Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0098-1354(00)00665-7
Title: Multiobjective optimization of an industrial wiped film poly(ethylene terephthalate) reactor: Some further insights
Authors: Bhaskar, V.
Gupta, S.K.
Ray, A.K. 
Keywords: Fibre grade polyester
Genetic algorithm
Multiobjective optimization
Pareto sets
Polyethylene terephthalate
Wiped-film reactor
Issue Date: 15-Mar-2001
Citation: Bhaskar, V., Gupta, S.K., Ray, A.K. (2001-03-15). Multiobjective optimization of an industrial wiped film poly(ethylene terephthalate) reactor: Some further insights. Computers and Chemical Engineering 25 (2-3) : 391-407. ScholarBank@NUS Repository. https://doi.org/10.1016/S0098-1354(00)00665-7
Abstract: Multiobjective optimization of an industrial third-stage, wiped-film poly(ethylene terephthalate) reactor is carried out, using a pre-validated model. The two objective functions minimized are the acid and vinyl end group concentrations in the product. These are two of the undesirable side products produced in the reactor. The optimization problem incorporates an end-point constraint to produce polymer having a desired value of the degree of polymerization (DP). In addition, the concentration of the di-ethylene glycol end group in the product is constrained to lie within a certain range of values. The possible decision variables for the problem are the reactor pressure, temperature, catalyst concentration, residence time of the reaction mass in the reactor and the speed of rotation of the agitator. The nondominated sorting genetic algorithm (NSGA) is used to solve this multiobjective optimization problem. It is found that this algorithm is unable to converge to the correct solution(s) when two or more decision variables are used, and we need to run the code several times over (with different values of the computational variable, Sr, the seed for generating the random numbers) to obtain the solutions. In fact, this is an excellent test problem for future multiobjective optimization algorithms. It is found that when temperature is kept constant, Pareto optimal solutions are obtained, while, when the temperature is included as a decision variable, a global unique optimal point is obtained. © 2001 Elsevier Science Ltd.
Source Title: Computers and Chemical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/92158
ISSN: 00981354
DOI: 10.1016/S0098-1354(00)00665-7
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