Modeling and optimizing an electrochemical oxidation process using artificial neural network, genetic algorithm and particle swarm optimization

• Published in: Journal of the Serbian Chemical Society Vol. 83; no. 3; pp. 379 - 390
• Main Authors: Liu, Banghai, Jin, Chunji, Wan, Jiteng, Li, Pengfang, Yan, Huanxi
• Format: Journal Article
• Language: English
• Published: Belgrade Journal of the Serbian Chemical Society 2018; Serbian Chemical Society
• Subjects: Acid Black 2; artificial neural network; Artificial neural networks; Back propagation networks; Current efficiency; Electrochemical oxidation; Energy consumption; genetic algorithm; Genetic algorithms; Mathematical models; Neural networks; Organic carbon; Oxidation; Particle swarm optimization; Process parameters
• ISSN: 0352-5139; 1820-7421; 1820-7421
• DOI: 10.2298/JSC170721101L

This study proposes a novel hybrid of artificial neural network (ANN), genetic algorithm (GA), and particle swarm optimization (PSO) to model and optimize the relevant parameters of an electrochemical oxidation (EO) Acid Black 2 process. The back propagation neural network (BPNN) was used as a modelling tool. To avoid over-fitting, GA was applied to improve the generalized capability of BPNN by optimizing the weights. In addition, an optimization model was developed to assess the performance of the EO process, where total organic carbon (TOC) removal, mineralization current efficiency (MCE), and the energy consumption per unit of TOC (ECTOC) were considered. The operation conditions of EO were further optimized via PSO. The validation results indicted the proposed method to be a promising method to estimate the efficiency and to optimize the parameters of the EO process. nema