Development of an artificial neural network model for prediction of cell voltage and current efficiency in a chlor-alkali membrane cell

Abstract

This paper presents the development of an artificial neural network (ANN) model for the prediction of cell voltage and caustic current efficiency (CCE) versus various operating parameters in a lab scale chlor-alkali membrane cell. 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 studied. The membrane cell incorporated a standard DSA/Cl 2 electrode as the anode, a nickel electrode as the cathode and a Flemion 892 polymer film as the membrane. Each of the six process parameters including 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/m 2), and run time were thoroughly studied at four levels and low caustic concentrations (5–22 g/L). The predictions of ANN model as well as those from other statistical methods were evaluated versus the measured values of cell voltages. The developed ANN model is not only capable to predict the cell voltage and caustic current efficiency but also to reflect the impacts of process parameters on the same functions. The predicted cell voltages and current efficiencies using ANN modeling were found to be close to the measured values, particularly at higher current densities.