An integrated prediction model of heavy metal ion concentration for iron electrocoagulation process

Abstract

Abstract In the electrocoagulation process of the heavy metal wastewater treatment, the acquisition of the heavy metal ions concentration at outlet requires long-term analysis, resulting in delayed control of the process and many other continuing problems. This study focuses on establishing the prediction model of heavy metal ions concentration for electrocoagulation process. Based on the mechanistic analysis of the electrode reaction and the adsorption kinetics, a novel kinetics model of the electrocoagulation process is proposed. Then, an industrial condition classification and equalization method, as well as a metaheuristic method named state transition algorithm are introduced to optimize the unknown model parameters. After that, a data-based ANNs-ARIMA model is applied to compensate the errors to overcome the shortcomings of the kinetics model caused by the industrial process uncertainties. Finally, an integrated prediction model of heavy metal ion concentration is established by connecting the kinetics model and the compensation model in parallel. All the experimental results showed that the predicted values of the kinetics model roughly agree with the actual industrial data, but the prediction accuracy of integrated model reaches to 90% and the mean relative errors is within ± 8.2 % , which indicates that the integrated model is highly accurate and has better predictive capacity. Especially when the industrial conditions fluctuate violently, the advantages of the integrated model can be more demonstrated.