will have a bearing on maintaining a

reliable heat supply for a community. This vulnerability would suggest that

processes or operations that are less at risk from changing market conditions

may be best suited to supplying waste heat. Energy from waste operations are

an obvious candidate and such schemes already exist in the UK (e.g. in Sheffield

and Newcastle). Similarly, on the demand side, supplying concentrated heat

loads (‘heat anchors’) such as blocks of housing or offices, schools, hospitals etc

is likely to be more practicable than recruiting individual households on a street-

by-street basis, where the possibility of non-acceptance may be problematic,

particularly if a commitment for longer than 24 months is required. Overall, even

where supplying waste process heat to district heating schemes makes energetic

sense (i.e. where the heat cannot be recycled internally), it will rarely be

uncomplicated.

Providing a little more detail, while those questioned were broadly supportive of

the idea of district heating, particularly if this would involve reductions in domestic

heating costs, both the qualitative and quantitative work revealed significant

concern about contractual lock-in, hence the title of the paper based on the work:

Don’t lock me in: public opinion on the prospective use of waste process heat for

district heating. In contrast, the stability of long-term demand is highly valued by

those responsible for the supply-side, which obviously sets up a tension that

would need to be resolved.

We also observed some gender differences in the first reactions to district

heating. Specifically, women were more neutral in terms of their stated

propensity to buy a property on the basis of what they have been told about

district heating and were also less certain about district heating. We concluded

that while the results imply that an appeal to the environmental performance of


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district heating with waste heat may be facilitate acceptance, trust-building and

price inducements will also be required to overcome end-user concerns.


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12 Conclusions

This work has shown that some applications of low grade heat recovery can

significantly reduce the greenhouse gas emissions of energy systems. However,

in other very carbon and fuel intensive processes the impact of low grade heat

recovery makes only a very marginal difference to overall greenhouse gas

emissions. It should also be noted that some of the systems studied incur

substantial direct energy consumption (most often electricity) as part of the heat

recovery scheme. Depending on the electricity grid generating mix this may

increase the overall impact in some environmental impact categories. By contrast

other options, such as the organic Rankine cycle, may actually offset process

electricity demand and, where this is the case

Nevertheless there are greenhouse gas savings that can be made with

implementation of low grade heat recovery options. In many cases these are

economically viable based on stakeholder’s stated economic acceptability

criteria. However, there is still little commercial appetite for implementation of

these measures.

There are many reasons why this is the case, but key issues revolve around risk,

capital outlay and location/communication barriers. It seems unlikely therefore

that economic incentives focused on carbon or energy savings would be

sufficient to offset these and result in significantly increased uptake of low grade

heat recovery options. Much more radical interventions would be necessary to

support the necessary infrastructure development to make best use of low grade

heat and it should be noted that it is much easier to plan for future low grade heat

use than to modify established process

Overall the present energy policy context provides no incentive for recovery of

low grade heat, even where this does reduce greenhouse gas emission, since

the carbon reductions achieved would not be formally rewarded e.g. as the


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Renewables Obligation does for renewable electricity or the Renewable Heat

Incentive for renewable heat. However, the disparity between the different case

studies evaluated indicates that if low grade heat recovery were to be

encouraged to promote energy efficiency, great care would be needed to ensure

that an appropriate framework actually rewarded greenhouse gas reductions and

did not inadvertently increase other environmental impacts or electricity

consumption.


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13 Appendix B: List of Published Outputs Peer reviewed journal papers (see full texts below) Walsh, C. and Thornley, P., “Barriers to improving energy efficiency within the process industries with a focus on low grade heat utilisation”, Journal of Cleaner Production, 23 (1) pp. 138-146, 2012 Walsh, C and Thornley, P., “The environmental impact and economic feasibility of introducing an Organic Rankine Cycle to recover low grade heat during the production of metallurgical coke”, Journal of Cleaner Production, 2012 Walsh, C. and Thornley, P., “A comparison of two low grade heat recovery options”, Applied Thermal Engineering, 2012 Upham, P. and Jones, C. “Don’t lock me in: public opinion on the prospective use of waste process heat for district heating”, Applied Energy, available online 17 March 2011. Peer reviewed conference proceedings Walsh, C. and Thornley, P., “Barriers to improving energy efficiency within the process industries with a focus on low grade heat”, Proceedings of SusTEM 2010, Newcastle upon Tyne Upham, P., Chisholm, F. and Jones, C. (2010) “Don’t lock me in: public opinion on the prospective use of waste process heat for district heating” Proceedings of SusTEM 2010, Newcastle Upon Tyne. Walsh, C. and Thornley, P., Lifecycle impacts and techno-economic benefits associated with the introduction of a condensing boiler to a woodchip fluidized bed boiler, 19th Euroepan Biomass Conference, Berlin 2011 Yasmine Ammar, Hanning Li, Conor Walsh, Vinol Rego, PatriciaThornley, Vida Sharifi, Tony Roskilly "Desalination using low grade heat in the process industry: challenges and perspectives", submitted Jan 2012 Reports Walsh, C. and Thornley, P., “Stakeholder views on barriers to utilisation of low grade heat for process efficiency improvements”, University of Manchester, 2010


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Thornley, P. and Walsh, C. “Addressing the barriers to utilisation of low grade heat from the thermal process industries, University of Manchester, 2010 Other outputs 2011 Paul Upham was Invited speaker on public perceptions at the International Energy Agency Committee on Energy Research and Technology, Expert’s research group (EGRD) event "The Transition to a Low-Carbon Society: Socio-Economic Considerations", Will Baden, Austria 24-25 May 2011.


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A comparison of two low grade heat recovery options

Conor Walsh a*

, Patricia Thornley a

a Tyndall Centre for Climate Change Research, Pariser Building, The University of Manchester, Manchester, M13 9PL,

UK

* Corresponding author. Email: [email protected]; Tel. +44(0) 1612754332

Abstract Low grade heat (LGH) recovery is one way of increasing industrial energy efficiency and

reducing associated greenhouse gas emissions. The organic rankine cycle (ORC) and condensing

boilers are two options that can be used to recover low grade heat (<250 °C). This paper assesses

the lifecycle greenhouse gas reduction impacts and discounted payback periods associated with

both technologies. Generation of electricity through the operation of the ORC saves

approximately 11 kt of CO2 annually, but the high carbon intensity of the coking process means

this has a negligible influence (<1 %) on the overall process lifecycle impacts. However, if the

electricity generated offsets the external purchasing of electricity this results in favourable

economic payback periods of between 3 and 6 years. The operation of a condensing boiler within

a woodchip boiler reduces the fuel required to achieve an increased thermal output. The thermal

efficiency gains reduce the lifecycle impacts by between 11 and 21%., and reflect payback

periods as low as 1.5 to 2 years, depending on the condenser type and wood supply chain. The

two case studies are used to highlight the difficulty in identifying LGH recovery solutions that

satisfy multiple environmental, economic and wider objectives.

Keywords: Low grade heat; lifecycle assessment; discounted payback

1 Introduction

The recovery of low grade heat (LGH) has been recognised as a potential means of improving the

energy efficiency of industrial installations. Most industrial installations emit large quantities of

LGH as part of normal operations. Traditionally increasing energy consumption was seen as

being preferable to recovered heat of lower thermal quality. In most instances, the feasibility of

LGH recovery will depend on the thermal quality of the heat as well as its potential uses. Ideally,

recovered heat will have the capacity to be used within the installation itself. Alternatively, “over

the fence” options will have to be evaluated. In order to be appropriate for integration into

existing industrial processes, LGH recovery options must meet a number of the requirements.

Process managers will need reassurances on any new technology, particular if the process is well

established. The need to balance the perceived risk and potential rewards is a barrier to the uptake

of new technologies. Primarily the technology must be suitable for the low temperature range

involved. The integration of any new technology must be unobtrusive in terms of normal process

operation. Any potential technology must be sufficiently flexible to reflect the potential

variability within the process, particularly where the process runs continuously. Once these

requirements are met it is vital that there is a use for the recovered heat, either within or outside

the process. Finally any technology must demonstrate sufficient redundancy to allow it to be

repaired or removed without issue. This paper seeks to inform the expectations of what benefits

can be reasonably expected by the recovery of LGH by assessing its potential for reducing the

lifecycle environmental impact associated with two different industrial processes as well as the

likely discounted payback period (DPP).

1.1 Industrial case-studies.

mailto:[email protected]


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Two alternative technologies have been identified as being suitable for the recovery LGH; the

organic rankine cycle (ORC) and condensing boiler. These have been selected as they are

sufficiently different to allow for interesting comparison of the any benefits while demonstrating

established technologies. The first case study identified is the integration of an ORC to recover

heat from the flue gas leaving a coke oven within an integrated steel works. The Rankine cycle is

a thermodynamic cycle which converts heat into work which ultimately generates electricity

through a turbine. It is likely that approximately 80% of the electricity generated globally is a

result of the Rankine cycle. Within a Rankine cycle heat is supplied externally to a closed loop,

which usually uses water as the working fluid. Figure 1 below demonstrates a simplified Rankine

cycle.

Figure 39: simple (Organic) Rankine Cycle taken from Hung et al., [1].

A Rankine cycle which employs water as a working fluid is not economical if recovering heat

below 370°C. For that reason organic chemicals or refrigerants are often substituted for water

within a Rankine cycle, resulting in what has been termed the Organic Rankine Cycle (ORC).

Most organic fluids demonstrate relatively low critical pressures which require ORCs to be

operated at lower pressures and with significantly smaller heat capacities than traditional water-

vapour cycles. In an analogous point to the one discussed above, Lakew and Boland [2] state that

if a process seeks to recover power from condensing vapour then it will be necessary to choose a

working fluid with a critical temperature above that of the source fluid. Therefore, an ORC

system must function below the temperature and pressure at which the fluids are chemically

unstable [3]. McKenna and Norman [4] have identified the iron and steel sector as the largest

user of heat with a heat load of approximately 213 PJ but also demonstrate significant

potential for heat recovery. While a number of streams containing LGH have been identified

within the steel plant but flue gas from coke oven was chosen as being most suitable for recovery.

The coking process itself is integral to modern integrated steel works, as coking coal is the main

reducing agent in the blast furnace. Its suitability is due to the consistent operation of the coke

oven and the high thermal quality compared to other sources of LGH as well as the reduced

potential for process disruption. The gas stream has a temperature of 221 °C with a flow rate of

66 kg/s. This was estimated to yield 21 MW of recoverable energy [5].

One of the most common means of improving thermal efficiency is the introduction of a

condensing boiler to recover latent heat from the waste gas steam. Condensing boilers normally

fall into two main categories, direct and indirect content systems. Within direct-contact

condensing boilers there are no boundaries isolating hot combustion gases from the stream to be

heated. An indirect contact condensing boiler recovers heat from hot flue gases by passing them

through one or more heat exchangers. This benefit is more pronounced for raw biomass systems,

where the higher moisture content means that up to half of the calorific value of the fuel is

recoverable. Both for this reason and its lack of process disruption, Chen et al [6] have examined

the impact of a condensing boiler on a Finnish woodchip fluidized bed boiler which provides heat

for a district heating system. The woodchip plant demonstrates a basic (pre-condenser) output of


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40 MW. The heating systems served by this plant generally consist of water radiators whereby the

return water temperature is between 35 and 40°C. The return water temperature is preheated in

the condenser using both the recovered latent heat of water vapour and the sensible heat of the

flue gas. It is estimated that the condenser increases the thermal output to 52 MW.

2 Material and methods

2.1 Direct carbon savings

The direct carbon saving for both systems are calculated in a different manner. It is estimated that

1 tonne of coke requires 2.95 GJ to produce it. It is estimated that 2% of the energy demand is

satisfied by electricity, 5% is satisfied by steam and 93% by a gas source [4]. This latter may

include natural gas, blast furnace gas or coke oven gas (COG) itself. The calculation of the

emissions associated with the production of coke was based on equation 4.2 published in [7] and

shown below. The equation used in the calculation is shown below:

t CO2/ t coke = [ (1/y)*Ccoal + Σ (Qgas i* EFgas i) – 1* Ccoke ] * 44/12 [Eq 1]

Within the equation y refers to the coke yield (t coke/t coal), Ccoal is the carbon content of coal (%

w/w). Qgas equates to the quantity of gas used in coke production (Gj). EFgas represents the

emission factor for each specific gas (t C/Mj). Ccoke refers to the carbon content of coal (% w/w).

44/12 is used to translate C into CO2. At the steelworks under review, the underfiring gas used in

the production of coke was a mixture of blast furnace gas and COG. Based on the gas stream data

provided by the environment department of Corus, it was assumed that blast furnace gas and coke

oven gas (COG) represented 50/50 % by volume (A. Patsos, Pers. Comm.). As the COG

represents an energy source provided by the oven itself, its emissions are excluded from Equation

1 in order to prevent double counting. The carbon savings are estimated by comparing the

emissions negated though the generation of electricity by the ORC.

The amount of carbon directly emitted is calculated based on the fuel demands and the carbon

content of the woodchips themselves. The direct carbon savings due to the operation of the

condensing boiler within a woodchip boiler are quantified based on the relative fuel savings per

unit of thermal output. In order to accurately calculate fuel savings, an estimate for the thermal

efficiency of the system without the condenser is necessary. Table 1 and Figure 2 below are taken

from [6] and demonstrates the enthalpies of the various boiler process streams. Flow rates and

enthalpy changes are used to calculate the boiler, condenser and combined thermal output, which

in turn allow the thermal efficiency (w/o condenser) to be estimated.

Table 25:

Process stream parameters. Taken from [6]

Description Pressure Stream no. Temp Flow rate Enthalpy

(Bar) (°C) (kg/s) (kj/kg)

Woodchips 1 1 20 5.4 -17.2

Air feed 1 2 20 20.9 -5.15

Flue pre-ESP 1 3 150 26.3 146.7

Flue pre condenser 1 4 150 26.3 146.7

Flue gas to stack 1 5 35 23 10.6

Hot water from boiler 16 6 140 111.6 590

Return water 16 7 30 111.6 128

Preheated water 16 8 55 111.6 231.7

Condensate 1 9 35 3.32 42.4


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Figure 2: Woodchip boiler process diagram, taken from [6].

Based on the change in enthalpy between the hot and return water stream, the thermal output is

confirmed at 52 MW. The change in enthalpies between the hot water and preheated water

streams is used to estimate the output of the boiler itself. Relative fuel savings (not to be confused

with fuel efficiency) per thermal output are estimated using equation 2.

% fuel Savings = 1- ( % efficiency without condenser/ % efficiency with condenser) [Eq 2]

In order to gauge any additional and indirect benefits of LGH recovery, two alternative case

studies (representing offsite and terminal woodchip production) are assessed from a lifecycle and

techno-economic perspective.

2.2 Lifecycle assessment

Lifecycle assessment (LCA) attempts to collate and characterise the environmental impacts

(including climate change as well as wider impact categories) associated with the production, use

and disposal of a product or service. Within LCA all associated impacts are expressed in terms of

a rational reference, termed a functional unit. In order to communicate the lifecycle impact of

LGH recovery, the reference unit is expressed in terms of process output. In this case, 1 kg of

coking coal and 1 MWh of heat are used as the functional units to which energy and resource

requirements as well as emissions are allocated. In order to reflect the impact of both technologies

two lifecycle modules are generated for each case study, reflecting conditions with or without

LGH recovery technology. This is vital as any potential LGH technology will also represent

additional material and energy requirements.

The lifecycle impacts are modelled using a proprietary software package [8]. Using this method

the on-site emissions are estimated for both processes whereas upstream impacts are estimated

based on data provided from literature. For example, the lifecycle impacts associated with coke

production will include the emissions and resource consumption associated with the production

and overseas transportation of coal for the coking process. By contrast the lifecycle impacts

associated with heat from a woodchip boiler will include the emissions and resources embodied

in the cultivation and harvesting of wood residue. Data from a number of sources were used to

generate lifecycle modules for both systems. Because of the amount and diversity of the data

necessary to populate a LCA it is unfeasible to present the data here.

2.3 Techno-economic analysis

It is likely that any attempt to reduce the environmental impact of industrial processes will need

to demonstrate a degree of financial viability. Net Present Value (NPV), represents the difference

between the sum of the discounted cash flows which are expected from the investment and the

amount which is initially invested (equation 3).


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N

nn

n

k

FCNPV

1

0)1(

[Eq 3]

The discounted payback period (DPP) reflects the period in which the cost of investment (and

operation) is recouped. Whereby n is the time period (year), Fn the net cash flow for year n, C0 is

the initial investment, k the discount interest rate, assumed to be 5% and N is the number of years

of the investment’s lifetime or until the invest breaks even. In relation to the ORC it is assumed

that while the external purchasing of electricity is negated the ORC will incur costs due to

installation and maintenance. The Department of Energy and Climate Change estimate that extra

large manufacturing industries paid on average 5.078p (ex vat) per kWh in 2009. The Climate

Change Levy (CCL) for electricity was also estimated at 0.47 p/kWh [15]. For the condensing

boiler the savings due to a reduced woodchip demand is compared against the increased

electricity costs associated with additional fan operation as well as capital and installation costs.

The DPP for the installation in question has already been calculated in [6] whereby relative fuel

savings result in a revenue. In order to provide an alternative, the estimates for fuel savings were

augmented to reflect terminal (onsite) chipping. It is estimated in [16] that the cost at the power

plant for material transported 80 km (average for Finland) is approximately €30-35/solid m3. The

cost of chipping at the terminal is taken from the same source and is estimated at €1.8/solid m3.

Based on estimates of wood density and average annual exchange rates these estimates result in

an increased value of $ 69/tonne, an increase of $9/tonne from the value used in Chen et al. [6].

This changes the impact of wood chip savings and results in a different range of NPV and DPP.

3 Results

3.1 Direct carbon savings

The Aspen Hysys® simulation program was used by the Centre for Process Integration (CPI) at

the University of Manchester to estimate the net energy efficiency of an ORC system used to

recover heat from an equivalent waste stream. In this analysis, it was assumed that Benzene was

the working fluid with a flow rate of 400 kg/mol/h (A. Kapil, Pers. Comm.). The ORC energy

efficiency was calculated at 11% based on the values shown in Table 2.

Table 2

ORC operational parameters in Kj/h. Energy generated Energy consumed Energy supplied Energy released

1,990,000 4,789 17,990,000 15,990,000

The energy efficiency was estimated by subtracting the energy consumed by the pump from the

energy generated by the turbine and dividing by the energy supplied to the boiler. When applied

to the recoverable energy estimate of 21 MW results in an electricity generation estimate of 2.31

MW. (The high hydrogen content of COG results in a higher heat capacity than may be expected

for other combustion gases). The carbon savings due to the offsetting of external electricity are

estimated based on the emission factor for electricity consumption in 2010 [17], taken as 0.54 kg

CO2/kWh. The operational schedule was assumed to be maintained for 8,580 h/y (assuming 98%

availability). This results in an annual carbon saving of 10,702 t CO2. While, when viewed

collectively, this remains a significant carbon savings it does however represent a reduction 1.39

% to the carbon intensity of coke production.

Using data from Table 1, the overall thermal output of the boiler is calculated at 52 MW. Boiler

output is estimated at 40 MW, confirming the output of the condenser at 12 MW. In order to

estimate the thermal efficiency, a value for the thermal input to the boiler is necessary. This is


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estimated using the net calorific value of woodchips (8.16 Mj/kg) and the enthalpies of both the

woodchip and air stream (from Table 1 above). This results in a thermal input estimate of 44

MW, confirming the overall thermal efficiency of the boiler and condenser at 118%, as suggested

in [6]. This also suggests that the thermal efficiency of boiler itself (i.e. without the condenser) is

91%. Using equation 2 it is estimated that incorporating a condensing boiler will result in a fuel

saving of 22%. Given that the estimates in Table 1 include the operation of the condenser, it is

assumed to correspond to this saving, representing 78% of the wood necessary to achieve an

output of 52 MW without the use of a condenser. Based on this the operation of the condenser is

assumed to avoid an additional 38,381 tonnes of woodchip (and associated 36,059 tonnes of CO2)

annually. It must be clarified that the operation of a condensing boiler will not result in a decrease

in the actual amount of carbon emitted from the facility. Rather the increased thermal efficiency

will reduce the carbon intensity per unit of output by allowing for an increased district heating

capacity without the need for additional woodchip inputs (which may presumably offset an

increased fuel use at domestic level).

3.2 Lifecycle savings

Due to the large amount of data involved it is unfeasible to include all the data applied in both

calculations. In order to examine the lifecycle implications of installing an ORC system and a

condensing boiler boiler, process specific information was incorporated into modules generated

by [8]. Two separate modules were calculated for each case study, one in which LGH is

recovered and one in which it is not. Keeping all other factors equal, the impact of LGH use is

estimated using a lifecycle assessment method. The assessment is carried out using CML3 2 mid-

point impact assessment. Within mid-point analysis, inventory results for each environmental

impact category are multiplied by a characterisation factor which equates individual emissions to

a wider impact category. A simple example is the use of global warming potential to estimate

CO2 equivalents. The main stages in the LCA include coal production, transportation and

production of coke itself. It was assumed that coking coal was transported from Newcastle,

Australia by ship and subsequently by rail. The coal and energy (both electricity and gas) required

within the coking process are a fundamental part of LCA. Default direct emission estimates for

coke production were augmented with more recent values [18] and flue stream composition data

for emission of CO2, CH4, and CO [5]. The environmental impact of the production of additional

materials within an ORC system was also included based on the heat exchanger area requirement

(estimated by the Aspen module). Material compositional information for a suitable turbine and

generator system was provided by Siemens (Webster, Pers. Comm.). As can be seen from the

results in Table 3, negating the consumption of electricity has a negligible effect on the overall

lifecycle impact.

Table 3:

Lifecycle impact of producing 1 kg of coke, including LGH recovery.

Impact category Unit No recovery Recovery % Impact

Abiotic depletion kg Sb eqv 0.03 0.03 -0.36%

Acidification kg SO2 eqv 0.01 0.01 -0.43%

Eutrophication kg PO4 eqv 0.0049 0.0049 -0.29%

GWP100 kg CO2 eqv 9.03 9.02 -0.14%

Ozone depletion kg CFC-11 eqv 4.5 x 10-8

4.47 x 10-8

-0.64%

Human toxicity kg 1,4-DB eqv 0.73 0.72 -0.48%

Fresh water ecotoxicity kg 1,4-DB eqv 0.71 0.71 -0.32%

3 CML is an (non English) abbreviation for the Institute of Environmental Sciences at the University of

Leiden in the Netherlands.


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Marine ecotoxicity kg 1,4-DB eqv 1582.89 1577.15 -0.36%

Terr. ecotoxicity kg 1,4-DB eqv 0.01 0.01 -0.54%

Photochemical oxidation kg C2H4 0.02 0.02 -0.01%

Because of its capacity to reduce the relative feedstock demands associated with district heating

the lifecycle effects of LGH recovery are more pronounced. The results below incorporate data

from a number of distinct lifecycle stages such as forest nursery, tree cultivation, felling, as well

as boiler operation. As can be seen from the Table 4, the operation of a condensing boiler (and

associated woodchip savings) reduces the lifecycle impact estimate for most impact categories.

The exception being ‘abiotic depletion’ and ‘freshwater aquatic eco-toxicology.’ This is not to be

unexpected given the increased impacts associated with condensate treatment. Including these

categories, a condensing boiler is seen to reduce the lifecycle impacts by an average of 13%.

Table 4:

Lifecycle impact of producing 1 MWh of district heat, including LGH recovery.

Impact category Unit No recovery Recovery % Impact

Abiotic depletion kg Sb eqv 0.15 0.15 1.48 %

Acidification kg SO2 eqv 1.33 1.03 -22.05 %

Eutrophication kg PO4 eqv 0.37 0.29 -22.08 %

GWP100 kg CO2 eqv 38.79 36.16 -6.80 %

Ozone depletion kg CFC-11 eqv 0.0002 0.0002 -22.95 %

Human toxicity kg 1,4-DB eqv 82.33 65.75 -20.15 %

Fresh water ecotoxicity kg 1,4-DB eqv 9.98 10.26 2.82 %

Marine ecotoxicity kg 1,4-DB eqv 15967.14 15365.26 -3.77 %

Terr. ecotoxicity kg 1,4-DB eqv 0.57 0.49 -14.61 %

Photochemical oxidation kg C2H4 0.10 0.08 -22.49 %

The overall lifecycle impact reduction may seem disappointing considering the associated fuel

reduction, however the requirement to maintain flue stream buoyancy after condensation means

that the operation of the fan consumes significant amounts of electricity. As electricity is a

secondary energy source it will have a greater lifecycle impact (per unit of energy) than

woodchips. Indeed the scale of the temperature drop (from 140 ºC to 35 ºC) means that the net

electrical demand at plant (per MWh) is increased when this supplementary requirement is

included.

3.3 Techno-economic analysis

The (installation, engineering, material) costs associated with the installation of the ORC were

based on a power law relationship between power generation and reported installation costs for

projects of various size. Based on the available thermal energy and the estimated efficiency rating

for the ORC in question, the investment cost of a suitable ORC system was estimated to be 2,023

€/kWe. Up to a certain output (1.6 MWe), the ratio between equipment and total costs rose

linearly with output, beyond which the ratio was seen to level off. On average the equipment and

installation/engineering was seen to contribute to 57% and 43% of total cots respectively. It was

assumed that annual operational and maintenance costs amount to 4% of total investment costs.

Assuming a discount rate of 5%, the offsetting of purchased electricity the proposed project is

seen to break even in 3-6 years, depending on the elements of the calculation. It is reasonable

that 5 years represents an upper limit for an acceptable DPP but a period of 3 years would

probably be necessary to ensure investment.

Table 5


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DPP and NPV for ORC investment based on CCL and Tax. 5% discount rate.

Calculation Cap Ex Cap Ex +25% Cap Ex -25%

DPP (yr) NPV (£)

DPP

(yr) NPV (£) DPP (yr) NPV (£)

CCL, no Vat 4.16 726,858 5.34 538,936 3.03 873,752

No CCL, no Vat 4.59 323,554 5.91 66,120 3.34 543,436

CCL, 17.5% Vat 3.53 489,834 4.52 480,941 2.59 448,748

No CCL, 17.5% Vat 3.84 159,518 4.85 150,625 2.81 195,069

The investments cost associated with the condenser will be determined by the choice of material.

Equipment costs and installation costs are approximately equal. The main ancillary cost

associated reflects the additional energy required to power the flue gas fan necessary to maintain

buoyancy after the stark reduction in flue gas temperature following condensation. Additional

costs include maintenance and condensate treatment. The revenue is based on fuel savings

associated with the increased thermal efficiency. As stated previously, the savings associated with

a different chipping regime has been included to test the sensitivity of woodchip price.

Table 6

Impact of chipping regime on DPP in years. Off-site chipping estimates taken from [3].

Discount rate Discount rate

5% 10% 15% 5% 10% 15%

Off-Site Chipping DPP Terminal Chipping DPP

Stainless Steel 4.75 5.61 6.93 3.7 4.2 4.89

Carbon Steel 1.7 1.82 1.94 1.36 1.45 1.55

As can be seen from the figures above, the increased costs associated with terminal chipping

enhances the benefits derived from fuel savings. The results above suggest that the increased cost

(+ 15%) of woodchips provided by terminal chipping increase the benefit of the any associated

fuel savings, decreasing the DPP by an average of 23%. Based on both analyses it would appear

that the recovery of LGH can be economically feasible although this will depend on the targets

set by industry.

4 Discussion

The results in table 3 show that for the particular LCA weighting system chosen for this

evaluation there is a negligible (<1%) benefit in the overall environmental impact of the coke

production system obtained by installation of an ORC system. The largest improvements are in

the reduction to the extent of fossil fuel depletion, while reductions in carcinogen and respiratory

organic levels are also achieved, correlating with this reduced fossil fuel combustion. There is

also a small reduction in the climate change impact of the overall system achieved by installing

the ORC. This reduction must be viewed within the context of coke production itself. The

recovery of LGH in the form of electricity does not have the capacity to reduce the demand for

coal or gaseous feedstock which, due to the nature of coke production, can not be meaningfully

substituted with electricity. Similarly, the combustion of blast furnace gas prevents the need for it

to be flared. This would have been the case regardless of whether it was used within the coke

oven or not. This case study provides an example of the difficulties in discussing normalised and

overall emission savings. While the percentage reduction in fossil fuel use or global warming

potential achieved is small, the scale of the industry in the UK is large, magnifying its potential

impact. McKenna and Norman [4] estimate an annual coke capacity of 4.31 Mt for the UK and

applying the savings above to this total capacity would result in annual carbon savings of 43,100


133

tonnes of CO2. In order to place this value in context, the current target is to reduce UK emissions

by 34% of 1990 estimates. In 1990 the iron and steel sector emitted over 24 MT of CO2. A 34%

reduction would represent 8.2 MT of CO2. The overall carbon savings of widespread ORC

implementation would contribute to 0.5% of the required savings. When viewed collectively this

represents a significant carbon saving and may provide a more advantageous appraisal of the use

of ORC. Corus (who operate the integrated steel facility in question) estimate that 40% of their

electricity demand is currently satisfied by on-site generation such as the use of coke oven and

blast furnace gases. It is estimated that this will save approximately 700,000 tonnes CO2.

Adopting ORC technology may conceivably increase the current emission savings by 6.2%. This

advocacy should however be viewed with a caveat. Modern steel will generally be optimised at

the higher temperature range through pinch analysis and a complex network of neat exchangers.

For that reason, the recoverable LGH within a steelworks may be of insufficient thermal quality

to warrant attention. However the production of coke is a relatively standard process so the

estimate for recoverable energy is presented as being feasible. The effective determinant will be

whether the plant is an integrated steelworks which produces coke onsite or whether coke is

produced offsite and imported directly.

As stated previously the operation of the condensing boiler will significantly reduce the plume

temperature and convective flow, requiring an increased electricity demand. More immediately, this will result in the exhaust appearing as a continuous plume of steam which may contradict

existent planning or environmental licensing and regulation. This may impact upon plant location

and determine additional factors such as stack height. Because of the impact on ambient

temperature on plume buoyancy, the legal implication of plume buoyancy may be regionally

specific. In some instances this may potentially negate the option of installing a condensing

boiler. In examining the lifecycle impact of condensing boiler operation, the impact of an

increased electricity demand is seen to reduce the benefits of a significant fuel reduction. This is

significant given that electricity is a secondary energy source which incorporates the impacts not

just associated with generating the electricity itself but also those impacts embodied in electrical

infrastructure and the production of the primary fuels upon which electricity is dependent. By

contrast, as the impacts are allocated through the functional life of the plant, reducing the material

requirements associated with the condensing boiler has a less discernible effect on the overall

impact. By way of comparison, the lifecycle impacts of reducing the plant based electricity

demands by 50% are examined using the same impact categories as in Table 4. A reduction of the

electricity demand of the condenser fan by 50% results in an average lifecycle impact saving of

17% across all categories. This reinforces the importance of a secondary energy source within

LCA. If the additional electricity demands associated with the condenser can be negated by

electricity savings elsewhere in the plant, the associated lifecycle impacts are reduced by an

average value of 20%. The specific elements of the chosen impact assessment should not be

ignored. By way of comparison, the lifecycle inventory data for the woodchip plant was

reassessed by the Eco-indicator 99 endpoint assessment method (which attempts to quantify

actual human and environmental impacts such as losses to human health and species richness),

normalised to west European conditions. Using this method the actions of the condensing boiler

were seen to increase the lifecycle impact savings to 21%.

It should be mentioned however that the thermal efficiency gains supplied by the condensing

boiler do not provide a realistic appraisal of condensing boiler operations in general. This is due

to the high moisture content associated with flue streams from the combustion of raw biomass.

The plant under review represents one of the largest biomass fuelled plants in Finland and so may

not be representative of condensing boiler applications in general. In other countries, for example,

natural gas may represent a more realistic fuel of choice. In order to reassess the potential impact

of a different fuel choice, the data in Table 1 was replicated by substituting natural gas. (100%


134

methane was assumed for simplicity). It is assumed necessary to maintain the same overall output

of 52 MW. The net calorific value of methane and its stoichiometric combustion pathway

(assuming 20% excess air) are used to estimate the flow rate of fuel and flue gas. The flue gas is

assumed to have the same temperature (pre and post condenser) and that the same quotient of

latent and sensible heat is recovered. Based on these assumptions the contribution of the

condensing boiler is reduced from 12 to 7 MW. This serves to reduce the fuel savings from 22%

to 14%. While this does reduce the thermal efficacy of the condensing boiler, and cautions

against an overly optimistic appraisal it does show that a condensing boiler can result in a fuel

savings within different markets.

In undertaking a technoeconomic analysis, the economic value of the electricity displaced by the

ORC is significant and could offer potentially attractive payback periods. (Although this will be

based on the chosen discount rates, increasing the discount rate to 10% is seen to increase the

DPP of the base case by an average of 14%. Increasing the discount rate to 20% increases the

DPP by approximately 72%). However, this is also reliant on the difference between electricity

selling and purchase prices. If the site owner/operator were to sell the electricity the revenue from

this would be much lower than the cost savings incurred by their not having to purchase the

electricity from an external supplier. In other words, reducing the demand for external electricity

will result in a much shorter payback period than can be expected if electricity or carbon offsets

are sold on the market. This is likely to prove significant for other LGH recovery systems where

the capacity to directly use recovered energy (in this case electricity) may not be available.

Despite the benefits of adopting “over the fence” benefits, the economic reality of these scenarios

in the current market may act as a barrier to implementation. By contrast the provision of district

heat through the use of condenser presents a different set of challenges. As opposed to the

operation of the coke oven, heat itself is the main process output. This means that while there

may be a consistent demand for the heat, the peak demand for will need to be satisfied. The

output of the boiler (without the condenser) represents full fuel feed so it could be argued that the

additional output of the condenser may (wholly or partially) satisfy peak demand. In that regard

value of the additional heat may represent more rational revenue for the condenser than a

reduction in woodchip demand. Assuming an average price of 45 €/MWh [19] for Finnish district

heat and an operational period of 7,000 hours pa, this additional output of 12 MW is seen to

reduce the DPP to 1.4 and 0.54 years for stainless and carbon steel respectively.

Perhaps the most interesting point of debate is the implications for determining both assessment

criteria and targets for the recovery of LGH. Both case studies represent fundamentally different

systems with which to recover heat in different forms. This also reaffirms that each case study for

the recovery of LGH must be viewed within its own context. While the electricity generated using

the ORC does not demonstrate a significant reduction per functional unit it is important to

remember the ORC cannot change the feedstock demand of an inherently carbon intensive

process. However the overall carbon savings may be seen as being significant. By contrast the

operation of the condensing boiler will not result in a reduction in the actual emissions and

requires extra demand (which again questions the wider applicability of the assessment) to

capitalise on this increase in thermal efficiency. The question of whether overall or normalised

reduction targets should be adopted require more input than be afforded by two case studies.

However theses studies raise two important points. Firstly, the fundamental variability in LGH

supply and recovery will frustrate attempts to standardise any assessment criteria and targets.

Secondly it is likely that lifecycle resource demands will reduce the impact of LGH recovery and

has implications for any proposed targets.

5 Conclusion


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The economic and environmental analyses provide disparate appraisals of the impact of the ORC

to recover LGH from flue gas emitted during coke production. The process under review is a

carbon intensive process, particularly when blast furnace gas is used. Despite this, the potential

savings due to on-site electricity generation suggest a DPP of less than 4 years. This is reliant on

the difference between electricity selling and purchase prices. The operation of a condensing

boiler has been shown to increase plant thermal efficiency from 91% to 118%. This increases the

thermal output to 52 MW. However the use of the condensing boiler necessitates additional

electricity consumption which reduces the lifecycle benefits of the condenser. In economic terms,

the DPP associated with the condenser varies significantly depending on material type, discount

rate and chipping regime. While many estimates fall within a timeframe of 5 years, it appears that

carbon steel represents a more feasible material choice, particularly if terminal chipping is used.

Overall the results demonstrate that LGH is a variable resource whose utility and capacity to

reduce emission and improve process efficiency will depend not just on the process itself but on

the form in which it is recovered as well as the apparent demand. This variability means that any

proposed criteria or targets for LGH recovery will have to be sufficiently tailored to be widely

applicable but also highlight the advantages which may not be immediately apparent (such as in

the case of the coke oven). Overall it should be reaffirmed that “win-win” scenarios which

perform favourably from both an environmental and economic perspective are possible through

the recovery of LGH.

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References [1] Pre Consultants, Simapro lifecycle software [v7.2], (2007). Amersfoort, the Netherlands. [2] University of Newcastle, National sources of low grade heat available from the process industry EPSRC: Thermal Management of Industrial Processes. (2010). [3] Hung, T.C., Shai, Y.T., Wang, S.K. 1997. A review of Organic Rankine Cycles for the recovery of low-grade waste heat. Energy. 22: 661–667. [4] Kapil, A., 2010. Personal communication. [5] NPI. 2011. Submitted emission estimates for New South Wales coal mines. Australian National Inventory. Department of Sustainability, Environment, Water, Population and Communities. Canberra, Australia. [6] Walsh, C., Thornley, P., 2012. The environmental impact and economic feasibility of introducing an Organic Rankine Cycle to recover low grade heat during the production of metallurgical coke. (In press). [7] Goedkoop, M. and Spriensma R. 2000. The Eco-indicator 99 – A Damage-oriented Method for Life Cycle Impact Assessment. Methodology Report (second ed.) Pré Consultants, B.V. Amersfoort, The Netherlands (17-4-2000) [8] Chen, Q., Finney, K., Li, H., Zhang, X., Zhou, J. Sharifi, V. Swithenbank, J. 2012. Condensing boiler applications in the process industry. Volume 89, Issue 1, January 2012, Pages 30-36 [9] Walsh, C., Thornley, P., (under review) A comparison of two low grade heat recovery options. Paper submitted to Applied thermal engineering. [10] Amnar, Y., Li, H., Walsh, C., Thornley, P., Sharifi, V., Roskilly, T., (under review) Desalination using low grade heat in the process industry: challenges and perspectives. Paper submitted to Applied thermal engineering. [11] Raluy, 2009 Evaluación ambiental de la integración de procesos de producción de agua con sistemas de producción de energía. Final year dissertation, University of Zaragoza (2009) [12] Wall, G. 1986. Exergy flows in industrial processes. Chalmers University of Technology .

Final report - Newcastle Universityresearch.ncl.ac.uk/pro-tem/components/pdfs/EPSRC_Thermal... · 2012-05-08 · EP/G060045/1 Final Report 3 Abstract A large amount of low-grade heat

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