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

作者：Liu, Banghai; Jin, Chunji^*; Wan, Jiteng; Li, Pengfang; Yan, Huanxi

来源：Journal of the Serbian Chemical Society, 2018, 83(3): 379-390.

DOI：10.2298/JSC170721101L

• electrochemical oxidation
• artificial neural network
• genetic algorithm
• particle swarm optimization
• Acid Black 2

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.

• 出版日期2018
• 单位中国海洋大学