Abstract
Multi-objective optimization is used in many chemical engineering fields that have conflict objective functions. Prevaporation is an effective process for removing trace or minor amount of the component of diluting solutions. This process is used for dehydration of alcohols containing small amounts of water. In this process membrane flux and separation factor have conflict with each other. So a multi-objective optimization approach can be used for optimization of the process. In this paper, in first stage a neural network based model was developed for preparation conditions for polyetherimide membrane in isopropanol prevaporation. Four major variables involved in the membrane preparation procedure, including polymer concentration, additive content, solvent evaporation temperature and time was considered. Membrane flux and separation factor were considered as objective functions. Elitist Non-dominated sorting genetic algorithm with jumping gene and altruistic adaptation (Alt-NSGA-aJG) was applied for simultaneous maximization of flux and separation factor. Pareto optimal solutions for membrane preparation conditions and effect of decision variables (four preparation variables) on Pareto front were investigated.
References
1. Huang RY (ed.), Pervaporation membrane separation processes. Amsterdam: Elsevier, 1991.10.1016/0958-2118(91)90003-DSearch in Google Scholar
2. Okamoto K, Semoto T, Tanaka K, Kita H. Application of pervaporation to phenol-acetone condensation reaction. Chem Lett 1991;1:167–74.10.1246/cl.1991.167Search in Google Scholar
3. Yanagishita H, Nakane T, Nozoye H, Yoshitome H. Preparation of asymmetric polyimide membrane for water/ ethanol separation by chemical vapor deposition and polymerization technique (CVDP). J Appl Polym Sci 1992;49:565–75.10.1002/app.1993.070490402Search in Google Scholar
4. Ribeiro CP, Freeman BD, Kalika DS, Kalakkunnath S. Aromatic polyimide and polybenzoxazole membranes for the fractionation of aromatic/aliphatic hydrocarbons by pervaporation. J Membr Sci 2012;390–91:182–93.10.1016/j.memsci.2011.11.042Search in Google Scholar
5. Deb K. Multi-objective optimization using evolutionary algorithms. Chichester: John Wiley & Sons, 2001.Search in Google Scholar
6. Sharma S, Rangaiah GP. Multi-objective optimization applications in chemical engineering. In: Rangaiah GP, Bonilla-Petriciolet A, editors. Multi-objective optimization in chemical engineering: developments and applications. UK: John Wiley & Sons, 2013.10.1002/9781118341704.ch3Search in Google Scholar
7. Deb K, Pratap A, Agarwal S, Meyarivan TA. Fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 2002;6:182–97.10.1109/4235.996017Search in Google Scholar
8. Kasat RB, Gupta SK. Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using genetic algorithm (GA) with the jumping genes operator. Comput Chem Eng 2003;27:1785–800.10.1016/S0098-1354(03)00153-4Search in Google Scholar
9. Ramteke M, Gupta SK. Biomimicking altruistic behavior of honey bees in multi-objective genetic algorithm. Ind Eng Chem Res 2009;48:9671–85.10.1021/ie9004817Search in Google Scholar
10. Sharma S, Nabavi SR, Rangaiah GP. Jumping gene adaptations of NSGA-II with altruism approach: performance comparison and application to Williams–Otto process. In: Valadi J, Siarry P, editors. Applications of metaheuristics in process engineering. New York: Springer, 2014.Search in Google Scholar
11. Huang RY, Feng X. Dehydration of isopropanol by pervaporation using aromatic polyetherimide membranes. Separ Sci Technol 1993;28: 2035–47.10.1080/01496399308016732Search in Google Scholar
12. Feng X, Huang RY. Preparation and performance of asymmetric polyetherimide membranes for isopropanol dehydration by pervaporation. J Membr Sci 1996;109:165–71.10.1016/0376-7388(95)00198-0Search in Google Scholar
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