Applied Energy 92 (2012) 686–693

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Applied Energy

journal homepage: www.elsevier .com/ locate/apenergy

Carbon emission reduction targeting through process integration and fuelswitching with mathematical modeling

B.J. Tiew a, M. Shuhaimi a,⇑, H. Hashim b

a Process Development Research Group, Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak, Malaysiab Faculty of Chemical Engineering, University Teknologi Malaysia, 81310 Skudai, Malaysia

a r t i c l e i n f o a b s t r a c t

Article history:Received 12 May 2010Received in revised form 13 May 2011Accepted 28 July 2011Available online 22 September 2011

Keywords:Greenhouse gasHENRetrofitMINLPEnergy saving

0306-2619/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.apenergy.2011.07.048

Abbreviations: i, hot process stream; j, cold prolocation; f, fuel.⇑ Corresponding author.

E-mail address: Shuham@petronas.com.my (M. Sh

Carbon emission reduction targeting is an important and effective effort for industry to contribute in con-trolling greenhouse gases concentration in atmosphere. Graphical approach has been proposed for CO2

emissions reduction targeting via HEN retrofit and fuel switching. However, it involves potentially timeconsuming manual procedures and the quality of solutions produced greatly depends on designer’s expe-rience and judgment. Besides, graphical approach hardly account for the cost factor during the designphase, thus potentially generate complex design. This paper introduces an MINLP model for simultaneousCO2 emissions reduction targeting via fuel switching and HEN retrofit. A sequential model execution wasproposed along with the proposed model. The application of the model on a crude preheat train casestudy has demonstrated its workability to generate optimal solution for targeted CO2 emissions reductionat minimum payback period.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Human activities are major contributors to the increase ofgreenhouse gases concentration in the upper atmosphere, whichcatalyzed global warming effect and lead to melting of polar icecaps, rising sea level, desertification, and weather disruption.Greenhouse gases are defined as gases that are capable of trappingradiative energy emitted by sun. Among the greenhouse gases gen-erated by human, CO2 generated from fossil fuel combustion forheat and power supply accounts for 68% of estimated total green-house gases [1].

Many approaches have been proposed to control and/or reducethe greenhouse gases emissions, such as carbon capture, fuelswitching, CO2 storage, and process integration. Among all ap-proaches, improvements in efficient use of energy and changes infuel selection appear to be most straight forward as well as finan-cially feasible. Other options are either immature or financially lessattractive to be put into practice [2]. Previous study [3] shows thatswitching fuel to lower carbon-to-hydrogen ratio fuel is a finan-cially attractive option to reduce greenhouse gases emissions. Amethodology has been proposed by Delaby and Smith [4] to

ll rights reserved.

cess stream; k, temperature

uhaimi).

minimize flue gas emissions through fuel switching. The proposedmethod accounts for the fuel cost and annualized fuel switchingcost and developed an economic model [5,6] to determine thefeasibility of several options to minimize greenhouse gasesemissions from power plants.

Ever since energy crisis in the late 1970s, process integrationhas become an important branch of process engineering for thepurpose of valuable resources conservation, which is achievedthrough optimizing efficient use of resources and/or minimizinggeneration of waste in various forms. Two popular approaches ofprocess integration are graphical approach and mathematicalprogramming.

Over the past decade, graphical pinch analysis techniqueand mathematical programming approaches have been widelyadopted to achieve energy consumption reduction by achievingoptimal heat exchanger network (HEN). Gundersen and Naess[7] and also Furman and Sahinidis [8] have published completereviews on HEN synthesis. Pinch analysis focuses more ongraphical approach, such as composite curve and grand compositecurve based on thermodynamic concepts and heuristics [9–14].On the other hand, HEN synthesis is treated as optimizationproblem in mathematical programming approach [15–20]. HENsynthesis with mathematical modeling framework involves theoptimization of a superstructure of possible HEN configuration[18,21]. Adopted in this work is the superstructure proposed byGrossmann [19].

Nomenclature

PBP payback periodsaving saving achieved in utility costinvcost investment cost for HEN retrofitCOreduction

2 CO2 emissions reduction achievedpreCO2 CO2 emissions rate of existing HENCOemission

2 CO2 emissions rate of HEN after retrofitdesCOreduction

2 CO2 emissions reduction targetPBPmax maximum allowable payback periodqcu heat transferred in between of hot process and cold util-

ity streamqhu heat transferred in between of hot utility and cold pro-

cess streamzf binary variable for fuel selectionzp binary variable for existence of HExzhu binary variable for existence of heaterzcu binary variable for existence of coolergb efficiency of external heating devicedhu delivery efficiency of the hot utilityQfuel amount of heat supplied by fuel

Qf amount of heat supplied by specific fuelCER carbon to energy ratio of specific fuelAp area of HExAhu area of heaterAcu area of coolercostU total utility costcosthu unit cost for hot utilitycostcu unit cost for cold utilityprecostU total utility cost for existing HExk1 cost for additional HEx or heater or cooler unitk2 cost for additional HEx areak3 cost for additional heater areak4 cost for additional cooler areaacoeff area cost coefficient for HExhucoeff area cost coefficient for heatercucoeff area cost coefficient for cooleraexp constant exponential value for HEx/heater/cooler area

B.J. Tiew et al. / Applied Energy 92 (2012) 686–693 687

CO2 emission is an important issue in chemical processingindustry. Retrofit of existing HEN and fuel switching appear tobe favorable approaches to reduce carbon emissions. Studieson the effect of CO2 emissions reduction targeting throughcombined HEN retrofit and fuel switching have been carriedout in graphical approach [22]. Graphical approach provides userinteraction but involves potentially time consuming manual pro-cedures. Besides, the quality of solutions obtained from graphicalapproach greatly depends on the designer’s experience andjudgment in applying the pinch rules and guidelines. Further-more pinch analysis does not account for the cost of structuralchanges implemented in the retrofit design, thus the methodcan potentially generate complex designs which are economicallyinfeasible.

In order to overcome the mentioned issues arisen from graphi-cal approach in CO2 emissions reduction targeting, this paper pro-poses a MINLP model which is capable to achieve targeted CO2

emissions reduction via HEN retrofit and fuel switching for retrofitcase, along with payback period minimization for relevant retrofitsto take place.

2. The principle

2.1. Superstructure

Shown in Fig. 1 is the Synheat model [21] for HEN design. Themodel allows both parallel decoupling of the heat exchanger withits stagewise superstructure. Each process stream enters at itssource temperature and exit at its targeted temperature. The num-ber of stages (NOK) is equal to total number of hot process or coldprocess streams, whichever comes with greater value. At everystage, each hot and cold process stream matches is being consid-ered. Together with the stages structure, all possible matches andits sequences are taken into account. The number of temperaturelocation is equal to NOK+1. Utilization of utility is only being con-sidered at the end point of process streams to ensure processstream achieve its targeted temperature. Only single utility is beingconsidered, although it is possible to include multiple utility if re-quired. It is assumed that pressure drop is negligible. To strengthenthe assumption, no stream-splitting is allowed in the mathematicalmodel in later context to avoid pressure drop due to stream-splitting.

2.2. Area efficiency

For HEN retrofit case, existing HEN area efficiency (a) should beimproved or at least maintained. HEN area efficiency is defined asthe ratio of minimum area requirement (area target) to that actu-ally used in the network for the existing energy usage [23].

CO2 emissions reduction targeting can be achieved through var-ious ways, but only HEN retrofit and fuel switching are being con-sidered in this work. Either method can be adopted individually toachieve CO2 emissions reduction, but a combination of both meth-ods is the more preferable option. First of all, cost of fuel switchingcan be assumed negligible compared to cost of HEN retrofit [22].Secondly, high HEN retrofit cost usually limits the possible CO2

emissions reduction options that can be achieved solely throughHEN retrofit if constrained by maximum allowable payback period.By combining both fuel switching and HEN retrofit methods, theachievable CO2 emissions reduction is boosted while still obeyingthe allowable payback period constraint.

The effect of combining fuel switching and HEN retrofit is illus-trated in Fig. 2. These three diagrams refer to three different possi-ble scenarios for CO2 emissions reduction targeting throughcombined HEN retrofit and fuel switching. In Fig. 2a, shown thefirst scenario, in which A% of CO2 emissions reduction can beachieved by fuel switching solely while further reduction can beachieved through the combination approach, up to B% of CO2 emis-sions reduction. However, it should be noted that CO2 emissionsreduction targeting lower than A% is only to be achieved throughHEN retrofit solely. If the combination approach is adopted forCO2 emissions reduction targeting below A%, the result obtainedwill eventually deteriorate the efficiency of existing HEN area.

For the scenario shown in Fig. 2b, HEN retrofit solely can onlycontributes maximum C% of CO2 emissions reduction. The applica-tion of fuel switching solely can contributes as much as D% of CO2

emissions reduction, while combination of both approaches wouldhas maximum CO2 emissions reduction of E%. In this scenario, theCO2 emissions reduction targeting is not suggestible to fall in be-tween C% and D% to avoid deterioration of existing HEN surfaceefficiency.

The last scenario is shown in Fig. 2c. In the scenario, maximumachievable CO2 emissions reduction is as much as H%. G% of CO2

emissions reduction can be achieved solely by HEN retrofit whilethe fuel switching solely can contribute as much as F% of CO2

Stage 1 End point of hot process stream

End point of cold process stream

Stage 2

Temperature location 3

Temperature location 1

Temperature location 2

H1

H2

C2

C1

Fig. 1. Synheat model proposed by Yee and Grossmann [21].

688 B.J. Tiew et al. / Applied Energy 92 (2012) 686–693

emissions reduction. Noted that CO2 emissions reduction target,which falls in between F% and G%, can technically be achievedeither by HEN retrofit solely or combination approach. However,since the HEN retrofit is more costly compared to fuel switching,combination approach is more preferable in order to cut downthe required investment cost.

3. Methodology

The three scenarios mentioned earlier pointed out the appropri-ate condition for application of fuel switching solely, HEN retrofitsolely, and combination of both approaches in a given CO2 emis-sions reduction targeting case. In mathematical modeling, the fac-tor of area efficiency can be included in two different approaches:either by formulating a constraint in mathematical model to atleast maintaining existing area efficiency, or through certain modelexecution strategy. In this work, the later approach is selected toprovide better insights to user in retrofit case. Fig. 3 shows the pro-posed model execution strategy.

Input data for the model includes existing plant data such asnumber and surface area of existing heat exchanger, heater andcooler, fuel type adopted, current CO2 emissions rate, annual ex-penses data and process streams data of existing plant. Addition-ally economic parameters such as utility cost and heat exchangerretrofit cost should be provided as well.

As shown in Fig. 3, the input data will first be analyzed in Part A,where all the analysis omits the financial factor. For Step 1 in PartA, Synheat model [24] with no stream splitting together with theproposed Eqs. (1)–(5) are executed to analyze the maximumachievable CO2 emissions reduction (COreduction;max

2 ) through combi-nation of HEN retrofit and fuel switching methods. The result ob-tained in step 1 will be sent to user.

� Objective function:

to maximize CO2 emissions reduction, COreduction

2

COreduction2 ¼ ðpreCO2 � COemission

2 ÞpreCO2

ð1Þ

� CO2 emissions calculation:

COemission2 ¼

Xf

ðQ ff CERf Zf Þ ð2Þ

� Fuel consumption:Qfuel ¼

Xf

ðQ ff Zf Þ ð3Þ

gbdhuQ fuel ¼X

j

qhuj ð4Þ

� Fuel selection:Xf

ðZf Þ ¼ 1 ð5Þ

Eq. (1) is the objective function for Part A Step 1. The termCOreduction

2 has the unit of %CO2 emissions reduction, where preCO2

and COemission2 terms refer to CO2 emissions for existing plant and

after retrofit, respectively.Eq. (2) is an energy balance which accounts for the COemission

2 cal-culation. The term Qf

f refers to amount of specific fuel adopted andCERf is the ratio of CO2 emissions to energy for the specific fuel se-lected. The terms Zf is a binary variable which will have the value of1 if the respective fuel is selected or zero if it is otherwise. It can beeasily spotted that multiple choice of fuel is being considered inthis model.

Eq. (3) is a simple energy balance. The term Qfuel refers to totalamount of fuel energy required. Eq. (4) is another energy balancewhere the term gb, dhu, and qhu

j refer to efficiency of heating deviceadopted (such as boiler), delivery efficiency of specific hot utility,and amount of hot utility energy required by specific cold processstream. Lastly, Eq. (5) is the constraint which ensures only selec-tion of one fuel is allowed.

All equations adopted in Part A Step 1 are adopted in Part A Step2 too, with addition of Eq. (6). Eq. (6) forces the model to choosethe fuel that currently adopted in existing plant, thus the result ob-tained will be the maximum CO2 emissions reduction with onlyHEN retrofit solely (COreduction;hen

2 ). By deducting the COreduction;hen2

from COreduction;max2 , we obtain the CO2 emissions reduction achiev-

able by fuel switching solely (COreduction;fuel2 ).

Zf¼existing ¼ 1 ð6Þ

(a) Scenario 1

0

A

B

Minimum fuel heat duty

Current fuel heat duty

Fuel heat duty

% CO2 emissions reduction

Combination approach

Fuel switching solely

HEN retrofit solely

(b) Scenario 2

Combination approach

Fuel switching solely

HEN retrofit solely

0

Minimum fuel heat duty

Current fuel heat duty

Fuel heat duty

% CO2 emissions reduction

C

E

D

(c) Scenario 3

Combination approach

Fuel switching solely

HEN retrofit solely

Minimum fuel heat duty

Current fuel heat duty

Fuel heat duty

% CO2 emissions reduction

0

F

H

G

Fig. 2. The graphical illustration of integrating fuel switching with the retrofit ofthe HEN of the case study: (a) Scenario 1, (b) scenario 2 and (c) scenario 3.

B.J. Tiew et al. / Applied Energy 92 (2012) 686–693 689

After execution of part A, user will be notified with the values ofCOreduction;max

2 , COreduction;hen2 , and COreduction;fuel

2 . At this step, user will benotified with the potential of CO2 emissions reduction in either

HEN retrofit approach solely, fuel switching approach solely, orcombination of both approaches. Through this, user will has clearerpicture of the case study and thus has better idea in deciding thevalue of CO2 emissions reduction target (desCOreduction

2 ).After deciding the desired value for desCOreduction

2 ,together with the three values obtained in Part A, Part B Step3 will next be executed. In part B, financial factor will be takeninto account. Together with Synheat model and Eqs. (1)–(5), thefollowing equations (Eqs. (7)–(12)) are included in the executiontoo.

The main objective of the model in Step 3 is to achievedesCOreduction

2 with minimum payback period (PBP). Instead of usingmulti-objective optimization approach, the model is formulated assingle objective optimization problem, where payback period min-imization serves as objective function, while CO2 emissions reduc-tion serves as constraint. Simple payback period calculation isadopted in this work.

� Objective function:

Minimize PBP

PBP � sav ing ¼ invcost ð7Þ

Whereby the invcost is the investment cost for CO2 emissionsreduction ($) and saving refers to the saving in utility cost annually($/yr).

Minimization of payback period involves the tradeoff betweensaving maximization and investment cost minimization simulta-neously. The amount of savings achievable are influenced byamount of investment cost. As the investment cost reduces, achiev-able saving will be reduced accordingly, and the amount of CO2

emissions reduction will also be reduced. In short, the shorterthe PBP, the lower is the value of COreduction

2 . For targeting purposes,a lower bound on CO2 emissions reduction is applied to ensure thesolution gives the minimum value of PBP for selected value ofdesCOreduction

2 .

COreduction2 P desCOreduction

2 ð8Þ

Although equality is straight forward option for Eq. (8) above,the inequality is adopted to give more relaxation to the model.

Eq. (9) below is proposed to tighten the search space for thesolution. The term PBPmax refers to maximum allowable PBP,which is a parameter with its value chosen by user. Good approx-imation of PBPmax will help avoid the model from stopping at localoptimal.

PBP 6 PBPmax ð9Þ

The saving is calculated by Eq. (10), which is deduction of utilitycost after retrofit (costU) from utility cost before retrofit (precostU).The utility cost after retrofit is calculated by Eq. (11), which is theaddition of hot and cold utility cost.

sav ing ¼ precostU � costU ð10Þ

costU ¼X

i

qhuj costhu þ

Xi

qcui costcu ð11Þ

Lastly, the investment cost is calculated by Eq. (12).

invcost ¼ k1 þ k2 þ k3 þ k4 ð12Þ

where

k1 PXi;j;k

Zpi;j;k � preZp

!þ

Xj

Zhuj � preZhu

!

þX

i

Zcui � preZcu

!ð12aÞ

Fig. 3. Strategy for model execution.

Furnace

Crude Feed

Desalter

Crude Tower

Naphtha-PA

BPA

ATB

H-Gas Oil

L- Gas Oil

Kerosene

Fig. 4. The process flow diagram of case study.

C1

H6

H5

H4

H3

H2

H1

E6 E5 E4 E3 E2 E1 H1

C1

C2

C3

C4

C5

193.0235.3390 119.0 75.77 90.19 31.54 20

290

0.511051

223.9380

1.821053

8.831072

06.97081

190

100

50

30

40

30

Fig. 5. The grid diagram of existing HEN design for case study.

690 B.J. Tiew et al. / Applied Energy 92 (2012) 686–693

Table 1Process stream data for case study.

Stream Stream name Flow (kg/s) Ts (�C) Tt (�C) HTC (W/m2 �C)

H1 Kerosene 23 180 30 492.2H2 LGO 44 270 40 477.8H3 HGO 13 350 30 439.8H4 ATB 56 380 50 470.7H5 Naphtha 253 150 100 561.5H6 BPA 148 290 190 432.6C1 Crude feed 200 20 390 343.0

Note: Exchanger capital cost ($) = 8600 + 670(area)1. Hot utility cost ($/kW yr) = 70.Cold utility cost ($/kW yr) = 7. CP (J/kg �C) = 2600 for all process streams. Hotstream HTC (W/m2 �C) = 500. Cold stream HTC (W/m2 �C) = 500.

Table 2Heat exchanger data of existing HEN design for case study.

Heat exchanger Heat load (kW) Existing area (m2)

E1 6000 280E2 23,000 1480E3 750 280E4 15,000 800E5 38,480 2760E6 22,000 1360

32.35%

11.86%

20.49%

0.00%0%

5%

10%

15%

20%

25%

30%

35%

50000 60000 70000 80000 90000

% C

O2 e

mis

sion

s re

duct

ion

Fuel heat duty

Result for case study in Part A

Combination approach

HEN retrofit solely

Fig. 6. Result obtained in Part A for case study.

Table 3Summary of solutions for the case study.

Unit Before CO2 emissions r

5% 1

Number of HEX – 6 5 5Number if heater – 1 1 1Number of cooler – 5 5 5Total utility cost $ million 6.004 5.694 5Total HEX area m2 6960 6960 7Total heater area m2 4062 4062 4Total cooler area m2 2597 2597 2CO2 emissions million kg/yr 169.02 160.57 1Fuel type – LPG LPG LInvestment cost required $ million – 0 0Payback period yr – 0 0

B.J. Tiew et al. / Applied Energy 92 (2012) 686–693 691

k2 P acoeff �Xi;j;k

Api;j;k � preAp

!aexp

ð12bÞ

k3 P hucoeff �X

j

Ahuj � preAhu

!aexp

ð12cÞ

k4 P cucoeff �X

i

Acui � preAcu

!aexp

ð12dÞ

Eq. (12a) accounts for the cost of additional heat exchanger,heater and cooler unit, while Eqs. (12b)–(12d) account for cost ofadditional heat exchanger, heater and cooler surface area requiredrespectively.

4. Case study

The case study is modified from Panjeshahi’s work [25]. It con-sists of crude pre-heat train with a furnace, crude tower and a de-salter. As illustrated in Fig. 4, the feed stream is preheated in twosections by interchange with the hot fractionate streams resultingfrom the distillation column. The first section run from storage todesalter while the second from the desalter to the crude tower.Process heating is provided by a furnace to preheat the crude feedbefore entering crude tower. Fig. 5 illustrates the grid diagram forthe existing HEN design as shown in Fig. 4. Table 1 shows thestreams and cost data, while Table 2 shows the existing process-to-process heat exchanger area for the HEN in the case study [25].

The same case study has been adopted in Mahmoud’s work [22]with existing system set to be utilizing oil as fuel. However, in or-der to consolidate the assumption of negligible fuel switching cost[22], in this paper the existing system is set to be utilizing liquefiedpetroleum gas (LPG) as fuel. Heat duty for the only heater (H1) inthis case study is as much as 80418 kW. Together with furnace effi-ciency assumed to be constant value of 85%, 169.02 thousand tonsof CO2 emission is recorded annually under the existing HEN de-sign and fuel selection.

5. Results and discussion

The result from Part A gives the result as shown in Fig. 6. Fuelswitching solely and HEN retrofit solely can achieve 11.86% and amaximum of 20.49% CO2 emission reduction, respectively. Combi-nation of both approaches can achieve maximum 32.35% CO2 emis-sion reduction. The result shows that CO2 emission reductiontargeting is only feasible if desCOreduction

2 is set to be positive valuewhich is equal or smaller than 32.35%.

eduction

0% 15% 20% 25% 30% 32.35%

5 5 6 6 61 1 1 1 16 4 5 4 5

.385 5.998 5.634 5.270 4.906 4.735878 6960 7497 8224 10,136 16,976062 4062 4062 4062 4062 4046597 2597 2597 2597 2597 276452.12 143.67 135.22 126.76 118.31 114.34PG NG NG NG NG NG.615 0 0.365 0.847 2.136 6.831.619 0 0.986 1.154 1.938 5.377

0

1

2

3

4

5

6

0

1

2

3

4

5

6

7

8

Payb

ack

perio

d (y

r)

Cos

t ($

mill

ion)

% CO2 reduction

Cost and payback period against %CO2 emissions reduction target

Investment cost required Total utility cost

Payback period

0% 10% 20% 30% 40%

Fig. 7. Cost against %CO2 emissions reduction target.

02000400060008000

1000012000140001600018000

0% 10% 20% 30% 40%

HEx

sur

face

are

a (m

2 )

%CO2 reduction

Heat exchanger surface area against %CO2 reduction target

Total HEX area Total heater areaTotal cooler area

emissions

Fig. 8. Heat exchanger surface area against %CO2 emissions reduction target.

0.00E+00

5.00E+06

1.00E+07

1.50E+07

2.00E+07

2.50E+07

3.00E+07

3.50E+07

0.00% 10.00% 20.00% 30.00% 40.00%

Inve

stm

ent C

ost R

equi

red

CO2 Emissions Reduction Target

Comparison in between Graphical Approach Result and Mathematical Programming Result

Graphical approach

Mathematical programming

Fig. 9. Result obtained through graphical approach (adjusted value from Mah-moud’s work [22]).

692 B.J. Tiew et al. / Applied Energy 92 (2012) 686–693

As for Part B Step 3, a set of CO2 emissions reduction targetwithin the feasible zone is compiled for the case study to provethe claim of earlier statement regarding the avoidance of deterio-ration in area efficiency. Results obtained are summarized inTable 3.

Figs. 7 and 8 are plotted using data from Table 3. Fig. 7 showszero cost is required to achieve 5% of CO2 emission reduction tar-geting. Fig. 8 also shows that no additional area is required to beinstalled in order to achieve 5% CO2 emission reduction. These indi-cate that the reduction can be achieved by relocating the existingheat exchangers to more suitable stream matches. Neither fuelswitching nor heat exchanger retrofit is required. In other words,the first 5% of CO2 emission reduction is achieved through boostingthe area efficiency of existing HEN.

For 10% CO2 emission reduction, Fig. 7 shows certain amount ofcost is required in order to achieve such reduction. Fig. 8 explainsthat the involved cost is spent on additional heat exchanger area.As explained in the previous section, no fuel switching is requiredfor 10% CO2 emission reduction targeting,which indicates that thearea efficiency of existing HEN is either improved or at least main-tained here.

For the 15% CO2 emission reduction target, again Figs. 7 and 8show no investment cost is required and no additional heatexchanger area required. The target is achieved through fuel

switching and heat exchanger relocation. Fuel switching contributes11.86% of reduction, and the other 3.14% is contributed by relocat-ing heat exchangers, which is an improvement in area efficiency.

For 20%, 25%, 30%, and 32.35% CO2 emission reduction targeting,Figs. 7 and 8 show exponential rise in investment cost and amountof additional heat exchanger area required, accompanied by a stea-dy reduction in utilities cost. The mentioned CO2 reduction target-ing is achieved through fuel switching and heat exchanger retrofit.The model for the case study is coded in GAMS 23.3.3, with24.927 s of executing time to come out with all results shown be-low. The computer adopted is running on Intel Pentium ProcessorT4400 with 2GB DDR2 SDRAM memory using Window XP SP2.

Fig. 9 shows comparison in between result obtained fromgraphical approach [22] with result obtained from model proposedin this paper. As shown in the figure, mathematical approach hasthe ability to generate better result compared to the graphical ap-proach since mathematical approach is capable of improving or atleast maintaining area efficiency of existing HEN.

6. Conclusion

The proposed mathematical model in this context together withits execution strategy are able to achieve CO2 emissions reductiontargeting in shorter period compared to graphical approach,through minimizing payback period, with fuel switching andHEN retrofit option taken into consideration. Besides, the area effi-ciency can be at least maintained or improved through the applica-tion of proposed execution strategy. The proposed mathematicalmodel together with its execution strategy enables user to get bet-ter insight before deciding value of CO2 emissions reduction target.

In this paper, important assumptions such as ‘‘pressure drop arenegligible’’ and ‘‘counter current flow heat transfer’’ have beenmade. Besides, no stream split is allowed in the proposed model.In further development, those important assumptions should beremoved, and the effect of stream splitting should be included.Lastly, more detailed cost estimation for fuel switching and appli-cation of different heat exchanger type should be included in fu-ture development of the model.

Acknowledgments

The authors acknowledge Universiti Teknologi PETRONAS forproviding financial support under the graduate assistance schemeto conduct this work.

B.J. Tiew et al. / Applied Energy 92 (2012) 686–693 693

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