Multi-objective dynamic optimization study of fed-batch bio-reactor

被引:21
|
作者
Patel, Narendra [1 ]
Padhiyar, Nitin [2 ]
机构
[1] Vishwakarma Govt Engn Coll, Dept Chem Engn, Ahmadabad 382424, Gujarat, India
[2] Indian Inst Technol Gandhinagar, Dept Chem Engn, Gandhinagar 382355, Gujarat, India
来源
关键词
Multi-objective optimization; Mesh sort; Fed-batch reactor; Dynamic optimization; GENETIC ALGORITHM; MULTICRITERIA OPTIMIZATION; DIFFERENTIAL EVOLUTION; 2ND-ORDER INFORMATION; ENGINEERING PROBLEMS; FERMENTATION; BIOREACTOR;
D O I
10.1016/j.cherd.2017.01.002
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Evolutionary algorithms are widely used for dynamic optimization problems of fed-batch bio-reactors for productivity-yield maximization by optimizing the substrate feed recipe. However, this is usually done for a fixed fed-batch time. Conventionally, the optimum fed batch time is computed by solving several single objective dynamic optimization problems for different fed-batch time. Since this approach is computationally quite expensive, we propose a Multi-Objective Optimization (MOO) problem formulation to find the optimum fed-batch time for maximizing productivity and/or yield. Such an MOO approach is expected to save significant computational efforts. To demonstrate the proposed MOO implementations for dynamic optimization of fed-batch bio-reactors, secreted protein production is considered as a case study. Specifically, four distinct objectives, namely productivity, yield, fed-batch time, and endpoint substrate concentration are considered in this work. An evolutionary multi-objective differential evolution algorithm is used for solving the MOO problems. (C) 2017 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:160 / 170
页数:11
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