Different Tools on Multi-Objective Optimization of a Hybrid Artificial Neural Network – Genetic Algorithm for Plasma Chemical Reactor Modelling

Abstract

A hybrid ANN-GA was successfully developed to model, to simulate and to optimize simultaneously a catalytic–DBD plasma reactor. The integrated ANN-GA method facilitates powerful modeling and multi-objective optimization for co-generation of synthesis gas, C2 and higher hydrocarbons from methane and carbon dioxide in a DBD plasma reactor. The hybrid approach simplified the complexity in process modeling of the DBD plasma reactor. In the ANN model, the four parameters and four targeted responses (CH4 conversion (yo1),C2 hydrocarbons selectivity (yo2), hydrogen selectivity (yo3), and C2 hydrocarbons yield (yo4) were developed and simulated. In the multi-objectives optimization, two responses or objectives were optimized simultaneously for optimum process parameters, i.e. CH4 conversion (yo1) and C2 hydrocarbons yield (yo4). Pareto optimal solutions pertaining to simultaneous CH4 conversion and C2 hydrocarbons yield and the corresponding process parameters were attained. It was found that if CH4 conversion improved, C2 hydrocarbons yield deteriorated, or vice versa. Theoretically, all sets of non-inferior/Pareto optimal solutions were acceptable. From the Pareto optimal solutions and the corresponding optimal operating parameters, the suitable operating condition range for DBD plasma reactor for simultaneous maximization of CH4 conversion and C2 hydrocarbons yield could be recommended easily. The maximum CH4 conversion and C2 hydrocarbons yield of 48 % and 15 %, respectively were recommended at corresponding optimum process parameters of CH4/CO2 feed ratio 3.6, discharge voltage 15 kV, total feed flow rate 20 cm3/min, and reactor temperature of 147 oC.