Energy efficiency evaluation of ethylene industries based on DEA models

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1 Eergy efficiecy evaluatio of ethylee idustries based o DEA models CHEN Yaxu, HAN Yogmig, WANG Yaqig, XU Yua, ZHU Quxiog * College of Iformatio Sciece ad Techology, Beiig Uiversity of Chemical Techology, Beiig 0090 Egieerig Research Ceter of Itelliget PSE, Miistry of Educatio i Chia, Beiig 00029, Chia zhuqx@mail.buct.edu.c Abstract: Data evelopmet aalysis (DEA) have bee widely applied i the eergy efficiecy evaluatio of process systems. However, the DEA has may differet kids of models ad characteristics, which leads to differet discrimiatio power i differet applicatio fields. Particularly i efficiecy evaluatio of petrochemical systems, the proper choice of DEA model makes great differece i improvig the sesibility ad accuracy of the result, which cotributes to guidig productio ad improvig the eergy efficiecy. This paper proposes a more appropriate DEA model to evaluate the productio efficiecy of ethylee plats, which ca provide better guidace of ethylee productio ad eergy savig. The empirical results demostrate the effectiveess ad uiversality of the DEACCR model applicatio i eergy efficiecy evaluatio of ethylee plats. Key Words: Data evelopmet aalysis, eergy efficiecy evaluatio, ethylee plats Itroductio Petrochemical idustry is a typical process idustry, producig products cotiuously or semicotiuously ad cosumig high eergy, which leadig to geeratig heavy pollutio. The total eergy cosumptio of petrochemical idustry accouts for about 20% of atioal total idustrial eergy cosumptio [].As a result, petrochemical idustry brigs a lot of pressure to savig eergy ad protectig the eviromet. The differece of average eergy cosumptio of petrochemical idustry betwee Chia ad worldwide advaced level rages from 5% to 5% []. It is clear that petrochemical idustry of Chia falls behid iteratioal level a lot. There is much room i improvemet ad applicatio for eergy efficiecy optimizatio of petrochemical productio process of our coutry. As the pioeer of petrochemical idustry, ethylee idustry productio capacity has become a importat symbol of atioal idustrial level. The petrochemical compaies i Chia produced 9475kt/a ethylee i 202. Ethylee productio eeds kg stadard oil per to [2]. Ethylee output of oil compaies i Chia is 50kt/a, which requires 628.6kg stadard oil [3]. The eergy efficiecy of Chia is far below tha that of developed coutries, while eergy cosumptio is the mai operatig costs i ethylee productio [4].Therefore, aalyzig the eergy efficiecy of ethylee productio is of great sigificace. Nowadays, data evelopmet aalysis (DEA) has bee widely applied i eergy efficiecy aalysis of idustrial productio, ad DEA models have bee exteded ad improved uder differet demads. But there still exists some disadvatages. For example, the results of the DEA ca be affected by iput/output idexes ad quatity of samples, ad too may evaluatio stadards ca make the quatity of decisiomakig uits whose efficiecy value is * This work is supported by Natioal Natural Sciece Foudatio (NNSF) of Chia uder Grat ad the Fudametal Research Fuds for the Cetral Uiversities (YS404). exceeds oe third of the sum of uits, which will lead to iaccurate evaluatio differetiatio [5]. I the productio efficiecy evaluatio of process idustry, Zhu et al. [6] reduced the dimesio of data i petrochemical productio by combiig DEACCR model ad Pricipal Compoet Aalysis (PCA). Geg et al. [7] [8] proposed a DEA Cross Model based o fuzzy data sets (FDEACM), which obtaied directio ad the reaso of performace improvemets for ethylee productio process with the help of the Malmquist idex which improves DEA Cross Model. I the study of DEA Models classificatio, Ma [9] aalyzed ad summarized commo DEA models systematically ad put forward four most represetative basic models of DEA: CCR, BCC, FG ad ST, which are amed after the author who proposed. I the past, the eergy efficiecy of petrochemical idustry was evaluated maily by the improvig ad expadig DEACCR.CCR is a kid of the four classic DEA models. While the other three DEA basic models are researched ad applied rarely. Comparig with four DEA basic models i the eergy efficiecy evaluatio of petrochemical idustries, we ca decide the most suitable oe for eergy efficiecy evaluatio of the petrochemical idustry, ad there are practical sigificace ad referece value for the extesio ad improvemet of the DEA models i the eergy efficiecy evaluatio of the petrochemical idustry i the future. This paper takes ethylee idustry productio for istace, combiig with the basic priciples of DEA models to compare the performace of four DEA basic models, ad the, we fid out the proper DEA model which is more suitable for the ethylee productio eergy efficiecy evaluatio. This research is helpful to guide ethylee productio ad improve eergy efficiecy. 2 Data Evelopmet Aalysis Data Evelopmet Aalysis (DEA) was proposed by the Chares ad Cooper i 978 to evaluate the effectiveess of the decisiomakig uit. DEA has formed a relatively complete model system at preset, ad plays a importat role i decisiomakig ad evaluatio of may areas. DEA /6/$3.00 c 206 IEEE 543

2 model exteds the efficiecy cocept of sigleiput / sigleoutput to Decisio Makig Uits (DMU) relatively effective evaluatio system, which is of multiiput / multipleoutput. DMU ca be a idustrial system or a productio cell. A umber of productio factors make up the "iput", while the output is cosist of a umber of products. These products are the results of decisios produced by DMU, so the DMU is the obect that DEA models evaluate. CCR model, BCC model, FG model ad ST model are the four most represetative models of DEA model system i the DEA theory system [9]. 2. DEACCR CCR model is the basic model i relative efficiecy evaluatio system of fixed scale icome patters [9] : Assumig that evaluatio system has decisio uits, m iputs ad s outputs idexes, the th iput ad output idex vectors of DMU respectively are X =(x,x 2,,x m ) T >0 ad Y =(y,y 2,,y m ) T >0, =,2,...,. The liear programmig of CCR model is expressed as: mi{ θ} st.. Xλ s = θx 0 = DCCR = () Yλ s = Y0 = λ, s, s 0, =,2,..., I model (), X 0 represets iput vector of the th decisio uit (DMU ) ad Y 0 meas the th output vector. represets the weight coefficiet of iput ad output idexes. represets reductio ratio of ivestmet, s ad s are slack variables.,, s ad s are the optimal solutios of the model, which reflects the relative efficiecy of the decisiomakig uits. If the optimal target value * of liear programmig (D CCR) is, ad the optimal solutio * * * > 0, s > 0, s > 0, we call DMU as DEA effective [0]. 2.2 DEABCC BCC Model was proposed by Baker, Chares ad Cooper i 984. This model is the basic model of relative efficiecy evaluatio whose icome scale is variable. It was put forward uder the circumstace that productio possible sets of some problems were ot covex coe. We did ot cocer the circumstace that productio possible sets satisfied coe [9]. Supposig that evaluatio system has decisio uits, m iputs ad s outputs idexes, the iput ad output idex vectors of th DMU respectively are X =(x,x 2,,x m ) T >0 ad Y =(y, y 2,,y m ) T >0, =,2,...,. This model is expressed as: mi { θ} st.. Xλ s = θx0 = BCC = λ = 0 = λ = = λ = D Y s Y, s, s 0,,2,..., (2) I model (2), X 0 represets iput vector of the th decisio uit (DMU ) ad Y 0 meas the th output vector. represets the weight coefficiet of iput ad output idexes. represets reductio ratio of ivestmet. s ad s are slack variables.,, s ad s are the optimal solutios of the model, which reflects the relative efficiecy of the decisiomakig uits. If the optimal target value * of liear programmig (D BCC) is, ad the optimal solutio * * * > 0, s > 0, s > 0, we call DMU as DEA effective []. 2.3 DEAFG While researchig scale merit by usig oparametric methods, Fare ad Grosskopf proposed the ew DEA model i 985, which is ow called FG model. This model is based o the techology assumptios of oicreasig returs to scale [9]. The mathematical model as follows: mi{ θ} st.. Xλ s = θx0 = DFG = Yλ s = Y (3) = λ h = = λ, s, s, h 0, =,2,..., I model (3), X 0 represets iput vector of the th decisio uit (DMU ) ad Y 0 meas the th output vector. represets the weight coefficiet of iput ad output idexes. represets reductio ratio of ivestmet. s, s ad h are slack variables.,, s, s ad h are the optimal solutios of the model, which reflects the relative efficiecy of the decisiomakig uits. If the optimal target value * of liear programmig (D FG) is, ad the optimal solutio * > * * 0, s >0, s >0, h >0, we call DMU as DEA effective [2]. 2.4 DEAST I 990, Seiford ad Thrall preseted ST model, which is based o the techology assumptios of odecreasig returs to scale. The productio possible sets are based o the axiom system: triviality, covexity, ivalidity, expasio ad miimum axiom [9]. The mathematical tabular of this model is: th Chiese Cotrol ad Decisio Coferece (CCDC)

3 mi { θ} st.. Xλ s = θx0 = ST = λ = 0 = λ h = = λ = D Y s Y, s, s, h 0,,2,..., (4) I model (4), X 0 represets iput vector of the th decisio uit (DMU ) ad Y 0 meas the th output vector. represets the weight coefficiet of iput ad output idexes. represets reductio ratio of ivestmet. s, s ad h are slack variables.,, s, s, h are the optimal solutios of the model, which reflects the relative efficiecy of the decisiomakig uits. If the optimal target value * of liear programmig (D ST) * is, the optimal solutio * > 0, * * s >0, s >0, h >0, we call DMU as DEA effective [3]. 3 Ethylee Data Descriptio Differet ethylee plats i ethylee productio choose differet calculatio methods. I order to aalyze the eergy efficiecy of ethylee plats better, we refer to ethylee idustry stadard DB 37/ ad GB/T for uified computig [6 ] [7]. Ethylee productio ca be divided ito two parts as crackig ad separatio. Crackig sectio is the mai core of the whole ethylee plats. It is also the key equipmet of productio plats. While the maority eergy cosumptio of the ethylee plats comes from the crackig sectio. It requires a lot of fuel to provide heat for promotig the crackig reactio while the crackig plats are operatig. At the same time, quech boiler is producig a large amout of steam by recoverig waste heat. To make hydrocarbo reach the best crackig effect i a short time as well as reduce cokig, we should iect steam whe hydrocarbo is supplied to the crackig equipmet. Crackig techology structure is cosist of two radiat sectio ad a commo covectio sectio, as show i Figure : pressure steam); TTL Fuel (fuel gas, light oil, heavy oil); electricity; N2 ad compressed gases. The TTL is abbreviatio for total. While N2 ad compressed gas cosumptio are very low, so we did ot take them ito cosideratio of ethylee eergy efficiecy aalysis process [4]. Accordig to statistics, the eergy cosumptio takes up over 50% of the etire ethylee productio costs. So the eergy cosumptio of ethylee productio iclude the total ivestmet of fuel(ttl Fuel), the total ivestmet of steam(ttl Steam), the total ivestmet of water (TTL Water) ad power (Electricity).Ad eergy cosumptio makes up the iput of ethylee productio eergy efficiecy evaluatio,while the output is ethylee productio [5]. This paper coverts the cosumptio data of ethylee plats which based o coversio relatioship showed i Table ad Table i <Calculatio Method for Eergy Cosumptio i Petrochemical Egieerig Desig> (SH/T30200). It uiformly coverts the measuremet uits of the fuel, steam, water, ad electricity i the cosumptio relevat parameters to GJ [8]. 4 Examples Aalysis I this paper, the data comes from four differet ethylee productio plats. They are aalyzed ad explaied i accordace with same techology uder differet scales ad same scales uder differet techologies. Eergy efficiecy evaluatio i ethylee productio ca be cosidered as a assessmet model based o iput / output. The research obect of eergy efficiecy evaluatio i this paper is ethylee productio plats which cotais a whole set of productio processes. Cosidered as a whole, productio factor of ethylee icludes fuel, steam, water ad electricity. I order to make the results uiversal, we aalyzed two cases: Same Scale Uder Differet Techology ad Same Techology Uder Differet Scale [5]. The istace data comes from actual productio data every moth of each ethylee plats from 20 to Same Scale Uder Differet Techology Based o the four basic DEA model, we evaluate the productio efficiecy i every moth of three years of ethylee plat (800,000 tos, Lummus techology) ad plat 2(800,000 tos, S&W techology). By usig actual productio data, we calculate efficiecies for plat ad plat 2 uder model () / (2) / (3) / (4) respectively. Ad eergy cosumptio makes up the iput, while the output is ethylee productio. The results are show i Figure 2 ad Figure 3. Figure : Flow chart of crackig furace crafts We ca kow from the figure that the eergy types i mai factors of the ethylee productivity iclude: TTL Water (recycled water, idustrial water, boiler water); TTL Steam (ultrahigh pressure steam, high pressure steam, low Figure 2: Four DEA models evaluatig results of ethylee plat th Chiese Cotrol ad Decisio Coferece (CCDC) 545

Figure 3: Four DEA models evaluatig results of ethylee plat 2 By aalyzig ad comparig the productio eergy efficiecy of these two ethylee plats i 36 moths, the umber of decisiomakig uits whose

The proportio of DEA effective i whole uits is less tha oe third [5], which reaches the evaluate discrimiatio.

Obviously, CCR model has better performace i recogizig the oeffective productio moth of DEA productio plats. 4.

4 Figure 3: Four DEA models evaluatig results of ethylee plat 2 By aalyzig ad comparig the productio eergy efficiecy of these two ethylee plats i 36 moths, the umber of decisiomakig uits whose efficiecy value is (DEA effective) of CCR model evaluatio results are respectively 7 ad 3. The proportio of DEA effective i whole uits is less tha oe third [5], which reaches the evaluate discrimiatio. The umber of decisiomakig uits of the two plats whose efficiecy value is usig BCC, FG ad ST models were respectively 3 ad 9. Obviously, CCR model has better performace i recogizig the oeffective productio moth of DEA productio plats. 4.2 Same Techology Uder Differet Scale Similarly, Based o the four basic DEA model, we evaluate the productio efficiecy i every moth of three years of ethylee plat 3(200,000 tos, Lummus techology) ad plat 2(800,000 tos, Lummus techology). The results are show i Figure 4 ad Figure 5. Itegrated the result of ethylee productio efficiecy evaluatio usig four basic DEA models, we ca see that CCR model ca evaluate the efficiecy of ethylee productio better tha the others. 4.3 Productio Guidace To further illustrate the superiority of CCR i ethylee productio efficiecy evaluatio compared to the other three DEA basic models, we itroduce the CCR relaxatio factor results. We ca propose guidace to the moths that ethylee productio efficiecy BCC/FG/ST equal to (DEA effective) while CCR is ot. The relaxatio variable aalysis results of the same scale but differet techologies ethylee plats show i table, where CCR ot equal to but the value of BCC/FG/ST are. Sice CCR is a model whose icome scale is costat, so relaxatio factor s of the output is ot cosidered, ad the experimetal results are all 0. The table shows the result of the relaxatio factor s which is imported by ethylee productio system. The four iputs are fuel total ivestmet, the total ivestmet of steam, water ad electricity ivestmets, respectively. The correspodig value represets the reductio of the iput of the oeffective moth to improve the ethylee productio efficiecy. Take the result of May i 20 for example, ethylee productio efficiecy value of the moth reached (DEA effective) whe water iput reduced 0.3GJ/t ethylee ad electrical iput reduced 0.GJ/t ethylee. I this way, we ca improve productivity ad save eergy. Other moths i which ethylee productio plats are oeffective ca make the same improvemet by referrig to correspodig relaxatio factor, so as to achieve effective eergy efficiecy. The results are show i Figure 6 ad Figure7: Figure 4: Four DEA models evaluatig results of ethylee plat 3 Figure 5: Four DEA models evaluatig results of ethylee plat 4 At this time, the umber of decisiomakig uits that efficiecy value is of CCR model evaluatio results are respectively 5 ad 6,which are both less tha the required oethird. It meas that CCR model has great eergy efficiecy discrimiatio [5].While the umber of other three basic models decisiomakig uits which are DEA effective are respectively were 0 ad. From the eergy efficiecy results of two ethylee productio uit with same techology uder differet scale, we ca see that CCR model has better discrimiatio i ethylee productio eergy efficiecy evaluatio. Figure 6: Productio guidace of ethylee plat Figure 7: Productio guidace of ethylee plat th Chiese Cotrol ad Decisio Coferece (CCDC)

Table : Productio CCR relaxatio factors of ethylee plat ad 2 (GJ/t ethylee) Plat Plat2 May Jul May2 Jul2 Jul3 Mar Apr May Ju Aug Apr3 Fueltoal 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Steamtoal 0.

44 0.20 0.3 0.3 0.3 0.6 Table 2: Productio CCR relaxatio factors of ethylee plat 3 ad 4 (GJ/t ethylee) Plat3 Plat4 Feb Sep Dec Apr3 May3 May Nov Ja2 Oct3 Nov3 Fueltoal 0.00 7.50 0.97 0.00 0.00 0.46 0.

5 Table : Productio CCR relaxatio factors of ethylee plat ad 2 (GJ/t ethylee) Plat Plat2 May Jul May2 Jul2 Jul3 Mar Apr May Ju Aug Apr3 Fueltoal Steamtoal Watertoal Electricity Table 2: Productio CCR relaxatio factors of ethylee plat 3 ad 4 (GJ/t ethylee) Plat3 Plat4 Feb Sep Dec Apr3 May3 May Nov Ja2 Oct3 Nov3 Fueltoal Steamtoal Watertoal Electricity The figures show that the discrimiatio of BCC, FG ad ST models is lower tha the CCR model i evaluatio system of ethylee productio. CCR model ca detect more moths whose productio is oeffective. Adustig eergy productio accordig to the relaxatio factor is helpful to improve productivity of the moth. At the same time, we ca make productio guidace to achieve the purpose of eergy savig. Similarly, the relaxatio variable aalysis results of ethylee plats uder the same techology but differet scales are show i table 2, which CCR is ot equal to but the value of BCC/FG/ST are. The results i the tables ca be made to guide the productio of oeffective moth of ethylee plat 3 ad 4. For example, i September 20, material iput should be reduced by 0.93GJ/t ethylee, steam iput should be reduced by 0.2GJ/t ethylee ad water iput should be reduced by.5 GJ/t. Thus, the ethylee productio efficiecy of the moth ca be DEA effective. The productio guidace of oeffective moths of other plats is show i Figures 8 ad 9: Figure 8: Productio guidace of ethylee plat 3 Figure 9: Productio guidace of ethylee plat 4 I figures 6, 7, 8 ad 9, the blue bars i each chart represet the efficiecy value of BCC/FG/ST equalig to, ad the same moth of CCR is ot which is show i dark red bars. Furthermore, the CCR bars are stacked with fuel/water/steam/electricity bars, which aim to covey the productio guidace clearly. The size of stacked bars meas the iput adustmet of oeffective moth. I short, compared to BCC, FG ad ST models, CCR model ca guide ethylee productio more effectively, which ca improve productio efficiecy ad save eergy. 5 Coclusios This paper aalyzes the priciples of four basic DEA models ad makes eergy efficiecy evaluatio ad productio guidace of productio situatios of ethylee plats uder differet techologies ad scales. By the eergy efficiecy evaluatio of ethylee productio usig the relative efficiecy of decisiomakig uits, we obtai that CCR model has better discrimiatio tha the other three basic models. The CCR ca distiguish more decisiomakig uits whose productivity is ot. Meawhile, CCR model ca better aalyze ad evaluate ethylee productio efficiecy. Meawhile, by importig relaxatio factor i the CCR model, we could guide ad improve productio situatios of the ieffective productio moths to ehace productio efficiecy of ethylee as well as reach the target of eergy savig ad reducig cosumptio th Chiese Cotrol ad Decisio Coferece (CCDC) 547

6 By aalyzig ad comparig four DEA basic models, it ca be more obective ad comprehesive to aalyze ad select DEA model which is more suitable for productio evaluatio of ethylee idustry. By verifyig ad aalyzig actual productio of ethylee plats from differet agles, we cofirmed the effectiveess ad the uiversality of DEACCR model ad provided theoretical basis for choosig suitable eergy efficiecy evaluatio method of ethylee productio. Moreover, i the field of eergy efficiecy evaluatio i ethylee process, there is further space for CCR model research, e.g. improvig the DEACCR evaluatio model to improve the accuracy ad discrimiatio of eergy efficiecy estimatio i ethylee process. Furthermore, we ca add i carbo emissios thought, i.e. carryig out research i efficiecy evaluatio of ethylee process from the perspective of eergy coservatio ad carbo emissios reductio, which cotributes to makig better guidace i ethylee plat eergy savig ad improvig ethylee productivity. Refereces [] Natioal Bureau of Statistics, Chia Statistical Yearbook, Beiig: Chia Statistics Press, 202. [2] Zhag L.J., Liu J., Review of Petrochia's ethylee productio i 2008, Ethyl.Id.,25:70, 203. [3] Ji W.Y., Xu Y.H., Guo X., Review of siopec's ethylee productio i 202, Ethyl.Id.,25:6, 203. [4] Tao R., Patel M., Olefis from covetioal ad heavy feed stocks: Eergy use i steam crackig ad alterative processes. Eergy, 3:42545, [5] Y.M. Ha, Z.Q. Geg, X.B. Gu, Q.X. Zhu, Eergy efficiecy aalysis based o DEA itegrated ISM: A case study for Chiese ethylee idustries. Egieerig Applicatios of Artificial Itelligece, 45:8089, 205. [6] Q.X. Zhu, X. Che, Y.L. He, X.Y. Li, X.B. Gu. Eergy efficiecy aalysis for ethylee plat based o PCADEA. CIESC Joural, 66(): , 205. [7] Y.M. Ha, Z.Q. Geg, Q.X. Zhu, Eergy efficiecy aalysis method based o fuzzy DEA crossmodel for ethylee productio systems i chemical idustry. Eergy, 83:685695, 205. [8] Y.M. Ha, Z.Q. Geg, X.B. Gu, ad Z. Wag, Performace aalysis of Chia ethylee plats by measurig Malmquist productio efficiecy based o a improved Data Evelopmet Aalysis CrossModel, Idustrial & Egieerig Chemistry Research, 54: , 205. [9] Z.X. Ma, Research o the Data Evelopmet Aalysis method. Systems Egieerig ad Electroics, 24(3): 4246, [0] A. Chares, W.W. Cooper, Q.L. Wei, A semiifiite multi criteria programmig approach to data evelopmet aalysis with ifiitely may decisio makig uites. The Uiversity of Texas at Austi, ceter for Cyberetic Studies Report, CCS55, 986. [] R. D. Baker, A. Chares, W.W. Cooper, Some models for estimatig techical ad scale iefficiecies i data evelopmet aalysis. Maagemet Sciece, 30(9): , 984. [2] R. Fare, R. Grosskopf, A oparametric cost approach to scale efficiecy. Joural of Ecoomics, 87(4): , 985. [3] L.M. Seiford, R.M. Thrall, The mathematical programmig approach to frotier aalysis. Joural of Ecoomics, 46:738, 990. [4] Y.M. Ha, Z.Q. Geg, Eergy efficiecy hierarchy evaluatio based o Data Evelopmet Aalysis ad its applicatio i a petrochemical process. Chemical Egieerig & Techology, 37(2): , 205. [5] Z.Q. Geg, Y.M. Ha, X.B. Gu, Q.X. Zhu, Eergy efficiecy estimatio based o data fusio strategy: case study of ethylee product idustry. Idustrial & Egieerig Chemistry Research, 5: , 202. [6] Chia Stadards: the limitatio of eergy cosumptio for ethylee product (DB37/ ), [7] Chia Stadards: the geeral computig guide of special eergy cosumptio (GB/T ), [8] Calculatio method for eergy cosumptio i petrochemical egieerig desig (SH/T30200), th Chiese Cotrol ad Decisio Coferece (CCDC)

The Input-Output Analysis of Construction Industry in West China. Ke Bin Zhang 2, b

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