Shojai - Research Lab for Advanced Separation Processes, Department of Chemical Engineering, Iran University of Science and Technology, Narmak, Tehran ۱۶۸۴۶, Iran
Ashrafizadeh - Research Lab for Advanced Separation Processes, Department of Chemical Engineering, Iran University of Science and Technology, Narmak, Tehran ۱۶۸۴۶, Iran
Mohammadi - Iran Polymer and Petrochemical Institute, Tehran, Iran

This paper presents the comparison of artificial neural network (ANN) and Support Vector Machine (SVM) models 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 CCE of the membrane cell were experimentally investigated. Each of six process parameters including anolyte pH (2-5), operating temperature (25-90 o C), electrolyte velocity (1.3-5.9 cm/s), brine concentration (200-300 g/L), current density (1-4 kA/m 2 ), and run time (up to 150 min) were thoroughly studied at four levels for low caustic concentrations (5 g/L). The predictions of ANN & SVM models as well as those from other statistical methods were evaluated against the measured values. It was found that the developed ANN & SVM models are not only capable to predict the voltage and CCE but also to reflect the impacts of process parameters on the same functions. The predicted cell voltages and current efficiencies using these models were found to be close to the measured values with an average deviation of only 1.27% for predicted cell voltages with ANN and 1.98% for CCE with SVM.

Chlor-Alkali, Membrane Cell, Electrolysis, Artificial Neural Network, Support Vector Machine