Dynamic Multi-Objective Optimization of Autocatalytic Esterification in Semi Batch by Using Control Vector Parameterization (CVP) and Non-Dominated Sorting Genetic Algorithm (NSGA-II). Issue 1 (April 2020)

• Record Type: Journal Article
• Authors: Rohman, F S; Aziz, N
• Is Part Of: IOP conference series. Volume 778:Issue 1(2020)
• Publication Date: 2020-04
• Subjects: Dynamic optimization -- Multi-objective optimization -- Autocatalytic esterification; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/778/1/012081
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: 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.