Process industry energy retrofits: the importance of emission baselines for greenhouse gas reductions

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Energy Policy 32 (2004) 1375–1388

Process industry energy retrofits: the importance of emission baselines for greenhouse gas reductions ( Anders Adahl*, Simon Harvey, Thore Berntsson Heat and Power Technology Group, Department of Chemical Engineering and Environmental Science, Chalmers University of Technology, Kemivagen 4, SE-412 96 Goteborg, Sweden .

Abstract Fuel combustion for heat and/or electric power production is often the largest contributor of greenhouse gas (GHG) emissions from an industrial process plant. Economically feasible options to reduce these emissions include fuel switching and retrofitting the plant’s energy system. Process integration methods and tools can be used to evaluate potential retrofit measures. For assessing the GHG emissions reduction potential for the measures considered, it is also necessary to define appropriate GHG emission baselines. This paper presents a systematic GHG emission calculation method for retrofit situations including improved heat exchange, integration of combined heat and power (CHP) units, and combinations of both. The proposed method is applied to five different industrial processes in order to compare the impact of process specific parameters and energy market specific parameters. For potential GHG emission reductions the results from the applied study reveal that electricity grid emissions are significantly more important than differences between individual processes. Based on the results of the study, it is suggested that for sustainable investment decision considerations a conservative emission baseline is most appropriate. Even so, new industrial CHP in the Northern European energy market could play a significant role in the common effort to decrease GHG emissions. r 2003 Elsevier Ltd. All rights reserved. Keywords: Greenhouse gas reduction; Emission baselines; Process integration

1. Introduction The process industry sector is faced with the challenge of contributing to global reduction of greenhouse gas (GHG) emissions. Most GHG emissions from process industries are related to combustion of fossil fuel to satisfy process heat demands. For example, in 1998 nearly 80% of the total CO2 emissions from manufacturing industries in Sweden came from fossil fuel combustion (Skarborg, 2001). In existing process industries, the main retrofit options for reducing GHG emissions are improvements in the energy system and fuel switching. In the future, new processes that are more energy efficient need to be implemented. Energy cost savings in industrial energy systems, both grass-root and retrofit design, can be identified with process integration method and tools (Linnhoff et al., 1994; Linnhoff, 1994). In an existing energy intensive industrial energy system such savings can be accom*Corresponding author. Tel.: +46-31-7728533; fax: +46-31821928. ( E-mail address: [email protected] (A. Adahl). 0301-4215/04/$ - see front matter r 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0301-4215(03)00103-4

plished through, in principle, four types of retrofit measures: *

*

* *

reduction of hot and cold utility by improved heat exchange; efficient heating by integration of combined heat and power (CHP) plants; heat recovery by integration of a heat pump; fuel switching.

Energy cost optimisation models have been developed which do not consider the complex interplay between measures (e.g. non-linearity) for retrofit situations (e.g. Boot, 2000; Delaby, 1993). However, to reach a given GHG emission target at the lowest possible cost would normally demand a combination of these measures. Each measure is dependent on the others, and the links between them are complex. A methodology accounting for the complex interplay between different measures has been developed in previous work at the authors’ department (Axelsson et al., 1999; Wiktorsson et al., ( 1998; Adahl et al., 2000). The methodology calculates lowest total cost and associated emissions for different

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mixes of measures (improved heat exchanging, integration of CHP, integration of HP, and fuel switching). The above-described method is suitable for industrial decision-making when the availability of process data is good. With this paper we develop the method further and try to use it on a meso-level1 to discuss different measures in respect to their potential for GHG emission savings and what parameters are most important to reach those emission savings. This paper focuses on CHP generation in combination with improved heat recovery by heat exchanging. For measures changing the net electricity balance at the plant, GHG emissions from electricity producing plants connected to the electricity grid (in the following referred to as electricity grid emissions) are important for global2 GHG emission reductions. For assessing the GHG reduction achieved by an individual project, it is vital to define an accurate, practically applicable and useful emissions baseline (Burer, . 2001; Gustafsson et al., 2000). A baseline tries to answer the question ‘‘what would have happened in absence of the project’’. Burer . gives examples of how different GHG emission trading schemes can work (including both company-wide trading or baseline-andcredit trading schemes). It is particularly difficult to define a suitable baseline for electricity grid emissions. A wide range of literature has recently focused on how the Kyoto flexible mechanisms CDM and JI should be implemented. This literature includes several suggestions for baseline construction (see for example IEA/OECD, 2000; UNEP/OECD/IEA, 2001; PCF, 2000; OECD/ IEA, 2002). UNEP/OECD/IEA presents an extensive survey of all the difficulties involved in selecting a baseline. Different types of projects should use different baselines. In OECD/IEA (2002), recommendations for baseline construction suitable for projects in the electric power sector are given. A distinction is made between ‘‘Build Margin’’3 and ‘‘Operating Margin’’4. Most types of projects are recommended to use a combination of these, in principal different, margins that is called ‘‘Combined Margin’’.5 The Combined Margin is intended to reflect short- and long-run consequences on the electricity market. For the Northern European electricity market many authors (e.g. Werner, 2001; STEM, 1999) suggest that a suitable GHG emissions baseline can be selected 1

The meso-level is a level in between the micro-level of an individual plant and the macro-level of an economy like a country or a region. 2 In this context global denotes the total GHG emission change resulting from a project. See Section 4.1 for more information. 3 Built Margin means replacing a facility that would have otherwise been built. 4 Operating Margin means affecting the operation of current and/or future power plants. 5 The Combined Margin is an average of the Built Margin and the Operating Margin.

assuming that coal-fired condensing steam turbine power plant technology currently constitutes the marginal electric power generation technology. However, as described in papers in the previous paragraph, defining a suitable baseline is intrinsically connected to predicting the evolution of electricity markets, which is difficult given the current wave of deregulation and privatisation, and the type of project. The literature contains a wide range of possible scenarios for evolution of electricity markets in Europe, with pertaining GHG emissions baselines. Examples of such scenarios may be found in (STEM, 2001a, b, 2002). A general consensus in most such studies is that a suitable long-term GHG emissions baseline for the electricity grid corresponds to electric power generation in a high-efficiency natural gas-fired combined cycle (NGCC) power plant, assuming that such plants will have the lowest marginal operating costs. This long-term baseline would in such case constitute the future Built Margin. In this paper, we examine integration of an industrial CHP unit, which is assumed to increase the net electricity supply to the grid. We assume that the investigated processes run 24 h a day year round, i.e. the CHP plant needs to deliver heat to the process on a year round basis. Since the CHP unit, due to the heat demand, operates continuously at full load, the additional supply of electricity is of base load type. Therefore, a baseline for electricity grid affecting measures in this paper should preferably be constituted by displaced or delayed base load capacity technologies. Scenarios for future development of energy markets can be developed using macro-economical energy models. One weakness with such models is the inability to allow for illogical behaviour of future decisionmakers. Tol (1998) discusses the need for a better analysis of how current decision-makers must act, given future uncertainties. This requires a broad understanding of how different baselines affect how well different measures can be expected to reduce global GHG emissions during the lifetime of the investment. A number of authors stress the importance of a relevant baseline, but less attention is paid to the implications of different choices of baseline. It is particularly important that adopted baselines be selected so as to steer energy market actors towards long-term sustainable energy solutions.

2. Scope This paper is focused on the interplay between the following process integration retrofit measures: improved heat exchanging, integration of CHP, and fuel switching. The aim is first to develop and present a GHG emissions calculation methodology for combinations of measures, and secondly to perform a comparative study of five different processes, in order to quantify

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the relative importance of different key parameters. Of special interest are the electricity grid GHG emissions and their impact on the choice of measures that should be adopted for sustainable development. We do not intend to suggest a baseline level or baseline construction methodology, but rather discuss consequences of different baseline choices. The conflict between decreasing heat demand by heat integration and integration of a CHP unit is stressed. An important goal is to identify recurrent patterns in the examined processes to reveal which influencing parameters determine the potential for GHG emission reductions for process integration measures taken at a given process plant. Five different industrial processes were studied, based upon available representative steady-state mean stream data values.

3. GHG emission reductions and costs Measures to reduce the GHG emissions should preferably be taken in a cost-effective way, both from a global economic and business economic viewpoint. GHG emission reduction cost-effectiveness may be expressed as follows: ce ¼ DCa =DEa ;

ð1Þ

where ce is the cost-effectiveness, DCa the change in periodical cost caused by GHG emissions reduction measure and DEa the periodical GHG emissions reduction. The target is to minimise ce ; thus projects with low costs and large GHG emissions reductions are to be preferred. This paper focuses on the GHG emissions reduction part of Eq. (1) for process integration retrofit measures. This paper intentionally chooses not to focus on the equally important economic part of the equation. The motivation for this choice may be summarised in three points. *

Currently we face a waiting game. Future, and even near future, policy instruments types and levels are difficult to predict given the uncertain political situation regarding implementation of the Kyoto protocol. Additional costs associated with energy and climate change policy instruments are, however, assumed to constitute a substantial fraction of future energy costs. Energy system options with low GHG emissions are therefore expected to have low additional costs. This in turn implies that when screening for attractive energy system retrofit options, identifying options with low GHG emissions can be considered as a reasonable form of risk management with respect to the risk for increased future costs associated with emitting greenhouse gases.

*

*

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Economic variables tend to be very changeable over time; e.g., exchange rates, economic lifetimes, interest rates, etc. Economic uncertainty may hide valuable GHG information when focusing too much on costeffectiveness. Focusing on GHG results, as a first step, is beneficial since such results are more stable over time and can easily be used as input for future work including economic and cost-effectiveness analysis. A benefit of first considering GHG emissions reduction is that many candidate measures with none or low GHG emissions reduction potential will be screened out, thus reducing the computational burden of further analysis.

In summary, the main idea in this paper is that identifying cost-effective measures should preferably start with finding measures that decrease global GHG emissions, and then proceed with cost calculations.

4. Methodology This chapter presents a methodology to compute GHG emissions from a given industrial process plant for two different energy system retrofit measures, improved heat exchanging and integration of a CHP unit, considered both individually and combined. The system boundary described below is used. The reader is referred to previous work for further information about pinch analysis (Linnhoff et al., 1994; Linnhoff, 1994) and process integration tools developed at the authors department (Axelsson et al., 1999; Wiktorsson ( et al., 1998; Adahl et al., 2000; Axelsson et al., 2003). Emphasis in this section is placed on suggesting how GHG emissions reduction from existing process plants should be computed. Emphasis is also placed on identifying the key parameters that influence GHG emissions reduction and how they interact with each other. 4.1. System boundary Setting an appropriate system boundary is important—and difficult. There is always a risk of setting the boundary in a way that serves the goals you are aiming at. An explanation of how the system boundary is set and what the consequences are is vital for understanding results in a system analysis. A systematic method for setting the system boundary and assessing the GHG emissions resulting from process integration measures at an industrial process plant is discussed in this section. A process integration measure taken at a process plant site can lead to GHG emission consequences at three levels.

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Level Type/location 1 Direct/on-site

Explanation and example Direct GHG emission changes at the plant site. For example, less fuel consumption due to improved heat recovery by heat exchanging.

restricted in this study to interaction with the electric power grid. Further effects due to fuel production chains can however easily be included in the methodology described here if results from LCA studies are available.

To find patterns it is useful first to have an understanding of the parameters involved. In this paper, parameters are divided in three categories (see Table 1) depending on how they affect the results. Some parameters can belong to more than one category. The potential for achieving cost-effective GHG emission reductions depends on all three categories of parameters. This paper attempts to assess the importance of E-parameters (energy market specific) compared to P-parameters (process specific) for the potential of reducing GHG emissions by internal heat exchanging, fuel switching, integration of a CHP unit, and mixes of these measures. As a final comment, T-parameters (technology specific) are held constant in this study, since the aim is not to evaluate technological progress or technological choices, but rather look at system aspects. Representative performance data for best available applicable technology is therefore used.

2

Direct/off-site

Direct changes of off-site GHG emission changes. For example, changes in grid electricity generation emissions as a result of changes in plant site electric power balance. Changes at this level can also include emissions from extraction, processing, and transportation of fuels and post-treatment of waste, i.e. effects included in LCA studies.

3

Indirect/off-site

Includes off-site GHG emission changes that might occur due to the measures taken. For example, decreased biofuel usage in a boiler due to increased heat integration will lead to less demand of biofuel on the (local) biofuel market. The released biofuel can supposedly be used more effectively somewhere else and thus decrease the GHG emissions. Level 3 consequences are very dependent on (local) market mechanisms and are thus very difficult to incorporate in a general methodology.

In order to account for Level 1 effects, it is sufficient to draw the system boundary around the process plant site. In order to account for Levels 2 and 3 effects, it is necessary to widen the system boundary. For Level 2 effects, the widened system must include parts of the electric power generation grid that are affected by changes in the net power balance at the plant site. The fuel production chain must also be included in the Level 2 system boundary if LCA fuel GHG emissions are to be included in the analysis. For Level 3 effects to be considered, it is necessary to further include all other potential fuel market players. In conclusion, accounting for Level 3 effects is complex and is beyond the scope of this study. Discussion of Level 2 effects is

4.2. Classification of parameters (PTE-parameters)

4.3. Enhanced heat exchange By enhancing heat recovery by heat exchange at a process plant site fuel demand decreases and therefore also GHG emissions. Pinch analysis allows the minimum heat demand for the process to be targeted. By knowing the heat content of all streams and their start and target temperatures a heat exchanging network (HEN) can be constructed that minimises the external Table 1 Class of parameter

Description

P-parameters (process specific)

Parameters connected to the examined process industry. Parameters in this class are unique to every single process. Examples of P-parameters are; temperature levels of heat demand, and current heat recovery level. Parameters that are specific to the technology used. Not dependent on the process. Examples of T-parameters are; boiler efficiency, and electricity efficiency of an integrated CHP plant. Parameters with influence on the results that are related to the location of the process industry, i.e. what energy market the process industry is a part of. The industry cannot influence the values of E-parameters. Examples of E-parameters are; GHG emissions from the electricity grid, and fuel prices.

T-parameters (technology specific) E-Parameters (energy market specific)

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heat demand (Carlsson et al., 1993). For an existing plant, there is often a significant opportunity to increase internal heat exchange so that the external heat demand decreases. How much the GHG emissions can be reduced for an existing process plant by enhanced heat exchange is dependent on the current process HEN layout, and the fuel used for heating. The amount of heat that can be saved is equal to the difference between current heat demand (QI ) and heat demand after retrofit (QII ;n ), as expressed in Eq. (2). Maximum savings are achieved when an infinitely small heat transfer temperature difference (0 C) is allowed in heat exchangers: DQsave ¼ QI  QII;n :

ð2Þ

The associated annual GHG emission reduction (DEsave ) is described by DEsave ¼ EI  EII;n ¼ DQsave cfuel

1 top ; Zb

ð3Þ

where cfuel is the GHG emissions from fuel combustion (kg/MWhfuel) Zb the boiler efficiency6 and top the annual operating time (h/year) Parameter classification P-parameter T-parameter DQsave ; top ;

E-parameter

Zb ; cfuel

Comments. Available options to reduce GHG emissions are mostly connected to P-parameter DQsave and to the T-parameter cfuel : 4.4. Integration of a combined heat and power plant When the methodology was applied we considered simple-cycle gas turbine CHP units for cogeneration calculations. However, the following discussion is valid for integration of any type of CHP unit unless it is stated otherwise. The CHP unit is sized to exactly cover the process heat demand, thus it completely replaces the current heat-only boiler (the boiler is kept for peak loads and/or back-up). Integration of the CHP unit is performed using new composite curves developed at the authors department, as described in Wiktorsson et al. (1998). The integration is performed with the aim of maximising CHP total efficiency. The annual change in GHG emissions when integrating a CHP unit (DEsave;CHP ; see Eq. (4)) consists of both on-site and off-site effects (Level 1 and Level 2 effects). On-site GHG emissions change occurs due to change of heating facility from a heat-only boiler (Efuel;I ) to a GT-CHP unit (Efuel;II ) to cover the heat demand. Off-site GHG emission change is due to electricity production from the 6 The boiler efficiency may decrease a little bit according to level n of energy savings. These small variations are neglected.

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integrated CHP unit (DEel ). It is assumed that the produced electricity can be sold on a deregulated market thus displacing electricity production from elsewhere (Level 2 effect): DEsave;CHP ¼ Efuel;I  Efuel;II þ DEel :

ð4Þ

On-site GHG emission changes (Eq. (5)) are due to changed fuel usage (Zb in relation to Zq ) and eventual fuel switching (cfuel;I in relation to cfuel;II ). The overall heat demand remains unchanged and equal to QI : ! cfuel;I cfuel;II QI top :  ð5Þ Efuel;I  Efuel;II ¼ Zb Zq The overall heat efficiency (Zq ) when integrating a simple-cycle gas turbine CHP unit is expressed accord. ing to Stromberg and Berntsson (1995) (Eq. (6)). The heat efficiency is dependent on both technical data for the gas turbine (specific heat flow of exhaust gases, mexg=f ; heat capacity of exhaust gases, cp;exg ; and exhaust gas temperature, Texg ) and lowest process temperature for heat delivery, the stack temperature, Tstack : The predominant factor of influence on the heat efficiency is Tstack : Zq ¼ mexg=f cp;exg ðTexg  Tstack Þ:

ð6Þ

Off-site GHG emission changes can be computed according to Eq. (7): Z DEel ¼ QI el cel top : ð7Þ Zq The relationship between the electrical efficiency Zel of the CHP unit and heat efficiency Zq is equal to the power-to-heat ratio. The specific grid emissions corresponding to the emission baseline level for the project is denoted by cel : cel for CHP integration with year round operation is assumed to correspond to GHG emissions from current and/or future base load capacity in the reference electricity market. Combining Eqs. (4)–(7) leads to Eq. (8) expressing global GHG emissions reduction for integration of a CHP unit at an industrial process plant site: #  QI QI DEsave;CHP ¼ cfuel;I þ ðZel cel  cfuel;II Þ top ; Zb Zq |fflfflfflfflffl{zfflfflfflfflffl} Present GHG emissions

ð8Þ where DEsave;CHP is the annual global GHG emission reduction, QI the present heat demand (MW), Zb the boiler efficiency, Zel the CHP electrical efficiency, Zq the CHP heat efficiency, cfuel;I the GHG emissions from present fuel (kg/MWhfuel), cfuel;II the GHG emissions from new fuel after retrofit measures (kg/MWhfuel), cel

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the GHG emissions from the electricity grid (kg/ MWhel), and top the annual operating time (h/year). Parameter classification P-parameter T-parameter QI ; top ; Zq

E-parameter

Zb ; Zel ; cfuel;I ; cfuel;II cel

Comments. The expression (Zel cel  cfuel;II ) in Eq. (8) indicates that three possible situations can occur: Case 1: (Zel cel  cfuel;II )o0 or Zel cel ocfuel;II : Case 2: ðZel cel  cfuel;II ÞE0 or Zel cel Ecfuel;II : Case 3: ðZel cel  cfuel;II Þ > 0 or Zel cel > cfuel;II : Variations in electrical efficiency values for different gas turbine engines are relatively small. Thus for a given choice of fuel the T-parameters Zel and cfuel;II are fixed and the sign of the expression depends on the Eparameter cel : Assuming a natural gas-fired gas turbine CHP unit with typical performance characteristics, the following analysis is valid. Analysis for Case 1: Case 1 occurs when the reference electricity grid GHG emissions are low, e.g. hydropower, nuclear power or even NGCC plants. When integrating an industrial CHP unit, global GHG emissions reduction is favoured by high heat efficiency. This is obtained when the stack temperature is low. This also indicates an efficient use of fuel. GHG emission reductions can be either positive or negative for this case. High grid GHG emissions favour natural gas-fired GT CHP units. Analysis for Case 2: Case 2 occurs for intermediate level electricity grid GHG emissions; e.g. combination of coal-fired condensing steam turbine power plants and NGCC plants. In this case, the net GHG emission from the new CHP heating facility is balanced by the displaced electricity grid GHG emissions. P-parameters such as heat efficiency do not affect the potential for GHG emissions reduction. Analysis for Case 3: Case 3 occurs when the reference electricity grid GHG emissions are high, corresponding to, e.g. coal-fired condensing power plants. The value of producing electricity at the plant for reducing global GHG emissions is in this case substantial, even with low heat efficiency (i.e. low total efficiency for the CHP unit). A large heat demand is of significant importance for substantial global GHG emission reductions, thus the incentive to reduce the process plant’s external heating requirements by increased heat exchanging is in this case clearly very low. Case 3 is discussed further in a later section of this paper. 4.5. Integration of a CHP unit in combination with enhanced heat exchange For GHG evaluation, combining the two measures is not as easy as simply adding the individual GHG

emissions effects. Conditions for integration of a CHP unit are affected by changes in the heat exchanger network. Enhanced heat exchange not only decreases the external heat demand, but also modifies the temperature levels of process streams requiring external heating. The heat efficiency for an integrated CHP unit often decreases as a result of enhanced heat exchange. Further discussion may be found in Wiktorsson (1998). Eq. (9) describes GHG emission reduction (DEsave;HE;n ) for enhanced heat exchange resulting in a new external heating demand (QII;n ), which is in between current heat demand (QI ) and minimum heat demand (QII;min ). DEsave;HE;n ¼ ðQI  QII;n Þcfuel;I

1 top : Zb

ð9Þ

The GHG emission reductions for the second step, integration of a CHP unit (Esave;CHP;n ), are described by Eq. (10). The integration is carried out for heat demand level n (QII;n ), as stated in Eq. (9). The resulting heat efficiency (Zq;n ) is a P-parameter and thus unique for every level n of heat demand: ! QII;n QII;n DEsave;CHP;n ¼ cfuel;I þ ðZ cel  cfuel;II Þ top : Zb Zq;n el ð10Þ Combining Eqs. (9) and (10) results in Eq. (11) that expresses the global GHG emission reduction (DEsave;HEþCHP;n ) if combining integration of a CHP unit with increased heat exchange, at any given level of heat integration: 

DEsave;HEþCHP;n ¼

QI QII;n cfuel;I þ ðZel cel  cfuel;II Þ top : Zb Zq;n |fflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflffl} |fflfflfflfflffl{zfflfflfflfflffl} |ffl{zffl} T;E P;T

Present GHG emissions

P

ð11Þ

Parameter classification P-parameter T-parameter

E-parameter

QI ; top ; Zq;n ; QII;n Zb ;Zel ; cfuel;I ; cfuel;II cel Comments. Eq. (11) is a generalised version of Eq. (8). The comments that can be made are to some extent similar to those made previously for CHP integration only. The consequences of CHP integration at a given heat integration level n have to be analysed in comparison to increased heat exchange. The heat demand (QII;n ) decreases with increased heat exchange. The heat efficiency (Zq;n ) is strictly a P-parameter, which decreases with decreasing heat demand (QII;n ), as discussed previously. As previously, three cases can be distinguished, depending on the sign of the expression (Zel cel  cfuel;II ).

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Case 1: ðZel cel  cfuel;II Þo0 or Zel cel ocfuel;II : Increasing heat recovery before integrating a CHP unit is beneficial for global GHG emissions reduction. A high heating efficiency Zq for the CHP unit increases the GHG emission reduction potential. Thus, the emissions reduction potential increases with decreasing Qn =Zq;n ratio. Case 2: ðZel cel  cfuel;II ÞE0 or Zel cel Ecfuel;II : In this case, the GHG reduction will be close to 100%, compared to the current heating system. Enhancing heat recovery has no impact on global GHG emissions reduction because the net GHG emissions from a new CHP unit are exactly balanced by the displaced electricity grid GHG emissions, regardless of the size of the process heat demand. Information about the process, except for current heat demand, is not needed for computing global GHG emissions potential in this case. Case 3: ðZel cel  cfuel;II Þ > 0 or Zel cel > cfuel;II : In this case, the global GHG emissions reduction potential resulting from integration of a CHP unit is substantial. The greater the heat demand is, the more substantial the global GHG emissions reduction will be. Thus, for this case, there is a conflict between heat recovery and integration of a CHP unit. The two measures have a counteracting effect on the ability to decrease the GHG emissions. This situation is discussed further in Section 7.

5. Methodology applied A comparative study is performed for five different processes. The purpose is both to apply and show concepts presented in the methodology chapter, and to quantify the importance of different influencing parameters (P-parameters in comparison to E-parameters). The energy market assumed is the future northern Europe electricity market, which is assumed to be deregulated.

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all the processes were obtained from the database of IEA heat pump integration computer programme (IEA Annex 21, 1997). The processes are referred to as processes A–E: A, cellulose acetate; B, chlorine and caustic soda; C, urea; D, aromatic; and E, cheese. The processes were selected in order to represent diversity in size, flow rates and heat demand temperature levels. The main process characteristics are presented in Table 2. The diversity makes a comparison between P- and Eparameters possible and meaningful. The cases were also selected so that an industrial GT CHP unit can be integrated covering the whole heat demand, i.e. the temperature levels of the process heat demands are well below the exhaust gas temperature of a typical gas turbine engine. Targeting calculations with pinch analysis show that the potential for energy savings by internal heat exchange vary a lot between the different processes. It is important to note that the process heat demands are very different in size, and therefore the gas turbine CHP unit sizes to be considered also vary considerably. Different sizes of gas turbines have different performance characteristics. This variation is accounted for in the study. The technical data (Tparameters) are presented in Appendix A. The heat demand profile as a function of the global temperature difference (DTG ) for each of the processes is shown in Fig. 1. The global temperature difference gives an indication of how well the process is heat integrated. A low value for DTG indicates a high degree of heat recovery and therefore a low potential for further heat recovery. DTG is high for most of the processes considered, implying that the current internal heat recovery is poor. In pinch analysis, the grand composite curve (GCC) is a graphical tool used for showing net heat and cooling demand as a function of temperature levels. For the five processes, the GCC for the initial situation is shown in Fig. 2. It can be seen that the pinch temperature varies between processes and therefore the potential for integration of a CHP unit also differs. This is especially valid for integration of gas turbine CHP units.

5.1. Overview of industrial processes considered 5.2. Procedure Five different industrial processes taken from the chemical and food industry are included in this study. Representative steady-state mean stream data values for

Note: Technical input data values (T-parameters) are presented in Appendix A.

Table 2 Process

Heat demand, present (MW)

Heat demand, maximum heat recovery (MW)

Potential reduction of heat demand by maximum heat recovery (MW)

Potential reduction of fuel usage by maximum heat recovery (%)

A B C D E

101.3 17.3 26.4 30.6 6.20

86.4 14.5 18.3 17.5 3.16

14.9 2.8 8.1 13.1 3.04

14.7 16.6 30.8 42.8 49.1

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Process C

120

30

100

25

Current situation

80

Q (MW)

Q (MW)

Process A

60 40

Current situation

20 15 10 5

20 0

0 0

20

40

60

80

0

Global temperature difference (K)

20

60

80

Process D 35

Current situation

30 25

Q (MW)

Q (MW)

Process B 45 40 35 30 25 20 15 10 5 0

40

Global temperature difference (K)

Current situation

20 15 10 5 0

0

20

40

60

80

0

Global temperature difference (K)

20

40

60

80

Global temperature difference (K) Process E

8 7

Q (MW)

6

Current situation

5 4 3 2 1 0 0

20

40

60

80

Global temperature difference (K)

Fig. 1. Heat demand profile for process A–E. In principle, the higher the global temperature difference is, the less heat integrated is the current process layout.

1. For each of the five processes, GHG emissions are calculated for the initial situation, assuming that a heat-only boiler covers the entire heat demand. Different boiler fuels are considered, namely coal, oil, natural gas or biofuel. Different boiler efficiency values are selected for the different fuels used. 2. Different degrees of increased heat recovery are considered for each process. The software Pro-Pi is used to perform the pinch analysis calculations (ProPi software, 2001). The GHG emissions reductions are computed according to Eq. (2) for 100% and 0% of the maximum possible heat recovery.

3. A natural gas-fired gas turbine CHP unit is integrated with the process at the current level of heat recovery. Novel composite curves, as described in (Axelsson et al., 1999), are used so as to achieve integration with the highest possible total efficiency. GHG emissions reductions are calculated according to Eq. (8). Four levels of electricity grid GHG emissions are considered, as described in Table 3. The significant difference between chosen levels also reflects the uncertainty regarding the choice of a suitable grid emissions baseline for this type of project for the North European electricity production system.

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Process A

250

150

T (˚C)

200 150

T (˚C)

100

100

50

50 0

0 0

20

40

60

80

100

0

120

10

20

50

600 500 400 300 200 100 0 -100 0

100 50

T (˚C)

T (˚C)

40

Process D

Process B 150

0 -50 0

30 Q (MW)

Q (MW)

5

10

15

20

25

30

Q (MW)

10

20

30

Q (MW) Process E 200

T (˚C)

150 100 50 0 -50 0

1

2

3

4

5

6

7

Q (MW)

Fig. 2. GCC for process A–E. The figure reveals the significant difference in temperature and heat demand levels for the processes.

6. Results

Table 3 Electricity grid GHG emissions (kg/MWhel)

Corresponding electricity production in the future deregulated northern European electricity market.

40

Based mainly on hydropower and/or nuclear power. NGCC power generation as marginal production technique. European fossil mix based on a mix (over time) of NGCC power generation and coal-fired steam turbine condensing plants as marginal production techniques. Based on conventional coal-fired steam turbine condensing plants as marginal production technique.

380 600

890

4. Combined internal heat exchange and integration of a GT CHP unit is performed for each process, in six steps from 100% to 0% of the maximum possible heat recovery level. The global GHG emission reductions are computed using Eq. (11).

The GHG emission reduction potential for increased heat recovery for each of the examined processes A–E is shown as Set 1 in Fig. 3, assuming that the heat demand is currently supplied by burning oil in a heat-only boiler. It is clear that when only considering increased heat recovery, the potential for GHG emission reduction is proportional to the potential for fuel savings. The resulting GHG emission reduction potential is 15% for process A, and 49% for process E. Only P-parameters influence the results. Since the only emissions reduction measures considered are increased heat recovery, 0% increased heat recovery always has 0% GHG emission reduction for a given process (the zero line in Fig. 3). Considering adding a measure that is affected by Eparameters will show more complex results. The results obtained when combining integration of a CHP unit with increased heat recovery by heat exchange are shown in Fig. 3 (Sets 2). The influence of the Eparameter cel (electricity grid emissions) is considerable. Assuming low reference grid GHG emissions (Set 2:40 in Fig. 3), integration of a CHP unit yields negative

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Relative GHG emission reduction (in percentage of current)

A B CD E

100 A B CD E

A B CD E

Explanations No improved heat exchange M improved heat exchange Max Set 1 Heat exchange only Set 2 :x Heat exchange in combination with integration of a natural gas fired GT-CHP plant when " Cel is × kgMWhel"

0 A B CD E A B CD E

Set 1

Set 2:40

Set 2:380

Set 2:600

Set 2:890 Type of measures

Fig. 3. GHG emission reduction for increased heat exchange and increased heat exchange in combination with integration of a CHP unit. Results for process A–E presented in relative scale assuming oil as present fuel in boiler. Four levels of electricity grid emissions are represented.

results in terms of global GHG emission reduction. In fact, for four processes (A–D) out of five the GHG emission reduction is less than if only considering increased heat recovery. When high electricity grid GHG emissions are considered (Set 2:890 in Fig. 3), a dramatic change can be seen. First of all, similar and substantial potentials for GHG emissions reduction are achieved for all processes considered. GHG emission reductions of more than 150% are achieved for four of the five processes. Secondly, combining integration of CHP with increased internal heat recovery always yields higher GHG emissions reduction than increased heat recovery alone. This is the ‘‘Case 3’’ situation discussed in the theory chapter. Fig. 3 Set 2:600 shows a third intermediate type of result. For this case with relatively high electricity grid GHG emissions, the calculated global GHG emissions reduction for each process is close to 100%. This case illustrates the situation described in Case 2 in the Theory section. When the electricity grid GHG emissions are equal to the integrated CHP unit GHG emissions, the total GHG emission reduction will be exactly 100%. As a consequence, detailed knowledge of the P-parameters is of less importance in this particular case. Based on the results of the case studies presented here, we may conclude that electricity grid GHG emissions lower than 600 kg/MWhel favour increased internal

process heat recovery, whereas higher electricity grid GHG emissions favour integration of GT CHP units without increasing internal heat recovery. It is important to note that in Fig. 3 it is assumed that the heat demand is initially supplied by burning oil in a heat-only boiler. Fig. 4 presents a comprehensive overview of GHG emission reduction in absolute figures for all five processes, assuming different fuels in the initial pre-retrofit system. A common feature for all processes and all starting fuels is that the value of the Eparameter electricity grid GHG emissions is vital for achieving global GHG emission reductions for integration of industrial GT CHP units. The magnitude of influence of P-parameters is lower than for the investigated E-parameter, illustrated by the similar results for the different processes considered. Similarities between results from different processes when integrating a GT CHP unit can be explained by the high exhaust gas temperature from the gas turbine in comparison to the stack temperature. Variations in the processes stack temperature are thus not very important. Even if it is assumed that the electricity grid GHG emissions baseline corresponds to NGCC power plant emissions, it can be seen in Fig. 3 that integration of GT CHP units has a greater impact than increased internal heat recovery. Set 2:380 shows that if combining maximum internal heat recovery with integration of a

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Current fuel

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Fig. 4. GHG emission reductions for process A–E. Results presented in absolute figures assuming coal, oil, natural gas, or biofuel as present fuel. Four levels of electricity grid emissions are represented.

GT CHP unit, the extra GHG emission reduction gain from heat recovery is far less than that contributed by CHP integration. For example, for process D, combining maximum internal heat recovery with CHP integration yields approximately 70% GHG emission reduction, compared to approximately 55% reduction with GT CHP integration alone.

7. Discussion Coal-fired condensing steam turbine power plants with high GHG emissions currently constitute the marginal power production technology on the deregulated North European electric power market. Market forces and energy policy commitments aiming to fulfil

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the Kyoto GHG emission reduction targets will probably result in that added capacity to the North European grid7 will be NGCCs (STEM, 2002). It is however impossible to predict exactly what the true grid emissions baseline will be during the retrofitted plant’s energy system’s lifetime. As discussed in IEA/OECD (2000), constructing a useful baseline is very much a question of a balance between environmental effectiveness and practical economic criterions and constraints. However, this study shows the significant impact the selected baseline level has. For example, when evaluating the effects of integration of a gas turbine CHP unit in combination with increased internal heat recovery, the choice of grid GHG emissions baseline impacts the results in two ways: 1. ranking of global GHG emissions reduction measures (qualitative impact); 2. level of GHG emissions reduction (quantitative impact). It is thus not just a question of how much the GHG emissions can decrease, but also a question of what measures should be taken. The results indicate that for Case 3 situations (high grid GHG emissions baseline), the environmental value of the electricity produced by integrating a CHP unit is so high that maintaining a high heat demand is advantageous. This is from a thermodynamic standpoint not an optimal situation. One way to avoid this problem could be to adopt different baselines depending on the purpose of the study. For example, different electricity grid baseline emissions could be considered for (i) long-term investment decisions and (ii) short-term GHG emissions reduction effects. Baseline (i) adopted for long-term investment decisions must steer towards sustainable development. To avoid gaming8 and free riders9 a conservative baseline is required (IEA/OECD, 2000). For long-term investment decisions, it is clearly most important to chose the right type of measures, i.e. a measure that achieves most GHG emission reductions in the long run. The second baseline (ii) would be quantitative and calculate the environmental effect on a short-term basis. Such baseline would be a ‘‘true’’ baseline at the moment of a decision. For investments in CHP projects in Northern Europe, a conservative grid emissions baseline could be emissions from NGCC power plants and a short-term ‘‘true’’ baseline could be coal-fired condensing power plants. It is also important to point out that in this paper it is assumed that the process plant management is prepared 7

In which for example Sweden, Norway, and Denmark is part of. Gaming occurs when an overestimated baseline is used. More credits are given than what is accurate. 9 Free riders are those who get credits for a project without contributing to environmental additionality. 8

to make a long-term investment in a GT CHP unit with the aim to reap the environmental benefits of GHG emissions reduction due to delivering relatively lowemissions power to the grid. If this is the main goal, it is important to consider alternative fuel usage for natural gas such as investing in a high-efficiency NGCC power plant. The alternative fuel usage would thus coincide with the baseline NGCC, Set 2:380 in Fig. 2. Consider for example the results for process D (Set 2:380 in Fig. 2) which indicate that it would be environmentally advantageous in the long term (NGCC grid emissions baseline) to integrate a natural gas-fired gas turbine CHP unit in combination with maximum internal process heat recovery (i.e. approximately 70% reduction compared to 55% reduction without increasing the degree of heat recovery). Thus, even with a conservative grid emissions baseline, the potential for GHG emissions reduction resulting from integration of a CHP unit is far greater than that resulting from increased internal heat recovery. On the other hand, considering the short-term GHG emission reduction effect (with a coal-fired condensing power plant emissions baseline) results in totally different conclusions. The results for Process D, Set 2:890 in Fig. 2, indicate that the GHG emissions reduction is larger (175%) when maintaining the current heat demand, compared to maximum heat recovery (150% emissions reduction). Rational industrial decision-makers invest in solutions that are economically profitable and robust. The robustness of an investment that reduces GHG emissions is to a great extent connected to types and levels of policy instruments, which are difficult to predict. Therefore, making—or not making—an investment decision is linked to a significant risk. This risk is both of economic and environmental nature. However, selecting a conservative10 grid emissions baseline for investment decisions is economically sound since it avoids overestimating the economic benefits resulting from GHG emissions reduction credits (represented by the future electricity price). Finally, it should be noted that traditional process integration is performed by a bottom-up approach, requiring good knowledge of reaction chemistry, unit operations, process streams, and utility systems (Linnhoff et al., 1994; Linnhoff, 1994). Process integration has so far mostly been applied for finding energy cost savings within the examined plant; exceptions are, e.g. Delaby (1993) and Klemes et al. (1997) who address the possibility of using process integration to target GHG emissions. Adding the challenge of decreasing global warming creates a partly new situation. An important conclusion of this study is that E-parameters (Energy market parameters) have a major impact on GHG emissions reduction potential. As a result from this 10

The term conservative is used in the sense of cautious.

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study, a complement to the traditional bottom-up approach would be to try to first apply a top-down approach. Good knowledge of the relevant energy market will help identify proper measures and understand consequences of GHG emission reducing measures taken at a plant site. The characteristics of the electric power market may well be of prime importance for determining GHG emissions reduction potential compared to the current process layout. Data extraction is in many cases a bottleneck in process integration. A top-down approach can as a first step give a better grip on the influence of different parameters. Furthermore, a number of candidate process integration measures can be screened out at an early stage, and the necessary data extraction for a process integration study can be more selective. Thus, the burden of calculation can in some cases be less if first a top-down approach is put on the problem.

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decision considerations a conservative baseline is most appropriate. Even so, new industrial CHP in the Northern European energy market could play a significant role in the common effort to decrease the GHG emissions.

Acknowledgements The process integration program at the Nordic Energy Research Programme and the Swedish Energy Administration (STEM) are thanked for financial support for this project. Virginie Bader of Compie" gne University of Technology, France is thanked for valuable help with calculations.

Appendix A. Technology specific data Heat-only boiler nominal efficiencies (Zb )

8. Conclusions It is suggested that influencing parameters should be grouped in three categories for better analysis of industrial GHG emissions reduction projects. The groups are Process specific parameters (P), Technology specific parameters (T), and Energy market specific parameters (E). When consider integration of a CHP unit at a process pant site the study shows that electricity grid emissions (an E-parameter) are far more important than individual differences between processes (P-parameters). Based on the results of the case studies presented here, we may conclude that electricity grid GHG emissions lower than 600 kg/MWhel favour increased internal process heat recovery, whereas higher electricity grid GHG emissions favour integration of GT CHP units without increasing internal heat recovery. Moreover, for a high or intermediate electricity grid emission baseline value, individual differences between processes are of little importance. In such cases CHP integration is always beneficial. Increased heat recovery plays in such case little or no role. For a market with a low electricity grid emission baseline value the opposite conclusion can be drawn. In such case increased internal heat recovery is the most beneficial measure to achieve potential GHG emission reductions. By first defining the baseline value for electricity grid emissions it is possible to understand, at an early stage, the importance of integration of a CHP unit compared to increased internal heat recovery. By doing so, detailed process information is not needed to understand the net GHG consequences for integration of a CHP unit in relation to increased internal heat recovery. It is suggested that different baselines could be used according to the purpose of the study. For investment

Coal fired Oil fired Natural gas fired Biofuel fired

0.93 0.87 0.95 0.81

Natural gas-fired gas turbine data: Specific heat flow of exhaust gases, mexg=f , electrical efficiency, Zel and exhaust gas temperature, Texg Process

Process product

A

Cellulose acetate 17.3 Chlorine and caustic soda Urea 26.4 Aromatic 30.6 compounds Cheese 6.20

B

C D E

Heat demand (MW) 101.3

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Zel

Texg ( C)

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0.338 504

1.14 1.14

0.355 516 0.355 516

1.22

0.301 522

0.328 536

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