Optimization of Predicted Cell Voltage & Caustic Current Efficiency in a Chlor-Alkali Membrane Cell with Application of Genetic Algorithm

Abstract

This paper presents the development of hybrid neural-genetic algorithm model for the prediction of cell voltage and caustic current efficiency (CCE) versus various operating parameters in a lab scale chlor-alkali membrane cell. Each of six process parameters including anolyte pH (2-5), operating temperature (25-90 oC), electrolyte velocity (1.3-5.9 cm/s), brine concentration (200-300 g/L), current density (1-4 kA/m2), and run time (up to 150 min) were thoroughly studied at four levels for low caustic concentrations (5 g/L). According to the obtained results, the predicted cell voltages and current efficiencies using GA modeling were found to be very close to the measured values with an average deviation of about 2.82% & 1.77% for test validation data, respectively. It was also found that the developed model is not only capable to predict the voltage and current efficiency but also to reflect the impacts of the other process parameters on the same functions.