College of Environment, Zhejiang University of Technology, Hangzhou 310032, China.
College of Biological and Environmental Engineering, Zhejiang Shuren University, Hangzhou 310005, China.
Int J Environ Res Public Health. 2019 Jul 10;16(14):2454. doi: 10.3390/ijerph16142454.
Electro-oxidation is an effective approach for the removal of 2-chlorophenol from wastewater. The modeling of the electrochemical process plays an important role in improving the efficiency of electrochemical treatment and increasing our understanding of electrochemical treatment without increasing the cost. The backpropagation artificial neural network (BP-ANN) model was applied to predict chemical oxygen demand (COD) removal efficiency and total energy consumption (TEC). Current density, pH, supporting electrolyte concentration, and oxidation-reduction potential (ORP) were used as input parameters in the 2-chlorophenol synthetic wastewater model. Prediction accuracy was increased by using particle swarm optimization coupled with BP-ANN to optimize weight and threshold values. The particle swarm optimization BP-ANN (PSO-BP-ANN) for the efficient prediction of COD removal efficiency and TEC for testing data showed high correlation coefficient of 0.99 and 0.9944 and a mean square error of 0.0015526 and 0.0023456. The weight matrix analysis indicated that the correlation of the five input parameters was a current density of 18.85%, an initial pH 21.11%, an electrolyte concentration 19.69%, an oxidation time of 21.30%, and an ORP of 19.05%. The analysis of removal kinetics indicated that oxidation-reduction potential (ORP) is closely correlated with the chemical oxygen demand (COD) and total energy consumption (TEC) of the electro-oxidation degradation of 2-chlorophenol in wastewater.
电化学氧化是去除废水中 2-氯苯酚的有效方法。电化学过程的建模对于提高电化学处理效率和增加对电化学处理的理解（而无需增加成本）起着重要作用。反向传播人工神经网络 (BP-ANN) 模型被应用于预测化学需氧量 (COD) 去除效率和总能耗 (TEC)。电流密度、pH 值、支持电解质浓度和氧化还原电位 (ORP) 被用作 2-氯苯酚合成废水模型的输入参数。通过使用粒子群优化与 BP-ANN 相结合来优化权重和阈值，可以提高预测精度。粒子群优化 BP-ANN (PSO-BP-ANN) 可高效预测 COD 去除效率和 TEC 的测试数据，相关系数分别为 0.99 和 0.9944，均方误差分别为 0.0015526 和 0.0023456。权重矩阵分析表明，五个输入参数的相关性为电流密度 18.85%、初始 pH 值 21.11%、电解质浓度 19.69%、氧化时间 21.30%和氧化还原电位 19.05%。去除动力学分析表明，氧化还原电位 (ORP) 与废水中 2-氯苯酚的电化学氧化降解的化学需氧量 (COD) 和总能耗 (TEC) 密切相关。
