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A state of art review on applications of multi-objective evolutionary algorithms in chemicals production reactors

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Abstract

Chemical reactors are employed to produce several materials, which are utilized in numerous applications. The wide use of these chemical engineering units shows their importance as their performance vastly affects the production process. Thus, improving these units will develop the process and/or the manufactured material. Multi-objective optimization (MOO) with evolutionary algorithms (EA’s) has been used to solve several real world complex problems for improving the performance of chemical reactors with conflicting objectives. These objectives are of different nature as they could be economy, environment, safety, energy, exergy and/or process related. In this review, a brief description for MOO and EA’s and their several types and applications is given. Then, MOO studies, which are related to the materials’ production via chemical reactors, those were conducted with EA’s are classified into different classes and discussed. The studies were classified according to the produced material to hydrogen and synthesis gas, petrochemicals and hydrocarbons, biochemical, polymerization and other general processes. Finally, some guidelines are given to help in deciding on future research.

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References

  • Abido MA (2006) Multiobjective evolutionary algorithms for electric power dispatch problem. IEEE Trans Evolutionary Comput 10:315–329

    Article  Google Scholar 

  • Abraham A, Jain L (2005) Evolutionary multiobjective optimization, Evolutionary Multiobjective Optimization. Springer, pp 1–6

  • Adeyemo J, Stretch D (2018) Review of hybrid evolutionary algorithms for optimizing a reservoir. S Afr J Chem Eng 25:22–31

    Google Scholar 

  • Afshar-Nadjafi B, Razmi-Farooji A (2018) Using NSGA II and MOSA for solving multi-depots time-dependent vehicle routing problem with heterogeneous fleet. J Optim Industrial Eng 11:161–170

    Google Scholar 

  • Agarwal A, Gupta SK (2008) Jumping gene adaptations of NSGA-II and their use in the multi-objective optimal design of shell and tube heat exchangers. Chem Eng Res Des 86:123–139

    Article  Google Scholar 

  • Agarwal P, Manna U, Mukherjee D (2015) Stochastic Control of Tidal Dynamics Equation with Lévy Noise.Appl. Math. Optim.,1–70

  • Aghbashlo M, Hosseinpour S, Tabatabaei M, Rastegari H, Ghaziaskar HS (2019) Multi-objective exergoeconomic and exergoenvironmental optimization of continuous synthesis of solketal through glycerol ketalization with acetone in the presence of ethanol as co-solvent. Renew Energy 130:735–748

    Article  Google Scholar 

  • Aghbashlo M, Tabatabaei M, Hosseinpour S, Rastegari H, Ghaziaskar HS (2018) Multi-objective exergy-based optimization of continuous glycerol ketalization to synthesize solketal as a biodiesel additive in subcritical acetone. Energy Convers Manage 160:251–261

    Article  Google Scholar 

  • Agrawal N, Rangaiah G, Ray AK, Gupta SK (2007) Design stage optimization of an industrial low-density polyethylene tubular reactor for multiple objectives using NSGA-II and its jumping gene adaptations. Chem Eng Sci 62:2346–2365

    Article  Google Scholar 

  • Agrawal N, Rangaiah GP, Ray AK, Gupta SK (2006) Multi-objective Optimization of the Operation of an Industrial Low-Density Polyethylene Tubular Reactor Using Genetic Algorithm and Its Jumping Gene Adaptations. Ind Eng Chem Res 45:3182–3199

    Article  Google Scholar 

  • Ahmadi P, Dincer I, Rosen MA (2013) Thermodynamic modeling and multi-objective evolutionary-based optimization of a new multigeneration energy system. Energy Convers Manage 76:282–300

    Article  Google Scholar 

  • Ahmed MZ, Padhiyar N (2020) Multi objective optimization of a tri-reforming process with the maximization of H2 production and minimization of CO2 emission & power loss. Int J Hydrog Energy 45:22480–22491

    Article  Google Scholar 

  • Al-Siyabi B, Gujarathi AM, Sivakumar N (2017) Harmonic multi-objective differential evolution approach for multi-objective optimization of fed-batch bioreactor. Mater Manuf Processes 32:1152–1161

    Article  Google Scholar 

  • Al-Zareer M, Dincer I, Rosen MA (2018) Multi-objective optimization of an integrated gasification combined cycle for hydrogen and electricity production. Comput Chem Eng 117:256–267

    Article  Google Scholar 

  • Al Ani Z, Gujarathi AM, vakili-Nezhaad GR (2021a) Hybridized multi-objective optimization approach (HMODE) for lysine fed-batch fermentation process. Korean Journal of Chemical Engineering Accepted

  • Al Ani Z, Gujarathi AM, Vakili-Nezhaad GR (2021b) Simultaneous energy and environment-based optimization and retrofit of TEG dehydration process: An industrial case study. Process Saf Environ Prot 147:972–984

    Article  Google Scholar 

  • Al Ani Z, Gujarathi AM, Vakili-Nezhaad GR, Al Wahaibi T (2021c) Evolutionary multi-criteria optimization aspects for sulfuric acid plant toward more economic, environmentally friendly and efficient process. Chem Pap 75:3649–3666

    Article  Google Scholar 

  • Al Ani Z, Gujarathi AM, Vakili-Nezhaad GR, Triki C, Wahaibi A, T (2020a) Simultaneous Multicriteria-Based Optimization Trends in Industrial Cases. Applied Mathematics and Chemo-Mechanical Analysis, Materials Physics and Chemistry, p 175

    Google Scholar 

  • Al Ani Z, Thafseer M, Gujarathi AM, Vakili-Nezhaad GR (2020b) Towards process, energy and safety based criteria for multi-objective optimization of industrial acid gas removal process. Process Saf Environ Prot 140:86–99

    Article  Google Scholar 

  • Alavi M, Eslamloueyan R, Rahimpour MR (2018) Multi objective optimization of a methane steam reforming reaction in a membrane reactor: considering the potential catalyst deactivation due to the hydrogen removal.International Journal of Chemical Reactor Engineering16

  • Albarelli JQ, Onorati S, Caliandro P, Peduzzi E, Meireles MAA, Marechal F, Ensinas AV (2017) Multi-objective optimization of a sugarcane biorefinery for integrated ethanol and methanol production. Energy 138:1281–1290

    Article  Google Scholar 

  • Allmendinger R, Simaria AS, Farid SS (2014) Multiobjective evolutionary optimization in antibody purification process design. Biochem Eng J 91:250–264

    Article  Google Scholar 

  • Amin S, Dayal P, Padhiyar N (2020) Process design of batch reactors using multi-objective optimization for synthesis of butylated urea formaldehyde resins. Comput Chem Eng 140:106892

    Article  Google Scholar 

  • Amouzgar K (2012)Multi-objective optimization using genetic algorithms

  • Angeline PJ (1998) Using selection to improve particle swarm optimization, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360). IEEE, pp. 84–89

  • Arora P, Hoadley AFA, Mahajani SM, Ganesh A (2017) Multi-objective optimization of biomass based ammonia production - Potential and perspective in different countries. J Clean Prod 148:363–374

    Article  Google Scholar 

  • Asgari A, Yari M, Mahmoudi SMS (2021) Exergy and exergoeconomic analyses and multi-objective optimization of a novel cogeneration system for hydrogen and cooling production. International Journal of Hydrogen Energy

  • Awad M, Dalkilic A, Wongwises S (2014) A critical review on condensation heat transfer in microchannels and minichannels. J Nanotechnol Eng Med 5:010904

    Article  Google Scholar 

  • Azzaro-Pantel C, Ouattara A, Pibouleau L (2013) Ecodesign of Chemical Processes with Multi‐Objective Genetic Algorithms. Multi‐Objective Optimization in Chemical Engineering: Developments and Applications, 335–367

  • Babu B, Anbarasu B (2005) Multi-objective differential evolution (MODE): an evolutionary algorithm for multi-objective optimization problems (MOOPs), Proceedings of International Symposium and 58th Annual Session of IIChE

  • Babu B, Chakole PG, Mubeen JS (2005) Multiobjective differential evolution (MODE) for optimization of adiabatic styrene reactor. Chem Eng Sci 60:4822–4837

    Article  Google Scholar 

  • Babu B, Gujarathi AM, Katla P, Laxmi V (2007a) Strategies of multi-objective differential evolution (MODE) for optimization of adiabatic styrene reactor, Proceedings of the international conference on emerging mechanical technology: macro to nano (EMTMN-2007). Citeseer, p. 243

  • Babu BV, Mubeen JHS, Chakole PG (2007b) Simulation and Optimization of Wiped-Film Poly-Ethylene Terephthalate (PET) Reactor Using Multiobjective Differential Evolution (MODE). Mater Manuf Processes 22:541–552

    Article  Google Scholar 

  • Bäck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford university press

  • Bäck T, Fogel DB, Michalewicz Z (1997a) Handbook of evolutionary computation. CRC Press

  • Bäck T, Hammel U, Schwefel H-P (1997b) Evolutionary computation: Comments on the history and current state. IEEE Trans Evolut Comput 1:3–17

    Article  Google Scholar 

  • Bäck T, Schwefel H-P (1993) An overview of evolutionary algorithms for parameter optimization. Evol Comput 1:1–23

    Article  Google Scholar 

  • Bahari A, Atashkari K, Mahmoudimehr J (2020) Multi-objective optimization of a municipal solid waste gasifier.Biomass Conversion and Biorefinery,1–16

  • Bakhshi Ani A, Ale Ebrahim H (2021) Modeling and Multi-objective Optimization of a Packed Bed Reactor for Sulfur Dioxide Removal by Magnesium Oxide Using Non-dominated Sorting Genetic Algorithm II. Chem Biochem Eng Q 35:251–266

    Google Scholar 

  • Bakhshi Ani A, Ebrahim A, Azarhoosh H, M.J (2015) Simulation and multi-objective optimization of a trickle-bed reactor for diesel hydrotreating by a heterogeneous model using non-dominated sorting genetic algorithm II. Energy Fuels 29:3041–3051

    Article  Google Scholar 

  • Banos R, Manzano-Agugliaro F, Montoya F, Gil C, Alcayde A, Gómez J (2011) Optimization methods applied to renewable and sustainable energy: A review. Renew Sustainable Energy Rev 15:1753–1766

    Article  Google Scholar 

  • Bayat M, Asil AG (2021) Robust multi-objective optimization of methanol steam reforming for boosting hydrogen production. Int J Hydrog Energy 46:29795–29811

    Article  Google Scholar 

  • Bayat M, Dehghani Z, Hamidi M, Rahimpour MR (2014a) Methanol synthesis via sorption-enhanced reaction process: Modeling and multi-objective optimization. J Taiwan Inst Chem Eng 45:481–494

    Article  Google Scholar 

  • Bayat M, Dehghani Z, Rahimpour MR (2014b) Dynamic multi-objective optimization of industrial radial-flow fixed-bed reactor of heavy paraffin dehydrogenation in LAB plant using NSGA-II method. J Taiwan Inst Chem Eng 45:1474–1484

    Article  Google Scholar 

  • Bazmi AA, Zahedi G (2011) Sustainable energy systems: Role of optimization modeling techniques in power generation and supply—A review. Renew Sustainable Energy Rev 15:3480–3500

    Article  Google Scholar 

  • Behroozsarand A, Ebrahimi H, Zamaniyan A (2009) Multiobjective optimization of industrial autothermal reformer for syngas production using nonsorting genetic algorithm II. Ind Eng Chem Res 48:7529–7539

    Article  Google Scholar 

  • Benyahia B, Latifi MA, Fonteix C, Pla F (2011) Multicriteria dynamic optimization of an emulsion copolymerization reactor. Comput Chem Eng 35:2886–2895

    Article  Google Scholar 

  • Beveridge G, Schechter R Optimization: Theory and practice,(1970). Google Scholar

  • Bhaskar V, Gupta SK, Ray AK (2001) Multiobjective optimization of an industrial wiped film poly(ethylene terephthalate) reactor: some further insights. Comput Chem Eng 25:391–407

    Article  Google Scholar 

  • Bhutani N, Ray AK, Rangaiah GP (2006) Modeling, Simulation, and Multi-objective Optimization of an Industrial Hydrocracking Unit. Ind Eng Chem Res 45:1354–1372

    Article  Google Scholar 

  • Bouveret G, Chassagneux J-F (2017) A comparison principle for PDEs arising in approximate hedging problems: application to Bermudan options.Appl. Math. Optim.,1–23

  • Boyd S, Boyd SP, Vandenberghe L (2004) Convex optimization. Cambridge university press

  • Camargo M, Morel L, Fonteix C, Hoppe S, Hu GH, Renaud J (2011) Development of new concepts for the control of polymerization processes: Multiobjective optimization and decision engineering. II. Application of a Choquet integral to an emulsion copolymerization process. J Appl Polym Sci 120:3421–3434

    Article  Google Scholar 

  • Cao Y, Dhahad HA, Togun H, Anqi AE, Farouk N, Farhang B (2021a) A novel hybrid biomass-solar driven triple combined power cycle integrated with hydrogen production: Multi-objective optimization based on power cost and CO2 emission. Energy Convers Manage 234:113910

    Article  Google Scholar 

  • Cao Y, Dhahad HA, Togun H, Hussen HM, Anqi AE, Farouk N, Issakhov A (2021b) Exergy, exergoeconomic and multi-objective optimization of a clean hydrogen and electricity production using geothermal-driven energy systems. International Journal of Hydrogen Energy

  • Carmona R, Delarue F, Lacker D (2017) Mean field games of timing and models for bank runs. Appl Math Optim 76:217–260

    Article  MathSciNet  MATH  Google Scholar 

  • Cartwright H, Gillet V, Habershon S, Harris K, Hartke B, Unger R, Woodley S (2004) Applications of evolutionary computation in chemistry. Springer Science & Business Media

  • Chaki S, Bathe RN, Ghosal S, Padmanabham G (2018) Multi-objective optimisation of pulsed Nd: YAG laser cutting process using integrated ANN–NSGAII model. J Intell Manuf 29:175–190

    Article  Google Scholar 

  • Chakraborti N, Mishra P, Aggarwal A, Banerjee A, Mukherjee SS (2006) The Williams and Otto Chemical Plant re-evaluated using a Pareto-optimal formulation aided by Genetic Algorithms. Appl Soft Comput 6:189–197

    Article  Google Scholar 

  • Chang H, Chen Y-H, Chen Y-T, Ho C-D (2018) Multi-objective Optimization of Mixed Membrane Reactors for Autothermal Reforming of Methane. Journal of Applied Science and Engineering 21, 485г495

  • Chang T, Lu J, Shen Z, Huang Y, Lu D, Wang X, Cao J, Morent R (2019) Simulation and optimization of the post plasma-catalytic system for toluene degradation by a hybrid ANN and NSGA-II method. Appl Catal B 244:107–119

    Article  Google Scholar 

  • Chase N, Redemacher M, Goodman E, Averill R, Sidhu R (2010) A benchmark study of optimization search algorithms. Red Cedar Technology, MI, USA, pp 1–15

    Google Scholar 

  • Chaudhari P, Gupta SK (2012) Multiobjective Optimization of a Fixed Bed Maleic Anhydride Reactor Using an Improved Biomimetic Adaptation of NSGA-II. Ind Eng Chem Res 51:3279–3294

    Article  Google Scholar 

  • Chaudhari P, Thakur AK, Kumar R, Banerjee N, Kumar A (2021) Comparison of NSGA-III with NSGA-II for multi objective optimization of adiabatic styrene reactor. Materials Today: Proceedings

  • Chen H, Hofbauer P, Longtin JP (2020) Multi-objective optimization of a free-piston Vuilleumier heat pump using a genetic algorithm. Appl Therm Eng 167:114767

    Article  Google Scholar 

  • Chen X, Du W, Qian F (2014) Multi-objective differential evolution with ranking-based mutation operator and its application in chemical process optimization. Chemom Intell Lab Syst 136:85–96

    Article  Google Scholar 

  • Chen Z, Shi G, Zhou M (2005) Analysis and optimization of the picking up medicine queuing system for a medical warehouse-a case study of Shenzhen Jianhua Medical Company, Services Systems and Services Management, 2005. Proceedings of ICSSSM’05. 2005 International Conference on. IEEE, pp. 297–301

  • Cheng S-H, Chen H-J, Chang H, Chang C-K, Chen Y-M (2008) Multi-objective optimization for two catalytic membrane reactors—Methanol synthesis and hydrogen production. Chem Eng Sci 63:1428–1437

    Article  Google Scholar 

  • Chevreux B (1997) Genetische Algorithmen zur Molekülstrukturoptimierung. Universität Heidelberg/Fachhochschule Heilbronn/Deutsches Krebsforschungszentrum Heidelberg

  • Chmielecki J, Foo J, Oxnard GR, Hutchinson K, Ohashi K, Somwar R, Wang L, Amato KR, Arcila M, Sos ML (2011) Optimization of dosing for EGFR-mutant non–small cell lung cancer with evolutionary cancer modeling. Sci. Transl. Med. 3, 90ra59-90ra59

  • Chua WJ, Rangaiah GP, Hidajat K (2017) Design and optimization of isopropanol process based on two alternatives for reactive distillation. Chem Eng Process 118:108–116

    Article  Google Scholar 

  • Chua WX, da Cunha S, Rangaiah GP, Hidajat K (2019) Design and optimization of Kemira-Leonard process for formic acid production. Chem Eng Science: X 2:100021

    Google Scholar 

  • Clark DE (2000) Evolutionary Algorithms in Molecular Design, volume 8 of Methods and Principles in Medicinal Chemistry. Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany

  • Coello CAC (1999a) A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowl Inf Syst 1:269–308

    Article  Google Scholar 

  • Coello CAC (1999b) An updated survey of evolutionary multiobjective optimization techniques: State of the art and future trends, Proceedings of the Congress on Evolutionary Computation. IEEE Press Piscataway, NJ, pp. 3–13

  • Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Method Appl M 191:1245–1287

    Article  MathSciNet  MATH  Google Scholar 

  • Coello CAC (2009) Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored. Front Comput Sci 3:18–30

    Article  Google Scholar 

  • Coello CAC (2020)Statistics of the EMOO Repository

  • Coello CAC, Lamont GB, Van V (2002) Evolutionary algorithms for solving multi-objective problems, vol 800. Springer

  • Coello CAC, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems. Springer

  • Coello CAC, Lechuga MS (2002) MOPSO: A proposal for multiple objective particle swarm optimization, Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No. 02TH8600). IEEE, pp. 1051–1056

  • Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evolut Comput 8:256–279

    Article  Google Scholar 

  • Cong D, Shi C, Cui Y, Han Y, Geng Z (2021) Novel competing evolutionary membrane algorithm based on multiple reference points for multi-objective optimization of ethylene cracking processes. Chemom Intell Lab Syst 217:104389

    Article  Google Scholar 

  • Contreras-Zarazúa G, Vázquez-Castillo JA, Ramírez-Márquez C, Segovia-Hernández JG, Alcántara-Ávila JR (2017) Multi-objective optimization involving cost and control properties in reactive distillation processes to produce diphenyl carbonate. Comput Chem Eng 105:185–196

    Article  Google Scholar 

  • Corne D, Dorigo M, Glover F, Dasgupta D, Moscato P, Poli R, Price KV (1999) New ideas in optimization. McGraw-Hill Ltd., UK

    Google Scholar 

  • Corne DW, Knowles JD, Oates MJ (2000) The Pareto envelope-based selection algorithm for multiobjective optimization, International conference on parallel problem solving from nature. Springer, pp. 839–848

  • Costa CBB, Potrich E, Cruz AJG (2016) Multiobjective optimization of a sugarcane biorefinery involving process and environmental aspects. Renew Energy 96:1142–1152

    Article  Google Scholar 

  • Cuadros JF, Melo DNC, Nascimento NM, Filho RM, Maciel W, M.R (2013) A hybrid GA-SQP multi-objective optimization methodology for carbon monoxide pollution minimization in Fluid Catalytic Cracking Process. In: Kraslawski A, Turunen I (eds) Comput. Aided Chem. Eng. Elsevier, pp 763–768

  • Cui Y, Geng Z, Zhu Q, Han Y (2017) Review: Multi-objective optimization methods and application in energy saving. Energy 125, 681–704

  • Cunha MdC, Sousa J (1999) Water distribution network design optimization: simulated annealing approach. J Water Res Plan Man 125:215–221

    Article  Google Scholar 

  • da Cunha S, Rangaiah GP, Hidajat K (2018a) Design, optimization, and retrofit of the formic acid process I: Base case design and dividing-wall column retrofit. Ind Eng Chem Res 57:9554–9570

    Article  Google Scholar 

  • da Cunha S, Rangaiah GP, Hidajat K (2018b) Design, Optimization, and Retrofit of the Formic Acid Process II: Reactive Distillation and Reactive Dividing-Wall Column Retrofits. Ind Eng Chem Res 57:14665–14679

    Article  Google Scholar 

  • Damsbo M, Kinnear BS, Hartings MR, Ruhoff PT, Jarrold MF, Ratner MA (2004) Application of evolutionary algorithm methods to polypeptide folding: Comparison with experimental results for unsolvated Ac-(Ala-Gly-Gly) 5-LysH+. Proc. Natl. Acad. Sci. 101, 7215–7222

  • Darvishi A, Bakhtyari A, Rahimpour MR (2018) A sensitivity analysis and multi-objective optimization to enhance ethylene production by oxidative dehydrogenation of ethane in a membrane-assisted reactor. Chin J Chem Eng 26:1879–1895

    Article  Google Scholar 

  • Das S, Suganthan PN (2011) Differential Evolution: A Survey of the State-of-the-Art. IEEE Trans Evolut Comput 15:4–31

    Article  Google Scholar 

  • Deb K (2002) Multi-objective optimization using evolutionary algorithms. John Wiley & Sons, England

    MATH  Google Scholar 

  • Deb K (2014) Multi-objective optimization, Search methodologies. Springer, pp 403–449

  • Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, International Conference on Parallel Problem Solving From Nature. Springer, pp. 849–858

  • Deb K, Mitra K, Dewri R, Majumdar S (2004) Towards a better understanding of the epoxy-polymerization process using multi-objective evolutionary computation. Chem Eng Sci 59:4261–4277

    Article  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6:182–197

    Article  Google Scholar 

  • Dehghani Z, Rahimpour MR, Shariati A (2021) Simulation and multi-objective optimization of a radial flow gas-cooled membrane reactor, considering reduction of CO2 emissions in methanol synthesis. J Environ Chem Eng 9:104910

    Article  Google Scholar 

  • Detchusananard T, Sharma S, Maréchal F, Arpornwichanop A (2019) Multi-objective optimization of sorption enhanced steam biomass gasification with solid oxide fuel cell. Energy Convers Manage 182:412–429

    Article  Google Scholar 

  • Di Nicola G, Moglie M, Pacetti M, Santori G (2010) Bioenergy II: Modeling and multi-objective optimization of different biodiesel production processes.International Journal of Chemical Reactor Engineering8

  • Dietz A, Aguilar-Lasserre A, Azzaro-Pantel C, Pibouleau L, Domenech S (2008a) A fuzzy multiobjective algorithm for multiproduct batch plant: Application to protein production. Comput Chem Eng 32:292–306

    Article  Google Scholar 

  • Dietz A, Azzaro-Pantel C, Pibouleau L, Domenech S (2006) Multiobjective optimization for multiproduct batch plant design under economic and environmental considerations. Comput Chem Eng 30:599–613

    Article  Google Scholar 

  • Dietz A, Azzaro-Pantel C, Pibouleau L, Domenech S (2007a) Optimal design of batch plants under economic and ecological considerations: Application to a biochemical batch plant. Math Comput Model 46:109–123

    Article  Google Scholar 

  • Dietz A, Azzaro-Pantel C, Pibouleau L, Domenech S (2008b) Strategies for multiobjective genetic algorithm development: Application to optimal batch plant design in process systems engineering. Comput Ind Eng 54:539–569

    Article  Google Scholar 

  • Dietz A, Pantel CA, Pibouleau LG, Domenech S (2007b) Ecodesign of batch processes: Optimal design strategies for economic and ecological bioprocesses.International Journal of Chemical Reactor Engineering5

  • Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evolut Comput 1:53–66

    Article  Google Scholar 

  • Eberhart R, Kennedy J (1995) Particle swarm optimization, Proceedings of the IEEE international conference on neural networks. Citeseer, pp. 1942–1948

  • Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer

  • Eiben AE, Smith JE (2015) What is an evolutionary algorithm?, Introduction to Evolutionary Computing. Springer, pp 25–48

  • Faghihi EM, Shamekhi AH (2010) Development of a neural network model for selective catalytic reduction (SCR) catalytic converter and ammonia dosing optimization using multi objective genetic algorithm. Chem Eng J 165:508–516

    Article  Google Scholar 

  • Falcone MA, Lopes HS, dos Santos Coelho L (2008) Supply chain optimisation using evolutionary algorithms. Int J Comput Appl Tech 31:158–167

    Article  Google Scholar 

  • Fan G, Yang B, Guo P, Lin S, Farkoush SG, Afshar N (2021) Comprehensive analysis and multi-objective optimization of a power and hydrogen production system based on a combination of flash-binary geothermal and PEM electrolyzer. Int J Hydrog Energy 46:33718–33737

    Article  Google Scholar 

  • Fan Q, Wang W, Yan X (2017) Multi-objective differential evolution with performance-metric-based self-adaptive mutation operator for chemical and biochemical dynamic optimization problems. Appl Soft Comput 59:33–44

    Article  Google Scholar 

  • Farsi A, Dincer I, Naterer GF (2020) Multi-objective optimization of an experimental integrated thermochemical cycle of hydrogen production with an artificial neural network. Int J Hydrog Energy 45:24355–24369

    Article  Google Scholar 

  • Farsi M, Jowkari H, Doust AI (2016) Multi–objective optimization approach to enhance ethylbenzene dehydrogenation in the multi-stage spherical reactors. Periodica Polytech Chem Eng 60:201–209

    Article  Google Scholar 

  • Farsi M, Shahhosseini H (2015) A modified membrane SMR reactor to produce large-scale syngas: modeling and multi objective optimization. Chem Eng Process 97:169–179

    Article  Google Scholar 

  • Fettaka S, Gupta YP, Thibault J (2012) Multiobjective optimization of an industrial styrene reactor using the dual population evolutionary algorithm (DPEA).International Journal of Chemical Reactor Engineering10

  • Flegiel F, Sharma S, Rangaiah GP (2014) Development and Multiobjective Optimization of Improved Cumene Production Processes. Mater Manuf Processes 30:444–457

    Article  Google Scholar 

  • Fogel GB, Corne DW (2002) Evolutionary computation in bioinformatics. Elsevier

  • Fogel LJ, Owens AJ, Walsh MJ (1966) Intelligent decision making through a simulation of evolution. Behav Sci 11:253–272

    Article  Google Scholar 

  • Fonseca CM, Fleming PJ (1993) Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization, Icga. Citeseer, pp. 416–423

  • Fonseca CM, Fleming PJ (1998) Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation. IEEE T Syst Man Cy A 28:26–37

    Article  Google Scholar 

  • Fuad MNM, Hussain MA (2015) Systematic design of chemical reactors with multiple stages via multi-objective optimization approach, Comput. Aided Chem. Eng. Elsevier, pp. 869–874

  • Furtuna R, Curteanu S, Leon F (2011a) An elitist non-dominated sorting genetic algorithm enhanced with a neural network applied to the multi-objective optimization of a polysiloxane synthesis process. Eng Appl Artif Intell 24:772–785

    Article  Google Scholar 

  • Furtuna R, Curteanu S, Racles C (2011b) NSGA-II-RJG applied to multi-objective optimization of polymeric nanoparticles synthesis with silicone surfactants. Open Chem 9:1080–1095

    Article  Google Scholar 

  • Gandibleux X, Ehrgott M (2005) 1984-2004-20 years of multiobjective metaheuristics. but what about the solution of combinatorial problems with multiple objectives?, International Conference on Evolutionary Multi-Criterion Optimization. Springer, pp. 33–46

  • Ganesan T, Elamvazuthi I, Shaari Ku, Vasant KZ, P (2013) Swarm intelligence and gravitational search algorithm for multi-objective optimization of synthesis gas production. Appl Energy 103:368–374

    Article  Google Scholar 

  • Ganesan T, Vasant P, Elamvazuthi I, Shaari Ku (2012) K.Z., Swarm intelligence for multi-objective optimization of synthesis gas production, AIP Conference Proceedings. American Institute of Physics, pp. 317–324

  • Gao X, Chen B, He X, Qiu T, Li J, Wang C, Zhang L (2008) Multi-objective optimization for the periodic operation of the naphtha pyrolysis process using a new parallel hybrid algorithm combining NSGA-II with SQP. Comput Chem Eng 32:2801–2811

    Article  Google Scholar 

  • Garg H, Sharma S (2011) Multi-objective optimization of crystallization unit in a fertilizer plant using particle swarm optimization. Int J Appl Sci Eng 9:261–276

    Google Scholar 

  • Garg S, Gupta SK (1999) Multiobjective optimization of a free radical bulk polymerization reactor using genetic algorithm. Macromol Ther Simul 8:46–53

    Article  Google Scholar 

  • Gen M, Cheng R (1996) Genetic Algorithms and Manufacturing Systems Design. John Wiley & Sons, Inc.

  • Gen M, Lin L (2007) Genetic algorithms. Wiley Encyclopedia of Computer Science and Engineering, pp 1–15

  • Geng Z, Wang Z, Zhu Q, Han Y (2016) Multi-objective operation optimization of ethylene cracking furnace based on AMOPSO algorithm. Chem Eng Sci 153:21–33

    Article  Google Scholar 

  • Ghahraloud H, Farsi M (2017) Modeling and optimization of methanol oxidation over metal oxide catalyst in an industrial fixed bed reactor. J Taiwan Inst Chem Eng 81:95–103

    Article  Google Scholar 

  • Gong M, Jiao L, Yang D, Ma W (2009) Research on evolutionary multi-objective optimization algorithms. J Syst Softw 20:271–289

    MathSciNet  MATH  Google Scholar 

  • Gonzalez TF (2007) Handbook of approximation algorithms and metaheuristics. Chapman and Hall/CRC

  • Gu J, Li G, Gan N (2017) Hybrid metamodel-based design space management method for expensive problems. Eng Optimiz 49:1573–1588

    Article  MathSciNet  Google Scholar 

  • Gujarathi A, Babu B (2009a) Improved multiobjective differential evolution (MODE) approach for purified terephthalic acid (PTA) oxidation process. Mater Manuf Processes 24:303–319

    Article  Google Scholar 

  • Gujarathi AM, Al-Siyabi B, Sivakumar N, Mathew M (2017) Multi-objective Optimization of Fed-Batch Bioreactor Towards Lysine Production, ICTEA: International Conference on Thermal Engineering

  • Gujarathi AM, Babu B (2009b) Optimization of adiabatic styrene reactor: a hybrid multiobjective differential evolution (H-MODE) approach. Ind Eng Chem Res 48:11115–11132

    Article  Google Scholar 

  • Gujarathi AM, Babu B (2010a) Hybrid multi-objective differential evolution (H-MODE) for optimisation of polyethylene terephthalate (PET) reactor. Int J Bio-Inspir Com 2:213–221

    Article  Google Scholar 

  • Gujarathi AM, Babu B (2016) Evolutionary computation: techniques and applications. CRC Press, New York

    Book  MATH  Google Scholar 

  • Gujarathi AM, Babu BV (2010b) Multi-objective optimization of industrial styrene reactor: Adiabatic and pseudo-isothermal operation. Chem Eng Sci 65:2009–2026

    Article  Google Scholar 

  • Gujarathi AM, Babu BV (2011) Multiobjective optimization of Industrial processes using elitist multiobjective differential evolution (Elitist-MODE). Mater Manuf Processes 26:455–463

    Article  Google Scholar 

  • Gujarathi AM, Motagamwala AH, Babu B (2013) Multiobjective optimization of industrial naphtha cracker for production of ethylene and propylene. Mater Manuf Processes 28:803–810

    Article  Google Scholar 

  • Gujarathi AM, Sadaphal A, Bathe GA (2015) Multi-objective optimization of solid state fermentation process. Mater Manuf Processes 30:511–519

    Article  Google Scholar 

  • Guo Z, Yan X (2018) Optimization of the p-xylene oxidation process by a multi-objective differential evolution algorithm with adaptive parameters co-derived with the population-based incremental learning algorithm. Eng Optimiz 50:716–731

    Article  MATH  Google Scholar 

  • Guzmán Martínez C, Nápoles Rivera F, Castro-Montoya A (2021) Multi-objective optimization of bioethanol reactive dehydration processes using genetic algorithms. Sep Sci Technol 56:3167–3182

    Article  Google Scholar 

  • Hafyan RH, Bhullar L, Putra ZA, Bilad MR, Wirzal MDH, Nordin NAHM (2020) Multi-objective Sustainability Assessment of Levulinic Acid Production from Empty Fruit Bunch. Process Integr Optim Sustain 4:37–50

    Article  Google Scholar 

  • Halsall-Whitney H, Thibault J (2006) Multi-objective optimization for chemical processes and controller design: Approximating and classifying the Pareto domain. Comput Chem Eng 30:1155–1168

    Article  Google Scholar 

  • Han H, Yu R, Li B, Zhang Y, Wang W, Chen X (2019) Multi-objective optimization of corrugated tube with loose-fit twisted tape using RSM and NSGA-II. Int J Heat Mass Transfer 131:781–794

    Article  Google Scholar 

  • Hanoon AN, Jaafar M, Hejazi F, Aziz A, F.N (2017) Energy absorption evaluation of reinforced concrete beams under various loading rates based on particle swarm optimization technique. Eng Optimiz 49:1483–1501

    Article  Google Scholar 

  • Harode H, Ramteke M (2017) Axial dispersion modeling of industrial hydrocracking unit and its multiobjective optimization. Chem Eng Res Des 121:57–68

    Article  Google Scholar 

  • Haupt S (2004) Practical genetic algorithms. State College. John Wiley &Song, inc. publication, Pennsylvania, pp 123–190

    Google Scholar 

  • Henderson D, Jacobson S, Johnson A (2003) Handbook of metaheuristics, Volume 57 of International Series in Operations Research & Management Science, Chapter The Theory and Practice of Simulated Annealing, 287–319. Springer

  • Heravi M, Bayat M, Rahimpour MR (2016) Simultaneous high hydrogen content-synthesis gas production and in-situ CO2 removal via sorption-enhanced reaction process: modeling, sensitivity analysis and multi-objective optimization using NSGA-II algorithm. Iran J Chem Eng (IJChE) 13:71–95

    Google Scholar 

  • Hernández-Melchor DJ, Camacho-Pérez B, Ríos-Leal E, Alarcón-Bonilla J, López-Pérez PA (2020) Modelling and multi-objective optimization for simulation of hydrogen production using a photosynthetic consortium. International Journal of Chemical Reactor Engineering 18

  • Hernandez-Perez LG, Alsuhaibani AS, Radwan N, El-Halwagi MM, Ponce-Ortega JM (2020) Structural and Operating Optimization of the Methanol Process Using a Metaheuristic Technique. ACS Sustainable Chem. Eng

  • Hernández-Pérez LGn, Sánchez-Tuirán E, Ojeda KA, El-Halwagi MM, Ponce-Ortega JM (2019) Optimization of Microalgae-to-Biodiesel Production Process Using a Metaheuristic Technique. ACS Sustainable Chem Eng 7:8490–8498

    Article  Google Scholar 

  • Holland J (1975) Adaptation in natural and artificial systems: an introductory analysis with application to biology. Control and artificial intelligence

  • Holland J, Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Massachusetts

    Google Scholar 

  • Horn J, Nafpliotis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multiobjective optimization, Proceedings of the first IEEE conference on evolutionary computation, IEEE world congress on computational intelligence. Citeseer, pp. 82–87

  • Hosseinpour S, Aghbashlo M, Tabatabaei M, Younesi H, Mehrpooya M, Ramakrishna S (2017) Multi-objective exergy-based optimization of a continuous photobioreactor applied to produce hydrogen using a novel combination of soft computing techniques. Int J Hydrog Energy 42:8518–8529

    Article  Google Scholar 

  • Hsu K-C, Wang F-S (2017) Model-based optimization approaches for precision medicine: a case study in presynaptic dopamine overactivity.PloS one12, e0179575

  • Huang E, Zhang X, Rodriguez L, Khanna M, de Jong S, Ting KC, Ying Y, Lin T (2019a) Multi-objective optimization for sustainable renewable jet fuel production: A case study of corn stover based supply chain system in Midwestern U.S. Renew Sustainable Energy Rev 115:109403

    Article  Google Scholar 

  • Huang Z, Xie Z, Zhang C, Chan SH, Milewski J, Xie Y, Yang Y, Hu X (2019b) Modeling and multi-objective optimization of a stand-alone PV-hydrogen-retired EV battery hybrid energy system. Energy Convers Manage 181:80–92

    Article  Google Scholar 

  • Huy NQ, Van Tuyen N (2017) New second-order Karush–Kuhn–Tucker optimality conditions for vector optimization.Appl. Math. Optim.,1–29

  • Ishaq H, Dincer I (2019) Multi-objective optimization and analysis of a solar energy driven steam and autothermal combined reforming system with natural gas. J Nat Gas Sci Eng 69:102927

    Article  Google Scholar 

  • Ishaq H, Dincer I (2020) Development and multi-objective optimization of a newly proposed industrial heat recovery based cascaded hydrogen and ammonia synthesis system. Sci Total Environ 743:140671

    Article  Google Scholar 

  • Ishibuchi H, Tsukamoto N, Nojima Y (2008) Evolutionary many-objective optimization: A short review, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).IEEE, pp.2419–2426

  • Ismail MA, Mezhuyev V, Deris S, Mohamad MS, Kasim S, Saedudin RR (2017) Multi-objective optimization of biochemical system production using an improve Newton Competitive differential evolution method. Int J Adv Sci Eng Inform Technol 7:1535

    Article  Google Scholar 

  • Jia X, Zhang T, Wang F, Han F (2006) Multi-objective modeling and optimization for cleaner production processes. J Clean Prod 14:146–151

    Article  Google Scholar 

  • Jia Y, Sun S, Liu L, Mu Y, An L (2004) Design of silicone rubber according to requirements based on the multi-objective optimization of chemical reactions. Acta Mater 52:4153–4159

    Article  Google Scholar 

  • Jiang Y, Hu T, Huang C, Wu X (2007) An improved particle swarm optimization algorithm. Appl Math Comput 193:231–239

    MATH  Google Scholar 

  • Kachhap R, Guria C (2005) Multi-Objective Optimization of a Batch Copoly (ethylene‐polyoxyethylene terephthalate) Reactor Using Different Adaptations of Nondominated Sorting Genetic Algorithm. Macromol Ther Simul 14:358–373

    Article  Google Scholar 

  • Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8:687–697

    Article  Google Scholar 

  • Karaboğa D, Ökdem S (2004) A simple and global optimization algorithm for engineering problems: differential evolution algorithm. Turkish J Electr Eng Comput Sci 12:53–60

    Google Scholar 

  • Kasat RB, Gupta SK (2003) Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using genetic algorithm (GA) with the jumping genes operator. Comput Chem Eng 27:1785–1800

    Article  Google Scholar 

  • Kasat RB, Kunzru D, Saraf DN, Gupta SK (2002) Multiobjective Optimization of Industrial FCC Units Using Elitist Nondominated Sorting Genetic Algorithm. Ind Eng Chem Res 41:4765–4776

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization, Proceedings of ICNN’95-International Conference on Neural Networks. IEEE, pp. 1942–1948

  • Khorasaninejad E, Hajabdollahi H (2014) Thermo-economic and environmental optimization of solar assisted heat pump by using multi-objective particle swam algorithm. Energy 72:680–690

    Article  Google Scholar 

  • Koledina K, Koledin S, Karpenko A, Gubaydullin I, Vovdenko M (2019) Multi-objective optimization of chemical reaction conditions based on a kinetic model. J Math Chem 57:484–493

    Article  MATH  Google Scholar 

  • Konak A, Coit DW, Smith AE (2006) Multi-objective optimization using genetic algorithms: A tutorial. Reliab Eng Syst Safe 91:992–1007

    Article  Google Scholar 

  • Kordabadi H, Jahanmiri A (2007) A pseudo-dynamic optimization of a dual-stage methanol synthesis reactor in the face of catalyst deactivation. Chem Eng Process 46:1299–1309

    Article  Google Scholar 

  • Kula C, Sayar NA (2019) Multi-objective optimization of a novel crude lipase-catalyzed fatty acid methyl ester (FAME) production using low-order polynomial and Kriging models. Int J Green Energy 16:657–665

    Article  Google Scholar 

  • Kumar A, Guria C, Pathak AK (2018) Optimal cultivation towards enhanced algae-biomass and lipid production using Dunaliella tertiolecta for biofuel application and potential CO2 bio-fixation: Effect of nitrogen deficient fertilizer, light intensity, salinity and carbon supply strategy. Energy 148:1069–1086

    Article  Google Scholar 

  • Kumar H, Yadav SP (2019) Using NSGA-II to Solve Interactive Fuzzy Multi-objective Reliability Optimization of Complex System. Soft Computing for Problem Solving. Springer, pp 399–412

  • Kumar M, Guria C (2017) The elitist non-dominated sorting genetic algorithm with inheritance (i-NSGA-II) and its jumping gene adaptations for multi-objective optimization. Inf Sci 382:15–37

    Article  Google Scholar 

  • Kundu PK, Zhang Y, Ray AK (2009) Multi-objective optimization of simulated countercurrent moving bed chromatographic reactor for oxidative coupling of methane. Chem Eng Sci 64:4137–4149

    Article  Google Scholar 

  • Lee ES-Q, Rangaiah G (2009) Optimization of recovery processes for multiple economic and environmental objectives. Ind Eng Chem Res 48:7662–7681

    Article  Google Scholar 

  • Lee FC, Rangaiah GP, Ray AK (2007) Multi-objective optimization of an industrial penicillin V bioreactor train using non‐dominated sorting genetic algorithm. Biotechnol Bioeng 98:586–598

    Article  Google Scholar 

  • Li C, Zhu Q, Geng Z (2007) Multi-objective particle swarm optimization hybrid algorithm: An application on industrial cracking furnace. Ind Eng Chem Res 46:3602–3609

    Article  Google Scholar 

  • Li G, Cai C (2017) Estimation parameters of hydrocracking model with NSGA-ii (Non-dominated Sorting Genetic Algorithm) by using discrete kinetic lumping model. Fuel 200:333–344

    Article  Google Scholar 

  • Li X, Chen S-L, Teo CS, Tan KK (2019) Enhanced sensitivity shaping by data-based tuning of disturbance observer with non-binomial filter. ISA Trans 85:284–292

    Article  Google Scholar 

  • Li X, Zhu H, Ma J, Teo TJ, Teo CS, Tomizuka M, Lee TH (2020a) Data-Driven Multiobjective Controller Optimization for a Magnetically Levitated Nanopositioning System. IEEE/ASME Trans Mechatron 25:1961–1970

    Article  Google Scholar 

  • Li Y, Ding Z, Wang J, Xu J, Yu W, Ray AK (2020b) A comparison between simulated moving bed and sequential simulated moving bed system based on multi-objective optimization. Chem Eng Sci 219:115562

    Article  Google Scholar 

  • Li Y, Xu J, Yu W, Ray AK (2020c) Multi-objective optimization of sequential simulated moving bed for the purification of xylo-oligosaccharides. Chem Eng Sci 211:115279

    Article  Google Scholar 

  • Liberatore R, Bassi A, Turchetti L, Venturin M (2018) Multi-objective optimization of a hydrogen production through the HyS process powered by solar energy in different scenarios. Int J Hydrog Energy 43:8683–8697

    Article  Google Scholar 

  • Link H, Vera J, Weuster-Botz D, Torres Darias N, Franco-Lara E (2008) Multi-objective steady state optimization of biochemical reaction networks using a constrained genetic algorithm. Comput Chem Eng 32:1707–1713

    Article  Google Scholar 

  • Liu P-K, Wang F-S (2008) Inverse problems of biological systems using multi-objective optimization. J Chin Inst Chem Eng 39:399–406

    Article  Google Scholar 

  • Løvbjerg M, Rasmussen TK, Krink T (2001) Hybrid particle swarm optimiser with breeding and subpopulations, Proceedings of the 3rd annual conference on genetic and evolutionary computation. Morgan Kaufmann Publishers Inc., pp. 469–476

  • Lv J, Jiang X, He G, Xiao W, Li S, Sengupta D, El-Halwagi MM (2017) Economic and system reliability optimization of heat exchanger networks using NSGA-II algorithm. Appl Therm Eng 124:716–724

    Article  Google Scholar 

  • Maier HR, Razavi S, Kapelan Z, Matott LS, Kasprzyk J, Tolson BA (2019) Introductory overview: Optimization using evolutionary algorithms and other metaheuristics. ‎Environ Model Softw 114:195–213

    Article  Google Scholar 

  • Maimon O, Rokach L (2005)Data mining and knowledge discovery handbook

  • Marini F, Walczak B (2015) Particle swarm optimization (PSO). A tutorial. Chemom Intell Lab Syst 149:153–165

    Article  Google Scholar 

  • Massebeuf S, Fonteix C, Hoppe S, Pla F (2003) Development of new concepts for the control of polymerization processes: Multiobjective optimization and decision engineering. I. Application to emulsion homopolymerization of styrene. J Appl Polym Sci 87:2383–2396

    Article  Google Scholar 

  • Metaxiotis K, Liagkouras K (2012) Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review. Expert Syst Appl 39:11685–11698

    Article  Google Scholar 

  • Mian A, Ensinas AV, Marechal F (2015) Multi-objective optimization of SNG production from microalgae through hydrothermal gasification. Comput Chem Eng 76:170–183

    Article  Google Scholar 

  • Miranda-Galindo EY, Segovia-Hernández JG, Hernández S, Gutiérrez-Antonio C, Briones-Ramírez A (2011) Reactive thermally coupled distillation sequences: Pareto front. Ind Eng Chem Res 50:926–938

    Article  Google Scholar 

  • Mitchell M (1998) An introduction to genetic algorithms. MIT press

  • Mitra K (2011) Handling uncertainty in kinetic parameters in optimal operation of a polymerization reactor. Mater Manuf Processes 26:446–454

    Article  Google Scholar 

  • Mitra K, Deb K, Gupta SK (1998) Multiobjective dynamic optimization of an industrial nylon 6 semibatch reactor using genetic algorithm. J Appl Polym Sci 69:69–87

    Article  Google Scholar 

  • Mitra K, Majumdar S (2007) Multicriteria optimal control of polypropylene terepthalate polymerization reactor. Mater Manuf Processes 22:532–540

    Article  Google Scholar 

  • Mitra K, Majumdar S, Raha S (2004a) Multiobjective dynamic optimization of a semi-batch epoxy polymerization process. Comput Chem Eng 28:2583–2594

    Article  Google Scholar 

  • Mitra K, Majumdar S, Raha S (2004b) Multiobjective Optimization of a Semibatch Epoxy Polymerization Process Using the Elitist Genetic Algorithm. Ind Eng Chem Res 43:6055–6063

    Article  Google Scholar 

  • Mogilicharla A, Chugh T, Majumdar S, Mitra K (2014) Multi-objective optimization of bulk vinyl acetate polymerization with branching. Mater Manuf Processes 29:210–217

    Article  Google Scholar 

  • Mogilicharla A, Reddy PS (2020) Kinetic modeling and development of optimal trajectories for biodiesel production using multi-objective optimization. Environ Technol Innov 20:101111

    Article  Google Scholar 

  • Mohammad Fauzi AH, Amin NAS, Mat R (2014) Esterification of oleic acid to biodiesel using magnetic ionic liquid: Multi-objective optimization and kinetic study. Appl Energy 114:809–818

    Article  Google Scholar 

  • Mohammad Fauzi AH, Saidina Amin NA (2013) Optimization of oleic acid esterification catalyzed by ionic liquid for green biodiesel synthesis. Energy Convers Manage 76:818–827

    Article  Google Scholar 

  • Mohanty S (2006) Multiobjective optimization of synthesis gas production using non-dominated sorting genetic algorithm. Comput Chem Eng 30:1019–1025

    Article  Google Scholar 

  • Mokeddem D, Khellaf A (2009) Optimal solutions of multiproduct batch chemical process using multiobjective genetic algorithm with expert decision system. Journal of Analytical Methods in Chemistry 2009

  • Mokeddem D, Khellaf A (2010a) Multicriteria optimization of multiproduct batch chemical process using genetic algorithm. J Food Process Eng 33:979–991

    Article  MATH  Google Scholar 

  • Mokeddem D, Khellaf A (2010b) Optimal feeding profile in fed-batch bioreactors using a genetic algorithm. Int J Prod Res 48:6125–6135

    Article  MATH  Google Scholar 

  • Mokeddem D, Khellaf A (2012) Optimal feeding profile for a fuzzy logic controller in a bioreactors using genetic algorithm. Nonlinear Dyn 67:2835–2845

    Article  MathSciNet  Google Scholar 

  • Mokeddem D, Khellaf A (2014) Modeling and multi-criteria optimization of an industrial process for continuous lactic acid production. Bioprocess Biosyst Eng 37:1141–1150

    Article  Google Scholar 

  • Mondal B, Parhi SS, Rangaiah GP, Jana AK (2021) Nano-catalytic heterogeneous reactive distillation for algal biodiesel production: Multi-objective optimization and heat integration. Energy Convers Manage 241:114298

    Article  Google Scholar 

  • Monsef H, Naghashzadegan M, Jamali A, Farmani R (2019) Comparison of evolutionary multi objective optimization algorithms in optimum design of water distribution network. Ain Shams Eng J 10:103–111

    Article  Google Scholar 

  • Montazer-Rahmati MM, Binaee R (2010) Multi-objective optimization of an industrial hydrogen plant consisting of a CO2 absorber using DGA and a methanator. Comput Chem Eng 34:1813–1821

    Article  Google Scholar 

  • Moore J, Chapman R (1999) Application of particle swarm to multiobjective optimization. Department of Computer Science and Software Engineering, Auburn University

  • Mostaghim S (2004) Multi-Objective Evolutionary Algorithms: Data Structures, Convergence, and Diversity. Shaker

  • Mu S, Su H, Gu Y, Chu J (2003) Multi-objective optimization of industrial purified terephthalic acid (PTA) oxidation process. Chin J Chem Eng 11:536–541

    Google Scholar 

  • Mu S, Su H, Jia T, Gu Y, Chu J (2004) Scalable multi-objective optimization of industrial purified terephthalic acid (PTA) oxidation process. Comput Chem Eng 28:2219–2231

    Article  Google Scholar 

  • Na J, Kshetrimayum KS, Lee U, Han C (2017) Multi-objective optimization of microchannel reactor for Fischer-Tropsch synthesis using computational fluid dynamics and genetic algorithm. Chem Eng J 313:1521–1534

    Article  Google Scholar 

  • Nabavi R, Rangaiah G, Niaei A, Salari D (2011) Design optimization of an LPG thermal cracker for multiple objectives.International Journal of Chemical Reactor Engineering9

  • Nabavi SR, Rangaiah GP, Niaei A, Salari D (2009) Multiobjective Optimization of an Industrial LPG Thermal Cracker using a First Principles Model. Ind Eng Chem Res 48:9523–9533

    Article  Google Scholar 

  • Nandasana AD, Ray AK, Gupta SK (2003) Dynamic model of an industrial steam reformer and its use for multiobjective optimization. Ind Eng Chem Res 42:4028–4042

    Article  Google Scholar 

  • Nayak A, Gupta SK (2004) Multi-Objective Optimization of Semi‐Batch Copolymerization Reactors Using Adaptations of Genetic Algorithm. Macromol Ther Simul 13:73–85

    Article  Google Scholar 

  • Ng QH, Sharma S, Rangaiah GP (2017) Design and analysis of an ethyl benzene production process using conventional distillation columns and dividing-wall column for multiple objectives. Chem Eng Res Des 118:142–157

    Article  Google Scholar 

  • Ngatchou P, Zarei A, El-Sharkawi A (2005) Pareto Multi Objective Optimization, Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems, pp. 84–91

  • Nishiwaki A, Dunn I (1999) Performance of a two-stage fermentor with cell recycle for continuous production of lactic acid. Bioprocess Eng 21:299–305

    Article  Google Scholar 

  • Nogueira Nakashima R, Oliveira Junior S (2021) Multi-objective optimization of biogas systems producing hydrogen and electricity with solid oxide fuel cells. International Journal of Hydrogen Energy

  • Nyarko EK, Cupec R, Filko D (2014) A Comparison of Several Heuristic Algorithms for Solving High Dimensional Optimization Problems. Int J Electr Comput Eng Syst 5:1–8

    Google Scholar 

  • Oh P, Rangaiah G, Ray AK (2002) Simulation and multiobjective optimization of an industrial hydrogen plant based on refinery off-gas. Ind Eng Chem Res 41:2248–2261

    Article  Google Scholar 

  • Oh P, Ray AK, Rangaiah G (2001) Triple-objective optimization of an industrial hydrogen plant. J Chem Eng Jpn 34:1341–1355

    Article  Google Scholar 

  • Okoro OV, Nie L, Hobbi P, Shavandi A (2021) Valorization of waste apple pomace for production of platform biochemicals: a multi-objective optimization study. Waste Biomass Valoriz 12:6887–6901

    Article  Google Scholar 

  • Oni A, Fadare D, Sharma S, Rangaiah GP (2018) Multi-objective optimisation of a double contact double absorption sulphuric acid plant for cleaner operation. J Clean Prod 181:652–662

    Article  Google Scholar 

  • Ouattara A, Pibouleau L, Azzaro-Pantel C, Domenech S, Baudet P, Yao B (2012) Economic and environmental strategies for process design. Comput Chem Eng 36:174–188

    Article  Google Scholar 

  • Ozbilen A, Dincer I, Rosen MA (2016) Development of a four-step Cu–Cl cycle for hydrogen production – Part II: Multi-objective optimization. Int J Hydrog Energy 41:7826–7834

    Article  Google Scholar 

  • Ozcan H, Dincer I (2017) Exergoeconomic optimization of a new four-step magnesium–chlorine cycle. Int J Hydrog Energy 42:2435–2445

    Article  Google Scholar 

  • Pan Q-K, Suganthan PN, Wang L, Gao L, Mallipeddi R (2011) A differential evolution algorithm with self-adapting strategy and control parameters. Comput Oper Res 38:394–408

    Article  MathSciNet  MATH  Google Scholar 

  • Panahi M, Yasari E, Rafiee A (2018) Multi-objective optimization of a gas-to-liquids (GTL) process with staged Fischer-Tropsch reactor. Energy Convers Manage 163:239–249

    Article  Google Scholar 

  • Panicker VV, Aryadutt C, Anoop K (2019) Elitist Non-dominated Sorting Genetic Algorithm-Based Heuristic for Optimizing Rail Freight Transportation, Proceedings of International Conference on Intelligent Manufacturing and Automation. Springer, pp. 623–630

  • Parhi SS, Rangaiah GP, Jana AK (2019) Multi-objective optimization of vapor recompressed distillation column in batch processing: Improving energy and cost savings. Appl Therm Eng 150:1273–1296

    Article  Google Scholar 

  • Patle DS, Sharma S, Ahmad Z, Rangaiah GP (2014) Multi-objective optimization of two alkali catalyzed processes for biodiesel from waste cooking oil. Energy Convers Manage 85:361–372

    Article  Google Scholar 

  • Pearl J (1984) Heuristics: Intelligent Search Strategies for Computer Problem Solving, Addision. Wesley Longman Publishing Co., Inc. Boston, MA, USA. ISBN: 0-201-05594-5

  • Peduzzi E, Tock L, Boissonnet G, Maréchal F (2013) Thermo-economic evaluation and optimization of the thermo-chemical conversion of biomass into methanol. Energy 58:9–16

    Article  Google Scholar 

  • Prakash S, Trivedi V, Ramteke M (2016) An elitist non-dominated sorting bat algorithm NSBAT-II for multi-objective optimization of phthalic anhydride reactor. Int J Syst Assur Eng Manage 7:299–315

    Article  Google Scholar 

  • Pulido GT, Coello CAC (2004) Using clustering techniques to improve the performance of a multi-objective particle swarm optimizer, Genetic and Evolutionary Computation Conference. Springer, pp. 225–237

  • Punase KD, Rao N, Gupta SK (2019) Simulation and multi-objective optimization of a fixed bed catalytic reactor to produce hydrogen using ethanol steam reforming. Int J Energy Res 43:4580–4591

    Article  Google Scholar 

  • Qin AK, Huang VL, Suganthan PN (2008) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evolut Comput 13:398–417

    Article  Google Scholar 

  • Quiroz-Ramírez JJ, Sánchez-Ramírez E, Segovia-Hernández JG (2018) Energy, exergy and techno-economic analysis for biobutanol production: a multi-objective optimization approach based on economic and environmental criteria. Clean Technol Environ Policy 20:1663–1684

    Article  Google Scholar 

  • Raha S, Majumdar S, Mitra K (2004) Effect of Caustic Addition in Epoxy Polymerization Process: A Single and Multi-Objective Evolutionary Approach. Macromol Ther Simul 13:152–161

    Article  Google Scholar 

  • Rahman RK, Ibrahim S, Raj A (2018) Multi-Objective Optimization to Predict Minimum Temperature for Efficient BTEX Destruction to Minimize Fuel Gas Consumption in Sulfur Recovery Units, Abu Dhabi International Petroleum Exhibition & Conference. Society of Petroleum Engineers

  • Rahman RK, Ibrahim S, Raj A (2019) Multi-objective optimization of sulfur recovery units using a detailed reaction mechanism to reduce energy consumption and destruct feed contaminants. Comput Chem Eng 128:21–34

    Article  Google Scholar 

  • Raj Gupta R, Gupta SK (1999) Multiobjective optimization of an industrial nylon-6 semibatch reactor system using genetic algorithm. J Appl Polym Sci 73:729–739

    Article  Google Scholar 

  • Rajesh J, Gupta SK, Rangaiah Gt, Ray AK (2000) Multiobjective optimization of steam reformer performance using genetic algorithm. Ind Eng Chem Res 39:706–717

    Article  Google Scholar 

  • Rajesh JK, Gupta SK, Rangaiah GP, Ray AK (2001) Multi-objective optimization of industrial hydrogen plants. Chem Eng Sci 56:999–1010

    Article  Google Scholar 

  • Ramteke M, Gupta SK (2008a) Biomimetic Adaptations of GA and SA for the Robust MO Optimization of an Industrial Nylon-6 Reactor. Mater Manuf Processes 24:38–46

    Article  Google Scholar 

  • Ramteke M, Gupta SK (2008b) Multiobjective optimization of an industrial nylon-6 semi batch reactor using the a-jumping gene adaptations of genetic algorithm and simulated annealing. Polym Eng Sci 48:2198–2215

    Article  Google Scholar 

  • Ramteke M, Gupta SK (2009) Biomimetic adaptation of the evolutionary algorithm, NSGA-II-aJG, using the biogenetic law of embryology for intelligent optimization. Ind Eng Chem Res 48:8054–8067

    Article  Google Scholar 

  • Rangaiah GP (2009) Multi-objective optimization: techniques and applications in chemical engineering. World Scientific, Singapore

    Google Scholar 

  • Rangaiah GP (2016) Multi-objective Optimization: Techniques And Applications In Chemical Engineering. World Scientific, Singapore

    Google Scholar 

  • Rangaiah GP, Sharma S, Sreepathi BK (2015) Multi-objective optimization for the design and operation of energy efficient chemical processes and power generation. Curr Opin Chem Eng 10:49–62

    Article  Google Scholar 

  • Rao RV, Rai DP, Balic J (2018) Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–learning-based optimization algorithm. J Intell Manuf 29:1715–1737

    Article  Google Scholar 

  • Rayward-Smith VJ, Osman C, Reeves CR, Smith GD (1996) Modern heuristic search methods. John Wiley

  • Reddy PS, Rani KY, Patwardhan SC (2017) Multi-objective optimization of a reactive batch distillation process using reduced order model. Comput Chem Eng 106:40–56

    Article  Google Scholar 

  • Rezghi A, Riasi A, Tazraei P (2020) Multi-objective optimization of hydraulic transient condition in a pump-turbine hydropower considering the wicket-gates closing law and the surge tank position. Renew Energy 148:478–491

    Article  Google Scholar 

  • Robinson J, Sinton S, Rahmat-Samii Y (2002) Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna, IEEE Antennas and Propagation Society International Symposium (IEEE Cat. No. 02CH37313). IEEE, pp. 314–317

  • Robles-Rodriguez CE, Bideaux C, Guillouet SE, Gorret N, Roux G, Molina-Jouve C, Aceves-Lara CA (2016) Multi-objective particle swarm optimization (MOPSO) of lipid accumulation in Fed-batch cultures, 2016 24th Mediterranean Conference on Control and Automation (MED). IEEE, pp. 979–984

  • Romero-García AG, Prado-Rúbio OA, Contreras-Zarazúa G, Ramírez-Márquez C, Segovia-Hernández JG (2019) Simultaneous design and controllability optimization for the reaction zone for furfural bioproduction. In: Kiss AA, Zondervan E, Lakerveld R, Özkan L (eds) Comput. Aided Chem. Eng. Elsevier, pp 133–138

  • Sadeghi S, Ghandehariun S, Naterer GF (2020) Exergoeconomic and multi-objective optimization of a solar thermochemical hydrogen production plant with heat recovery. Energy Convers Manage 225:113441

    Article  Google Scholar 

  • Sadi M, Dabir B (2007) Application of genetic algorithm to determine kinetic parameters of free radical polymerization of vinyl acetate by multi-objective optimization technique. Iran J Chem Chem Eng 26:29–37

    Google Scholar 

  • Sahraei MH, Farhadi F, Boozarjomehry RB (2013) Analysis and interaction of exergy, environmental and economic in multi-objective optimization of BTX process based on evolutionary algorithm. Energy 59:147–156

    Article  Google Scholar 

  • Salari D, Niaei A, NABAVI R (2008) Multi-objective genetic optimization of ethane thermal cracking reactor. Iran J Chem Eng 5:29–39

    Google Scholar 

  • Salcedo-Sanz S, Del Ser J, Landa-Torres I, Gil-López S, Portilla-Figueras J (2014) The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. The Scientific World Journal 2014

  • Sanabria-Borbón A, Tlelo-Cuautle E (2018) Sizing analogue integrated circuits by integer encoding and NSGA-II. IETE Tech Rev 35:237–243

    Article  Google Scholar 

  • Sanaye S, Hajabdollahi H (2010) Multi-objective optimization of shell and tube heat exchangers. Appl Therm Eng 30:1937–1945

    Article  Google Scholar 

  • Sankararao B, Gupta SK (2006) Multiobjective optimization of the dynamic operation of an industrial steam reformer using the jumping gene adaptations of simulated annealing. Asia-Pac J Chem Eng 1:21–31

    Article  Google Scholar 

  • Sankararao B, Gupta SK (2007) Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using two jumping gene adaptations of simulated annealing. Comput Chem Eng 31:1496–1515

    Article  Google Scholar 

  • Sankararao B, Yoo CK (2011) Development of a Robust Multiobjective Simulated Annealing Algorithm for Solving Multiobjective Optimization Problems. Ind Eng Chem Res 50:6728–6742

    Article  Google Scholar 

  • Sarkar D, Modak JM (2005) Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm. Chem Eng Sci 60:481–492

    Article  Google Scholar 

  • Sarkar D, Modak JM (2006) Optimal design of multiproduct batch chemical plant using NSGA-II. Asia-Pac. J Chem Eng 1:13–20

    Google Scholar 

  • Sarkar D, Rohani S, Jutan A (2007) Multiobjective optimization of semibatch reactive crystallization processes. AIChE J 53:1164–1177

    Article  Google Scholar 

  • Savic D (2002) Single-objective vs. multiobjective optimisation for integrated decision support

  • Sciacovelli A, Verda V (2012) Sensitivity analysis applied to the multi-objective optimization of a MCFC hybrid plant. Energy Convers Manage 60:180–187

    Article  Google Scholar 

  • Sepulveda GC, Ochoa S, Thibault J (2020) Methodology to Solve the Multi-Objective Optimization of Acrylic Acid Production Using Neural Networks as Meta-Models. Processes 8:1184

    Article  Google Scholar 

  • Seyam S, Al-Hamed KH, Qureshy AM, Dincer I, Agelin-Chaab M, Rahnamayan S (2019) Multi-objective Optimization of Hydrogen Production in Hybrid Renewable Energy Systems, 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 850–857

  • Shadbahr J, Zhang Y, Khan F, Hawboldt K (2018) Multi-objective optimization of simultaneous saccharification and fermentation for cellulosic ethanol production. Renew Energy 125:100–107

    Article  Google Scholar 

  • Shahhosseini HR, Farsi M, Eini S (2016) Multi-objective optimization of industrial membrane SMR to produce syngas for Fischer-Tropsch production using NSGA-II and decision makings. J Nat Gas Sci Eng 32:222–238

    Article  Google Scholar 

  • Shahhosseini HR, Iranshahi D, Saeidi S, Pourazadi E, Klemeš JJ (2018) Multi-objective optimisation of steam methane reforming considering stoichiometric ratio indicator for methanol production. J Clean Prod 180:655–665

    Article  Google Scholar 

  • Shahkarami P, Fatemi S (2015) Mathematical modeling and optimization of combined steam and dry reforming of methane process in catalytic fluidized bed membrane reactor. Chem Eng Commun 202:774–786

    Article  Google Scholar 

  • Sharifzadeh M, Meghdari M, Rashtchian D (2017) Multi-objective design and operation of Solid Oxide Fuel Cell (SOFC) Triple Combined-cycle Power Generation systems: Integrating energy efficiency and operational safety. Appl Energy 185:345–361

    Article  Google Scholar 

  • Sharma S, Celebi AD, Maréchal F (2017) Robust multi-objective optimization of gasifier and solid oxide fuel cell plant for electricity production using wood. Energy 137:811–822

    Article  Google Scholar 

  • Sharma S, Chao Lim Z, Rangaiah GP (2013) Process design for economic, environmental and safety objectives with an application to the cumene process.Multi-Objective Optimization in Chemical Engineering: Developments and Applications,449–477

  • Sharma S, Rangaiah GP (2013a) Improved Constraint Handling Technique for Multi-Objective Optimization with Application to Two Fermentation Processes.Multi‐Objective Optimization in Chemical Engineering: Developments and Applications,129–156

  • Sharma S, Rangaiah GP (2013b) An improved multi-objective differential evolution with a termination criterion for optimizing chemical processes. Comput Chem Eng 56:155–173

    Article  Google Scholar 

  • Sharma S, Rangaiah GP (2013c) Multi-objective optimization of a bio-diesel production process. Fuel 103:269–277

    Article  Google Scholar 

  • Shelokar P, Jayaraman V, Kulkarni B (2003) Multiobjective Optimization of Reactor–Regenerator System Using Ant Algorithm. Pet Sci Technol 21:1167–1184

    Article  Google Scholar 

  • Sheng L, Qian S, Ye Y, Wu Y (2017) An improved immune algorithm for optimizing the pulse width modulation control sequence of inverters. Eng Optimiz 49:1463–1482

    Article  MathSciNet  Google Scholar 

  • Shi Y (2001) Particle swarm optimization: developments, applications and resources, Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546). IEEE, pp. 81–86

  • Sierra MR, Coello CAC (2005) Improving PSO-based multi-objective optimization using crowding, mutation and∈-dominance, International conference on evolutionary multi-criterion optimization. Springer, pp. 505–519

  • Silva CM, Biscaia EC (2003) Genetic algorithm development for multi-objective optimization of batch free-radical polymerization reactors. Comput Chem Eng 27:1329–1344

    Article  Google Scholar 

  • Smith R, Kasprzyk J, Zagona E (2015) Many-objective analysis to optimize pumping and releases in multireservoir water supply network. J Water Res Plan Man 142:04015049

    Article  Google Scholar 

  • Smith SL, Cagnoni S (2011) Genetic and evolutionary computation: medical applications. John Wiley & Sons

  • Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2:221–248

    Article  Google Scholar 

  • Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359

    Article  MathSciNet  MATH  Google Scholar 

  • Streichert F, Kistner IA, Koch W, Commerzbank A (2001) „Evolutionäre Algorithmen:Implementation und Anwendungen im Asset-Management-Bereich “. Diplomarbeit

  • Subramani HJ, Zhang Z, Hidajat K, Ray AK (2004) Multiobjective Optimization Of Simulated Moving Bed Reactor And Its Modification—Varicol Process. Can J Chem Eng 82:590–598

    Article  Google Scholar 

  • Sun M, Xia S, Chen L, Wang C, Tang C (2020) Minimum entropy generation rate and maximum yield optimization of sulfuric acid decomposition process using NSGA-II. Entropy 22:1065

    Article  Google Scholar 

  • Taghdisian H, Pishvaie MR, Farhadi F (2015) Multi-objective optimization approach for green design of methanol plant based on CO2-efficeincy indicator. J Clean Prod 103:640–650

    Article  Google Scholar 

  • Tanabe R, Ishibuchi H, Oyama A (2017) Benchmarking Multi- and Many-Objective Evolutionary Algorithms Under Two Optimization Scenarios. IEEE Access 5:19597–19619

    Article  Google Scholar 

  • Tao L, Xu B, Hu Z, Zhong W (2017) Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm. Chin J Chem Eng 25:983–991

    Article  Google Scholar 

  • Tarafder A, Lee BCS, Ray AK, Rangaiah GP (2005a) Multiobjective Optimization of an Industrial Ethylene Reactor Using a Nondominated Sorting Genetic Algorithm. Ind Eng Chem Res 44:124–141

    Article  Google Scholar 

  • Tarafder A, Rangaiah G, Ray AK (2005b) Multiobjective optimization of an industrial styrene monomer manufacturing process. Chem Eng Sci 60:347–363

    Article  Google Scholar 

  • Tarafder A, Rangaiah GP, Ray AK (2007) A study of finding many desirable solutions in multiobjective optimization of chemical processes. Comput Chem Eng 31:1257–1271

    Article  Google Scholar 

  • Taras S, Woinaroschy A (2012) An interactive multi-objective optimization framework for sustainable design of bioprocesses. Comput Chem Eng 43:10–22

    Article  Google Scholar 

  • Thafseer M, Al Ani Z, Gujarathi AM, Vakili-Nezhaad GR (2021) Towards process, environment and economic based criteria for multi-objective optimization of industrial acid gas removal process. J Nat Gas Sci Eng 88:103800

    Article  Google Scholar 

  • Thakur AK, Gupta SK, Kumar R, Banerjee N, Chaudhari P (2021) Multi-objective optimization of an industrial slurry phase ethylene polymerization reactor. International Journal of Chemical Reactor Engineering

  • Thangaraj R, Pant M, Abraham A, Badr Y (2009) Hybrid evolutionary algorithm for solving global optimization problems, International Conference on Hybrid Artificial Intelligence Systems. Springer, pp. 310–318

  • Tomassini M (1999) Parallel and distributed evolutionary algorithms: A review

  • Torres-Cerna CE, Alanis AY, Poblete-Castro I, Bermejo-Jambrina M, Hernandez-Vargas EA (2016) A comparative study of differential evolution algorithms for parameter fitting procedures, 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 4662–4666

  • Uebel K, Rößger P, Prüfert U, Richter A, Meyer B (2016) CFD-based multi-objective optimization of a quench reactor design. Fuel Process Technol 149:290–304

    Article  Google Scholar 

  • Ursem RK (2003) Models for evolutionary algorithms and their applications in system identification and control optimization. BRICS

  • Van Veldhuizen DA, Lamont GB (2000) Multiobjective evolutionary algorithms: Analyzing the state-of-the-art. Evol Comput 8:125–147

    Article  Google Scholar 

  • Wagner F, Kühhorn A, Janetzke T, Gerstberger U (2018) Multi-Objective Optimization of the Cooling Configuration of a High Pressure Turbine Blade, ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, pp. V05CT20A001-V005CT020A001

  • Wang C, Li L (2012) Multi-objective optimization of ethylbenzene dehydrogenation process based on modified particle swarm algorithm. Control and Instrument in Chemical Industry 39:356–359

    Google Scholar 

  • Wang D, Feng X (2013) Simulation and multi-objective optimization of an integrated process for hydrogen production from refinery off-gas. Int J Hydrog Energy 38:12968–12976

    Article  Google Scholar 

  • Wang H (2018)Stochastic and deterministic algorithms for continuous black-box optimization

  • Wang H, Jia J, Tang S, Xu D, Zheng Y, Zhang H, Wu Y, Liu R (2010) Multi-Objective Optimization Research for Effective Constituents of QiliQiangxin Capsule of Chinese Formula, Bioinformatics and Biomedical Engineering (iCBBE), 2010 4th International Conference on. IEEE, pp. 1–3

  • Wang J, Chen W, Li Y, Xu J, Yu W, Ray AK (2021) Multi-Objective Optimizations of Non-Isothermal Simulated Moving Bed Reactor: Parametric Analyses. Processes 9:360

    Article  Google Scholar 

  • Wang J, Tian Y, Li Y, Xu J, Yu W, Ray AK (2020) Multi-objective optimization of non-isothermal simulated moving bed reactor: Methyl acetate synthesis. Chem Eng J 395:125041

    Article  Google Scholar 

  • Weifeng H, Hongye S, Shengjing M, Jian C (2007) Multiobjective optimization of the industrial naphtha catalytic reforming process. Chin J Chem Eng 15:75–80

    Article  Google Scholar 

  • Weise T (2009) Global optimization algorithms-theory and application. Self-published 2

  • Wu W, Liou Y-C, Zhou Y-Y (2012) Multiobjective Optimization of a Hydrogen Production System with Low CO2 Emissions. Ind Eng Chem Res 51:2644–2651

    Article  Google Scholar 

  • Xu B, Qi R, Zhong W, Du W, Qian F (2013) Optimization of p-xylene oxidation reaction process based on self-adaptive multi-objective differential evolution. Chemom Intell Lab Syst 127:55–62

    Article  Google Scholar 

  • Xu P (2012) Three essays on bilevel optimization algorithms and applications

  • Yang S, Li M, Liu X, Zheng J (2013) A grid-based evolutionary algorithm for many-objective optimization. IEEE Trans Evolut Comput 17:721–736

    Article  Google Scholar 

  • Yang Y, Li H, Yao M, Zhang Y, Zhang C, Zhang L, Wu S (2020) Optimizing the size of a printed circuit heat exchanger by multi-objective genetic algorithm. Appl Therm Eng 167:114811

    Article  Google Scholar 

  • Yasari E (2017) A green industrial scale di-methyl ether reactor with aiming to CO2 reduction: staging and multi-objective optimization approach. J Taiwan Inst Chem Eng 81:110–118

    Article  Google Scholar 

  • Yee AKY, Ray AK, Rangaiah GP (2003) Multiobjective optimization of an industrial styrene reactor. Comput Chem Eng 27:111–130

    Article  Google Scholar 

  • Yu W, Hariprasad J, Zhang Z, Hidajat K, Ray AK (2004) Application of multi-objective optimization in the design of SMB in chemical process industry. J Chin Inst Chem Engrs 35:1–8

    Google Scholar 

  • Yu W, Hidajat K, Ray AK (2003) Application of multiobjective optimization in the design and operation of reactive SMB and its experimental verification. Ind Eng Chem Res 42:6823–6831

    Article  Google Scholar 

  • Yu W, Ohmori T, Yamamoto T, Endo A, Nakaiwa M, Itoh N (2007) Optimal design and operation of methane steam reforming in a porous ceramic membrane reactor for hydrogen production. Chem Eng Sci 62:5627–5631

    Article  Google Scholar 

  • Yu X, Gen M (2010) Introduction to evolutionary algorithms. Springer Science & Business Media

  • Yusoff Y, Ngadiman MS, Zain AM (2011) Overview of NSGA-II for optimizing machining process parameters. Procedia Eng 15:3978–3983

    Article  Google Scholar 

  • Yüzgeç U (2010) Performance comparison of differential evolution techniques on optimization of feeding profile for an industrial scale baker’s yeast fermentation process. ISA Trans 49:167–176

    Article  Google Scholar 

  • Zakaria ZY, Amin NAS, Linnekoski J (2014) Optimization of catalytic glycerol steam reforming to light olefins using Cu/ZSM-5 catalyst. Energy Convers Manage 86:735–744

    Article  Google Scholar 

  • Zaker MR, Fauteux-Lefebvre C, Thibault J (2021) Modelling and Multi-Objective Optimization of the Sulphur Dioxide Oxidation Process. Processes 9:1072

    Article  Google Scholar 

  • Zbigniew M, Fogel DB (2000) How to solve it: modern heuristics. Springer-Verlag

  • Zendehboudi A, Mota-Babiloni A, Makhnatch P, Saidur R, Sait SM (2019) Modeling and multi-objective optimization of an R450A vapor compression refrigeration system. Int J Refrig 100:141–155

    Article  Google Scholar 

  • Zhang C, Shao H, Li Y (2000) Particle swarm optimisation for evolving artificial neural network, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics.‘cybernetics evolving to systems, humans, organizations, and their complex interactions‘(cat. no. 0. IEEE, pp. 2487–2490

  • Zhang H, Wang S, Dai Y, Yang X, Zhao J, Cui P, Zhu Z, Wang Y, Zheng S, Gao J (2021) Multi-objective optimization of a clean, high-efficiency synthesis process of methyl-ethyl-ketone oxime from ammoximation. J Clean Prod 315:128176

    Article  Google Scholar 

  • Zhang L, Chen L, Xia S, Ge Y, Wang C, Feng H (2020) Multi-objective optimization for helium-heated reverse water gas shift reactor by using NSGA-II. Int J Heat Mass Transfer 148:119025

    Article  Google Scholar 

  • Zhang Y, Hidajat K, Ray AK (2004) Optimal design and operation of SMB bioreactor: production of high fructose syrup by isomerization of glucose. Biochem Eng J 21:111–121

    Article  Google Scholar 

  • Zitzewitz P, Fieg G (2017) Multi-objective optimization superimposed model‐based process design of an enzymatic hydrolysis process. AIChE J 63:1974–1988

    Article  Google Scholar 

  • Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evolut Comput 3:257–271

    Article  Google Scholar 

  • Živković LA, Pohar A, Likozar B, Nikačević NM (2020) Reactor conceptual design by optimization for hydrogen production through intensified sorption- and membrane-enhanced water-gas shift reaction. Chem Eng Sci 211:115174

    Article  Google Scholar 

  • Ziyang Z, Hidajat K, Ray AK (2002) Multiobjective optimization of simulated countercurrent moving bed chromatographic reactor (SCMCR) for MTBE synthesis. Ind Eng Chem Res 41:3213–3232

    Article  Google Scholar 

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Funding

This work was supported by Sultan Qaboos University, Sultanate of Oman under Grant IG/ENG/PCED/19/01.

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Authors and Affiliations

  1. Department of Petroleum & Chemical Engineering, College of Engineering, Sultan Qaboos University, P.O. Box 33, P.C. 123, Al-Khod, Muscat, Sultanate of Oman

    Zainab Al Ani, Ashish M. Gujarathi & Ala’a H. Al-Muhtaseb

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  1. Zainab Al Ani
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  3. Ala’a H. Al-Muhtaseb
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Correspondence to Ashish M. Gujarathi.

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Nomenclature

Nomenclature

ADE

Adaptive Differential Evolution

AMOPSO-AHP

Adaptive Multi-Objective Particle Swarm Optimization Analytic Hierarchy Process

ANFIS

Adaptive Neuro-Fuzzy Inference System

AODE

Adaptive Opposition Based Differential Evolution

ALIFMO

Additive Linear Interdependent Fuzzy Multi-Objective Optimization

ACO

Ant Colony Algorithm

ABC

Artificial Bee Colony Algorithm

AIS

Artificial Immune System

ANN-GA

Artificial Neural Network-Genetic Algorithm

ALGA

Augmented Lagrangian Genetic Algorithm

B-NSGA-II-aJG

Biomimetic Non-Dominated Sorting Genetic Algorithm-II-a Jumping Genes

CPEA

Clustering Pareto Evolutionary Algorithm

CROA

Coral Reef Algorithm

DETL

Differential Evolution with Tabu List

DE

Differential Evolution

DPEA

Dual Population Evolutionary Algorithm

EA’s

Evolutionary Algorithms

EMOO

Excel-Based Multi-Objective Optimization

EP

Evolutionary Programing

FCCU

Fluidized-Bed Catalytic Cracking Unit

FADE

Fuzzy Adaptive Differential Evolution

GA

Genetic Algorithm

GSA

Gravitational Search Algorithm

GSA*

Grid Search Approach

HDE

Hybrid Differential Evolution

HS

Harmony Search

HMODE-DLS

Hybrid Dynamic Local Search MODE

ISADE

Immune Self-Adaptive Multi-Objective Differential Evolution Algorithm

I-MODE

Improved Multi-Objective Differential Evolution

JG

Jumping Genes

MINLP

Mixed Integer Non Linear Programming

MUGA

Multicriteria Genetic Algorithm

MODE

Multi-Objective Differential Evolution

MOGA

Multi-Objective Genetic Algorithm

MOGA-II

Multi-Objective Genetic Algorithm-II

MOO

Multi-Objective Optimization

MOPSO

Multi-Objective Particle Swarm Optimization

MOSA

Multi-Objective Simulated Annealing Algorithm

NComDE

Newton Competitive Differential Evolution

NPGA

Niched-Pareto Genetic Algorithm

NSPSO

Non-Dominated Particle Swarm Optimization

NSGA

Non-Dominated Sorting Genetic Algorithm

NSGA-II

Non-Dominated Sorting Genetic Algorithm-II

NSGA-II-RJG

Non-Dominated Sorting Genetic Algorithm-II With Real Jumping Genes

ODE

Opposition Based Differential Evolution

PSO

Particle Swarm Algorithm

PET

Polyethylene Terephthalate

PBMODE

Population Based Multi-Objective Differential Evolution

MODE-RMO

Ranking-Based Mutation Multi-Objective Differential Evolution

RPCEMA

Reference Point based Competing Evolutionary Membrane Algorithm

RSM

Response Surface Method

SADE

Self-Adaptive Multi-Objective Differential Evolution Algorithm

SQP*

Sequential Quadratic Programming

SOO

Single Objective Optimization

SPEA

Strength Pareto Evolutionary Algorithm

SQP

Successive Quadratic Programming

TL

Tabu List

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Al Ani, Z., Gujarathi, A.M. & Al-Muhtaseb, A.H. A state of art review on applications of multi-objective evolutionary algorithms in chemicals production reactors. Artif Intell Rev 56, 2435–2496 (2023). https://doi.org/10.1007/s10462-022-10219-z

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