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Received February 2, 2020
Accepted July 19, 2020
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Hybridized multi-objective optimization approach (HMODE) for lysine fed-batch fermentation process
Department of Petroleum & Chemical Engineering, College of Engineering, Sultan Qaboos University, P. O. Box 33, Al-Khod, P.C. 123, Muscat, Sultanate of Oman
ashishgujrathi@gmail.com, ashishg@squ.edu.om
Korean Journal of Chemical Engineering, January 2021, 38(1), 8-21(14), 10.1007/s11814-020-0642-y
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Abstract
A new hybrid multi-objective differential evolution (MODE) algorithm is proposed that combines the MODE algorithm for the global space search with a dynamical local search (DLS) method for the local space search. HMODE-DLS algorithm was validated using the tri-objective DTLZ7 test problem and the results were compared with MODE algorithm with respect to four performance metrics. In addition to HMODE-DLS, another three algorithms were used to solve two multi-objective optimization cases in an industrial lysine bioreactor at different feeding conditions. Case 1 considers maximizing lysine’s productivity and yield. While case 2 studies the maximization of productivity along with minimization of total operating time. In all cases, theoretical and industrial, HMODE-DLS showed a better performance with a better quality Pareto set of solutions. The Pareto front of case 1 found by HMODE-DLS was compared with a recent study trade-off, and the current non-dominated solutions values were found to be improved. This indicates that the lysine production process is enhanced. For case 2, the switching time from fed-batch to batch was found to be the key decision variable. Generally, these findings indicate the effectiveness of HMODE-DLS for the studied cases and its potential in solving real world complex problems.
Keywords
References
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Kim TY, Park JM, Kim HU, Cho KM, Lee SY, Metab. Eng., 28, 63 (2015)
Sarkar D, Modak JM, Chem. Eng. Sci., 60(2), 481 (2005)
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Chen X, Du W, Qian F, Chemom. Intell. Lab. Syst., 136, 85 (2014)
Qu BY, Liang JJ, Zhu YS, Wang ZY, Suganthan PN, Inf. Sci., 351, 48 (2016)
Wang YN, Wu LH, Yuan XF, Soft Comput., 14, 193 (2010)
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Deb K, Pratap A, Agarwal S, Meyarivan T, IEEE Trans. Evol. Comput., 6, 182 (2002)
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Coello CAC, Lechuga MS, Proceedings of the 2002 congress on evolutionary computation, Honolulu, HI, USA, 12-17 May (2002).
Ohno H, Nakanishi E, Takamatsu T, Biotechnol. Bioeng., 18, 847 (1976)
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Gujarathi AM, Babu BV, Chem. Eng. Sci., 65(6), 2009 (2010)
Gujarathi AM, Babu BV, Mater. Manuf. Processes, 24, 303 (2009)
Gujarathi AM, Babu BV, Int. J. Comput. Int. Stud., 2, 157 (2013)
Zitzler E, Evolutionary algorithms for multiobjective optimization: Methods and applications, Citeseer, Switzerland (1999).
Deb K, Multi-objective optimization using evolutionary algorithms, Wiley & Sons, England (2002).
Lee ESQ, Rangaiah GP, Ind. Eng. Chem. Res., 48(16), 7662 (2009)
