Open AccessDynamic Multi-Objective Optimization of Autocatalytic Esterification in Semi Batch by Using Control Vector Parameterization (CVP) and Non-Dominated Sorting Genetic Algorithm (NSGA-II)
Author(s) -
Fakhrony Sholahudin Rohman,
N. Aziz
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/778/1/012081
Subject(s) - sorting , mathematical optimization , multi objective optimization , pareto principle , genetic algorithm , process (computing) , set (abstract data type) , optimization problem , mathematics , computer science , algorithm , programming language , operating system
Catalyzed Esterification of sec-butyl propionate in semi batch reactor prefers to be solved by dynamic-nonlinear programming (NLP) based optimization for determining optimal temperature and feed flowrate trajectories. In this autocatalytic esterification process, there are contrary objective functions, i.e. maximum productivity and minimum process time. Simultaneous optimization of these objectives yields in a dynamic multi-objective optimization (DMOO) problem, which is characterized by a set of multiple solutions, known as non-dominated or Pareto solutions. In this work, a control vector parameterization (CVP) and non-dominated sorting genetic algorithm (NSGA-II) approach were used to generate the Pareto solutions for two objectives: maximize conversion and minimize process time. Each point of Pareto solutions consists of different optimal temperature reactor and feed rate profiles, which lead to a variation combination of conversion and process time. These solutions give multiple alternatives in evaluating the trade-offs and selecting the most suitable operating policy.