Conceptual Framework for an Integrated Method to Optimize Sustainability of Engineering Systems

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1 Journal of Energy and Power Engneerng 9 (2015) do: / / D DAVID PUBLISHING Conceptual Framework for an Integrated Method to Optmze Sustanablty of Engneerng Systems Alfredo del Caño, María Plar de la Cruz, Juan José Cartelle and Manuel Lara Department of Industral Engneerng II, Hgher Polytechnc Unversty College, Unversty of Coruña, Campus of Estero, Ferrol 15403, Span Receved: Aprl 11, 2015 / Accepted: June 03, 2015 / Publshed: July 31, Abstract: It s necessary to change the current dynamc of growth, to assure that future generatons can satsfy ther needs. Sustanable development and global sustanablty are two concepts that have acheved great mportance n almost every sector of actvty. There s a wde range of methods and models for sustanablty assessment. Nevertheless, t s necessary to go beyond evaluaton, lookng for sustanablty optmzaton. In spte of ths, lttle work has been done on the latter feld. The authors present here a conceptual proposal for an ntegrated method to optmze sustanablty of engneerng systems, based on the MIVES (ntegrated value method for evaluatng sustanablty) method. For a better understandng of the method, ts essental steps for optmzng an energy sub-system are summarzed (shell-and-tube heat exchanger). Key words: Sustanablty, MIVES, smulaton, optmzaton, metaheurstcs. 1. Introducton It s currently accepted that there are lmts to growth. For ths reason, measures are startng to be taken n order to protect the current and future generatons from the consequences of overcomng those lmts. Certan terms and concepts have arsen from ths trend. Over the years, they have acqured a hgh degree of notorety n almost any sphere of actvty. In partcular, the terms sustanable development and sustanablty come to mnd. On the whole, these terms can be related to the capacty to do somethng wth mnmum or nl mpact on the planet and ts populaton. Unfortunately, almost every actvty has some knd of effect on ts surroundngs. For the moment, t s therefore mpossble to acheve full-scale, strct sustanable development. Perhaps t could be reached n the future, wth the help of new technologes. As stated n the Ro Declaraton on the Envronment Correspondng author: Juan José Cartelle, Ph.D. student, research felds: sustanablty assessment of power plants, sustanablty optmzaton of energy systems and energy economcs. E-mal: juan.cartelle1@udc.es. and Development [1], human bengs are sad to have the rght to a healthy and productve lfe n harmony wth nature. Ths nvolves aspects related to the economy (productve lfe), socety (healthy lfe) and the envronment (n harmony wth nature). Therefore, t may be clamed that, sustanable development les on three basc pllars: envronmental, socal and economc. Some authors consder a fourth pllar, the techncal or techncal-functonal one. Aspects relatng to the envronment nclude polluton control and a reduced consumpton of energy, materals, or other natural resources. Economc factors are related to equalty and development n ths feld, n turn, lnked to the productvty of the planet s resources n the long term. Socal concerns are related to populaton s health and comfort, ensurng a decent lfe and socal stablty for people. Techncal aspects are related to functonal or techncal-technologcal advantages and dsadvantages of the engneerng systems under development. Table 1 lsts potental ndcators that can be ncluded n a sustanablty assessment n the energy sector. They are a selecton of

2 Conceptual Framework for an Integrated Method to Optmze Sustanablty of Engneerng Systems 609 all the sustanablty aspects found n Refs. [2, 3], among many others. There are dfferent methods for sustanablty assessment. Envronmental LCA (lfe-cycle analyss) s probably the most popular one, the varous mpacts on the planet are estmated, takng nto account all the phases of a product s lfe-cycle from cradle to grave, or even from cradle to cradle. LCA uses measurable varables called ndcators. The use of LCA has been extended to the economc and socal felds. Many authors have performed LCA studes n the energy sector. Among many others, Kannan, et al. [4] made a lfe cycle assessment (ncludng a lfe cycle cost analyss) to quantfy the non-renewable energy use and global warmng potental n electrcty generaton from an ol fred steam turbne plant n Sngapore. Odeh and Cockerll [5] examned the lfe cycle greenhouse gas emssons from exstng pulverzed coal power plants n the UK (Unted Kngdom). In Ref. [6], the author presents the results of a LCA of photovoltac energy generaton. Varun, et al. [7] made a revew of exstng lfe cycle analyses of renewable sources based electrcty generaton systems. The results of LCA can be used drectly, but there are methods for ntegratng the dfferent ndcators assessments. The majorty of ntegraton methods currently used n the constructon sector are based on a weghted scorng system for dfferent sustanablty ndcators. Research s beng done at the moment on more sophstcated alternatves, such as the AHP (analytc herarchy process), the MIVES (ntegrated value method for evaluatng sustanablty) method [8], or fuzzy mathematcs. As ponted out above, the sustanablty lterature s manly focused on the assessment, partcularly n envronmental LCA. It s now necessary to go further, lookng for sustanablty optmzaton, for maxmzng the contrbuton to sustanable development of engneerng systems. Despte ths, as far as the authors know, optmzaton has receved almost no attenton so far. There are not publshed studes about the optmzaton of sustanablty assessment models based on the MIVES method. In fact, there are very few publshed studes that dscuss the sustanablty optmzaton from a general pont of vew, and none of them cover wth enough depth the four sustanablty pllars prevously mentoned. The authors are workng n the optmzaton of MIVES assessment models, appled to the energy and constructon sectors. The am of ths paper s to present the man deas and technques to establsh such methodology, usng an energy sub-system (shell-and-tube heat exchanger) as an example of product to be optmzed. 2. The MIVES Method and Its Applcaton to Sustanablty Assessment The MIVES method s a combnaton of technques based on a requrement tree [8, 9], value functons [10], and the AHP [11, 12]. MIVES s used to transform dfferent types of varables, measured wth dfferent Table 1 Sustanablty ndcators n the energy sector. Dmenson Indcators Envronmental Socal Economc Techncalfunctonal Global warmng, depleton of the ozone layer, acdfcaton, onzng radaton, eutrophcaton, heavy metals, carcnogens, wnter smog, summer smog, conventonal waste generaton, specal waste generaton, hgh-level waste generaton, ntermedate-level and low-level waste generaton, sterle waste generaton, avalable reserves of fuel and raw materals, land use, nose, bad odours, mpact geographcal range. Employment generaton, populaton dsplacement, socal benefts, rsk of constructon accdents, rsk of operaton and mantenance accdents, rsk of external accdents, vsual mpact, socal acceptablty, effect on publc budget. Mnng and extracton cost, pre-treatment and enrchment cost, transportaton cost, engneerng cost, process equpment cost, cost of cvl works, cost of fuel and CO 2 emssons rghts, operaton and mantenance cost, decommssonng cost, subsdes. Relablty of electrcty supply, varablty/regularty of electrcty supply, stablty of the power supply chan, uncertanty n generaton, generaton manageablty, maturty.

3 610 Conceptual Framework for an Integrated Method to Optmze Sustanablty of Engneerng Systems unts, n the same dmensonless unt. It makes t possble to consder non-lnearty n the assessment. Moreover, t takes nto account the relatve mportance of the dfferent aspects consdered n the evaluaton. Fnally, t helps to ntegrate envronmental, socal, economc and techncal ndcators n a sngle, global sustanablty ndex. MIVES s composed of seven phases, whch wll be descrbed below. In Phase A, the problem to be solved wll be defned. Ths problem usually entals desgnng an engneerng system n lne wth sustanablty crtera. In fact, MIVES can be used for help n other type of systems or decsons. In Phase B, the requrement tree wll be constructed. Ths tree s a herarchcal scheme n whch the dfferent characterstcs of the product or process to be assessed are defned n an organzed way. It normally has three levels: requrements, crtera and ndcators. Fg. 1 ncludes an example of ths knd of graph. The thrd level defnes the concrete characterstcs that are gong to be assessed (ndcators). The other two levels establsh a structure to break down the requrements. The purpose of the tree s two-fold. On the one hand, the problem s structured to organze the aspects collected n the model and provde a general vew of the problem. On the other hand, the tree later facltates establshng the weghtngs of the ndcators, and calculatng the global sustanablty ndex. In Phase C, the value functons wll be establshed. Here, mathematcal elements from the general decson-makng theory come nto play. In partcular, general aspects from MCDM (mult-crtera decson makng) are used. When a desgn alternatve s compared to others, t s possble to consder the exstence of a value functon V: P R, wth P = (P 1, P 2,..., P N ) the set of ndcators ncluded n the tree. The problem conssts of constructng a dmensonless value functon V(P), whch ntegratng all the crtera P, reflects the preferences of the decson makers. The soluton s a functon V, whch s the weghted sum of the N functons for value V correspondng wth the N ndcators. For a requrement tree wth three levels, the V functon takes the shape of Eq. (1): N (1) 1 V P V P V(P) measures the degree of sustanablty of the alternatve under assessment, wth respect to the set of ndcators P; α and β are the weghts of the requrements and crtera to whch each ndcator belongs to; γ are the weghts for the dfferent ndcators; V (P ) are the value functons used to measure the degree of sustanablty of the alternatve under study wth respect to a gven ndcator ; and N s the total number of ndcators taken nto account n the assessment. MIVES uses Eq. (2) as a bass for defnng each value functon V. V 1 e 1 e A P P,mn -m n A P,max P,mn -m n (2) In Eq. (2), P s the nput value of the ndcator for the alternatve under study, P, max and P, mn are, respectvely, the values of P assocated to the maxmum and mnmum levels of sustanablty (here 1 and 0). A, n and m are shape factors used to generate concave, convex, S-shaped or straght lne value functons. The dfferent geometres make t possble to establsh greater or lesser exgency when complyng wth the requstes for satsfyng a gven ndcator. The denomnator from Eq. (2) ensures that V returns values that fall wthn the nterval [0, 1]. Fg. 2 ncludes an example of value functon for an ndcator related to ecoponts of envronmental mpact that measures the dfferent knds of envronmental mpact, such as global warmng, depleton of the ozone layer, acdfcaton, among others. Step functons can be used for qualtatve varables. These are dscrete, stepped functons n whch each ter s assocated wth a possble stuaton or semantc label.

4 Conceptual Framework for an Integrated Method to Optmze Sustanablty of Engneerng Systems 611 SI (sustanablty ndex) Requrements Crtera Indcators 21.27% Cost of obtanng the fuel or raw materals % Mnng and extracton cost 8.59% Cost of preparng the fuel or raw materals % Pre-treatment and enrchment cost 28.01% Economc 11.22% Cost of transportng the fuel or raw materals 32.32% Investment cost 24.19% Operatng cost % Transportaton cost 25.00% Engneerng and cvl works cost 75.00% Process equpment cost 42.23% Cost of fuel and CO % Operaton and mantenance cost 2.41% Subsdes % Subsdes 20.11% Employment generaton % Employment generated 8.02% Populaton dsplacement caused by the project % Populaton dsplacement 39.30% Socal 8.02% Development of new areas % Development of new areas 61.17% Health and safety % Rsk of accdent 2.69% Vsual mpact % Vsual mpact 32.69% Envronmental 88.37% Envronmental mpact 4.86% Dscomfort assocated wth nose and odours % Ecoponts of envronmental mpact 60.00% Nose 40.00% Bad odours 6.77% Impact geographcal range % Local/regonal/global mpact Fg. 1 Example of a requrement tree. Fg. 2 Example of value functon for an ndcator related to ecoponts of envronmental mpact. In Phase D, the weghts α, β and γ wll be establshed for the varous branches on the requrement tree. In some cases, ths can be done drectly. In general, drectly allocatng weghts n branches wth up to four ndcators does not generate problems. Wth more than four, one often loses the overall vew and ths can lead to nconsstences, among other potental problems. It s a good dea to use AHP [9, 11, 12]. Ths technque helps to organze the process effcently, reduce ts complexty and subjectvty and decrease possble dsagreements between the team members. AHP s based on a parwse comparson of the relatve mportance for the varous branches radatng from the same pont of the tree. Phases C and D can be developed smultaneously. Phase E conssts of defnng dfferent desgn alternatves that wll be evaluated by means of the model. Those optons wll be assessed n Phase F, calculatng the sustanablty ndex for each one. Eq. (1) wll be appled for ths purpose. Fnally, n Phase G, decsons wll be made, and the desgners wll choose the best opton. Addtonal nformaton about MIVES can be found n Refs. [8, 9]. 3. Optmzng the Sustanablty of Engneerng Systems 3.1 Dealng wth Uncertanty n MIVES Models Uncertanty can affect specfc varables of engneerng systems, and so the sustanablty ndcators. Moreover, t could be dscrepances among the experts at the tme of establshng value functons and the weghts of the tree. MIVES s a determnstc method, so t does not allow one to consder the uncertanty that could affect the varables ncluded n sustanablty assessment models. It s necessary to combne MIVES wth a

5 612 Conceptual Framework for an Integrated Method to Optmze Sustanablty of Engneerng Systems technque capable of consderng the uncertanty. An opton s Monte Carlo smulaton [13]. The MIVES-Monte Carlo method s composed of nne phases. In Phase P1, the probablstc parameters of the MIVES model wll be dentfed. Indcators, weghts and value functon parameters could be treated as probablstc varables. It s recommended that, only those varables wth the greatest nfluence over the model and a hgh degree of uncertanty are establshed as probablstc. To ths end, a senstvty analyss can be performed on the determnstc MIVES model. In Phases P2 and P3, the determnstc (Phase P2) and probablstc (Phase P3) parameters of the model wll be estmated. Phase P2 usually does not cause problems because determnstc varables can be estmated usng expert judgement, hstorcal databases would be very helpful. On the contrary, there are no databases coverng the complete set of sustanablty ndcators used n ths knd of models, nor are there any databases related to weghts and value functons. Consequently, smple and easy to understand probablty dstrbutons should be used: open and closed trangular ones, unform dstrbutons, Bernoull and general dscrete functons [9], for the potental use of each type of dstrbuton. In Phases P4-P6, smulaton wll be performed. Accordng to the prevously defned dstrbuton functons, pseudo-random values wll be generated for every probablstc varable (Phase P4). PRNGs (pseudo-random number generators) and complementary technques wll be used for ths purpose, for nstance, the nverson (nverse transform method) and acceptance-rejecton technques can be employed [9]. There could be correlatons between the model ndcators. If possble, t s recommended to use analytcal formulae for modellng correlatons. When the correlatons are not known n an analytcal way, the technque called correlaton among samples could be used [9]. Eqs. (1) and (2) wll be appled n each teraton, to obtan a potental value for the fnal sustanablty ndex of the alternatve under study (Phase P5). Phases P4 and P5 wll be repeated untl convergence has been reached n the results (Phase P6). When a value functon s beng constructed, dscrepances about of ts geometry may appear among the experts. One opton, n Phase P3, s to defne two or more value functons for that ndcator. A specfc probablty wll then be assgned to each one of those functons. Another alternatve could be estmated dstrbuton functons for A, m and n, n Phase P3. Uncertanty or dsagreements may exst about the weghts γ, β and α. In the frst nstance, dscrepances could be solved usng conventonal AHP. In other nstance, or n case of uncertanty, dstrbuton functons can be establshed for the weghts. In Phase P7, a statstcal analyss of the output sample wll be performed. Ths means calculatng ts essental statstcal parameters (maxmum, mnmum, mode, typcal devaton, percentles, etc.), as well as the frequency hstogram and the curve of cumulatve probablty for the global sustanablty ndex. In Phase P8, the users must nterpret the statstcal analyss, and make the opportune decsons about the system s desgn. Fnally, Phase P9 s crucal for mprovng effectveness n subsequent applcatons of the method. Real, fnal data must be collected, to be used n future projects. Hstorcal databases wll make t possble to perfect the model and to estmate ts varables more effectvely, n the future. Addtonal nformaton about the MIVES-Monte Carlo method can be found n Ref. [9]. 3.2 The Models to be Optmzed, Potental Applcatons The potental models for optmzng complete energy systems (e.g., an ar condtonng system, a power plant) are excessvely complex for a frst work. For the moment, the applcatons should begn wth smpler models related to relatvely uncomplcated energy sub-systems, wth a lmted amount of desgn parameters.

6 Conceptual Framework for an Integrated Method to Optmze Sustanablty of Engneerng Systems 613 As an example, n ths secton, some deas about the optmzaton of a MIVES model appled to a STHE (shell-and-tube heat exchanger) are proposed. Nevertheless, the same concepts can be appled to other partal or complete energy systems. The prncpal components of a STHE are shell, shell cover, tubes, channel, channel cover, tubesheet, baffles and nozzles. Other components nclude te-rods and spacers, pass partton plates, mpngement plate, longtudnal baffle, sealng strps, supports and foundaton. When a STHE s beng desgned, certan requrements have to be met: flow rates, nlet and outlet temperatures, operatng pressure, allowable pressure drop, or heat duty, among others. Dependng on the STHE applcaton, the desgner may have more or less freedom durng the desgn process. However, normally there are several sutable desgn alternatves for a partcular applcaton. Tube sze, materal of constructon, shell dameter, number of tubes, heat transfer area, tube ptch, tube layout angle, tube layout patterns, baffle type and baffle spacng are desgn parameters chosen by the desgner. It s possble to create a mathematcal model ncludng all the parameters and formulas necessary for an adequate desgn of a STHE (STHE sub-model). A dstrbuton functon can be assgned to the opportune parameters. Specfc formulae must be ncorporated to the model n order to avod absurd desgns. In ths way, a rght STHE for the applcaton s ensured, and a vald STHE wll be generated n each teraton. A MIVES model can be also created for evaluatng the sustanablty ndex of the STHE (sustanablty sub-model). Ths model wll be made up of a set of envronmental, socal, economc and techncal ndcators (an approprate selecton of those presented n Table 1, among other sutable ndcators). The STHE and sustanablty sub-models must then be lnked, confgurng the complete model to be optmzed. Fg. 3 shows a conceptual graph of ths model. Each STHE desgn wll have ts own lfe-cycle energy and resources consumpton, ts own lfe-cycle CO 2, SO 2 and PO -3 4 emssons, among others. The opportune formulae wll be establshed for estmatng the sustanablty ndcators correspondng to each result of the STHE sub-model. These data wll be the nput values to the MIVES sub-model. Fnally, a conventonal or metaheurstcs smulaton process wll be performed, to fnd the combnaton of the STHE desgn parameters wth the hghest sustanablty ndex. 3.3 Optmzaton Technques Frequently MIVES models nclude dscrete varables, so t s not possble to apply the usual dervaton technques to obtan the maxmum of the set of mathematcal functons defnng that knd of models. Other knds of technques must be used [14]. The easest conceptual alternatve s to apply Monte Carlo or Latn Hypercube (stratfed Monte Carlo) technques to the model, whch smply carry out a random search n a smlar way than the one explaned here n Secton 3.1. After smulaton, the alternatve wth the hghest sustanablty ndex wll be dentfed. Snce Monte Carlo s an approxmate method, ths wll allow to obtan an exact (optmal) or, more frequently, approxmate (suboptmal) soluton to the problem. Metaheurstcs [14, 15] are also random exploraton algorthms, but they perform a smarter search for that optmal or suboptmal soluton, consequently shortenng the computatonal tme. Instead of generatng random numbers and watng to the end to dentfy the optmal, metaheurstcs draw conclusons from the ntermedate results obtaned durng the smulaton, n order to gude the search towards more promsng areas of the soluton space. There s a wde range of metaheurstc algorthms [14]. Each type of algorthm has dfferent characterstcs, and may not be sutable for some of the possble models to be optmzed. Each metaheurstc technque must also be adapted to the model, defnng the varous operators, parameters and crtera for effectvely

7 614 Conceptual Framework for an Integrated Method to Optmze Sustanablty of Engneerng Systems Smulaton process usng the most approprate technque IP Mathematcal model OP Indcators Crtera Requrements IP 1 OP 1 Indcator 1 Crteron 1 IP 2 Phy scal-mathematcal OP 2 Indcator 2 Env ronmental IP 3 formulaton OP 3 Indcator 3 Crteron 2 Socal IP 4 of the energy sy stem OP 4 Indcator 4 Crteron 3 Economc operaton Techncal-functonal IP N OP n Indcator M C rteron m SI Fg. 3 Conceptual graph representng the models to be optmzed. gudng the search. Genetc algorthms [16, 17], tabu search [18, 19] and smulated annealng [20, 21] may be sutable technques for ths purpose. 4. Conclusons Ths paper has proposed the man deas and technques to establsh a methodology for optmzng the sustanablty of engneerng systems, partcularly of energy systems. Snce the opportune MIVES models use to nclude dscrete varables, t s not possble to apply conventonal dervaton technques. Smulaton or metaheurstc technques must be used. For large engneerng systems metaheurstc algorthms wll be necessary, n order to shorten the computatonal tme. As far as the authors know, there are no studes on the most approprate optmzaton technques for the ntended purpose. Consequently, t wll be necessary to carry out an exploratory phase to select and confgure the most sutable metaheurstc technque. At present, t seems that, tabu search, smulated annealng and, above all, genetc algorthms may be sutable technques for the purpose explaned here. For the moment, the applcatons should begn wth relatvely uncomplcated energy sub-systems, wth a lmted amount of desgn parameters. Thus, correctons and valdaton wll be easer to be made, ncreasng the probablty of success n larger applcatons. References [1] Unted Natons Organzaton Ro Declaraton on Envronment and Development. Presented at the UNCED (Unted Natons Conference on Envronment and Development), Río of Janero, Brazl. Accessed January 16, nnex1.htm. [2] Afgan, N. H., and Carvalho, M. G Mult-crtera Assessment of New and Renewable Energy Power Plants. Energy 27 (August): [3] Kaya, T., and Kahraman, C Multcrtera Renewable Energy Plannng Usng an Integrated Fuzzy VIKOR & AHP Methodology: The Case of Istanbul. Energy 35 (June): [4] Kannan, R., Tso, C. P., Osman, R., and Ho, H. K LCA-LCCA of Ol Fred Steam Turbne Power Plant n Sngapore. Energy Converson and Management 45 (November): [5] Odeh, N. A., and Cockerll, T. T Lfe Cycle Analyss of UK Coal Fred Power Plants. Energy Converson and Management 49 (February): [6] Stoppato, A Lfe Cycle Assessment of Photovoltac Electrcty Generaton. Energy 33 (February): [7] Varun, B. I. K., and Prakash, R LCA of Renewable Energy for Electrcty Generaton Systems A Revew. Renewable and Sustanable Energy Revews 3 (June): [8] Gómez, D., del Caño, A., de la Cruz, M. P., and Josa, A.

8 Conceptual Framework for an Integrated Method to Optmze Sustanablty of Engneerng Systems Generc Methodology for the Assessment of Sustanablty of Constructon Systems. The MIVES Method. In Sustanablty and Constructon, edted by Aguado, A. Madrd: Spansh Structural Concrete Assocaton. [9] de la Cruz, M. P., Castro, A., del Caño, A., Gómez, D., Lara, M., and Cartelle, J. J Comprehensve Methods for Dealng wth Uncertanty n Assessng Sustanablty. Part I: the MIVES Monte Carlo Method. In Soft Computng Applcatons for Renewable Energy and Energy Effcency, edted by García-Cascales, M. S., Sánchez-Lozano, J. M., Masegosa, A. D., and Cruz-Corona, C. Hershey: IGI-Global. [10] Alarcón, B., Aguado, A., Manga, R., and Josa, A A Value Functon for Assessng Sustanablty: Applcaton to Industral Buldngs. Sustanablty 3 (January): [11] Saaty, T The Analytc Herarchy Process. New York: McGraw-Hll. [12] Saaty, T Fundamentals of Decson Makng and Prorty Theory wth the Analytc Herarchy Process. Pttsburg: RWS Publcatons. [13] Rpley, B. D Stochastc Smulaton. New York: Wley & Sons. [14] Floudas, C. A., and Pardalos, P. M Encyclopeda of Optmzaton. USA: Sprnger. [15] Rothlauf, F Desgn of Modern Heurstcs: Prncples and Applcaton. Manz: Sprnger. [16] Haupt, R. L., and Haupt, S. E Practcal Genetc Algorthms. Hoboken: Wley. [17] Aboshosha, A., and Khalyfa, Y Genetc Algorthms Theores and Applcatons. Saarbrücken: LAP Lambert. [18] Glover, F Tabu Search: Part I. ORSA J Comput 1 (February): [19] Glover, F Tabu Search: Part II. ORSA J Comput 2 (November): [20] Krkpatrck, S., Gelatt, C. D., and Vecch, M. P Optmzaton by Smulated Annealng. Scence 220 (May): [21] Dekkers, A., and Aarts, E Global Optmzaton and Smulated Annealng. Mathematcal Programmng 50 (March):