Optimization of ethylene oxychlorination fluidized-bed reactor using Differential Evolution (DE) method

Abstract

Abstract. The present work aims to employ Di erential Evolution (DE) algorithm to optimize ethylene oxychlorination process to produce 1,2-dichloroethane in a uidized bed reactor as a feedstock of PVC production. A steady-state reactor model, based on twophase theory of uidization, was developed to investigate the e ects of various parameters on C 2 H 4 and HCl conversions. The model's results were compared favorably with the industrial data obtained from a pilot plant working in Italy. The feed temperature, pressure, HCl and O2 molar ow rates, and cooling medium temperature were selected as decision variables to minimize the objective function subject to the environmental constraints. The highest performance was found at HCl/C2H4 and O2/C2H4 molar ratios of 2 and 0.55, respectively; feed and cooling medium temperatures of 440 and 360 K, respectively; pressure of 367.6 kPa. The results show a decrease of 20 C in the feed temperature, which leads to saving energy and reducing the size of the pre-heater.

A Hybrid Model of Multi-Objective Differential Evolution Algorithm and Various Decision-making Methods to Optimize the Batch ABE Fermentation Process

“…The DE method is a stochastic optimization algorithm for minimizing an objective function subject to a number of linear or nonlinear constraints. DE has been successfully used by several researchers such as Babu et al [30][31][32] and Khademi et al [33][34][35][36] in various elds. After that, MODE algorithm, as an extended DE method, proposed by Babu et al [37] has been successfully used to handle multi-objective optimization problems.…”