Prediction of cell voltage and current efficiency in a lab scale chlor-alkali membrane cell based on support vector machines

Abstract

The main aim of this study is to investigate the impacts of operating parameters on the cell performance and predicting the same by SVM technique. This paper though introduces support vector machines (SVMs), a relatively new powerful machine learning method based on statistical learning theory (SLT), into cell voltage and current efficiency forecasting. In order to validate the model predictions, the effects of various operating parameters on the cell voltage and current efficiency of the membrane cell were experimentally investigated. The membrane cell included a standard DSA/Cl2 electrode as the anode, a nickel electrode as the cathode and a Flemion 892 polymer film as the membrane. Each of six process parameters counting anolyte pH (2–5), operating temperature (25–90 °C), electrolyte velocity (2.2–5.9 cm/s), brine concentration (200–300 g/L), current density (1–4 kA/m2), and run time were thoroughly studied at four levels for low caustic concentrations (5–22 g/L). The developed SVM model is not only capable to predict the cell voltage and caustic current efficiency (CCE) but also to reflect the impacts of process parameters on the same functions. The predicted cell voltages and current efficiencies using SVM modelling were found to be very close to the measured values, particularly at higher current densities.