Prediction of chlor-alkali’s caustic current efficiency by artificial neural network; case study: A zero-gap advanced chlor-alkali cell

摘要: The progress of the membrane chlor-alkali technology resulted in a meaningful reduction energy consumption process. In this research at first step, zero-gap oxygendepolarized cell with state-of-the-art silver plated nickel screen electrode (ESNS®) was employed to consider effects various process parameters on caustic current efficiency. anode side anolyte pH, temperature, flow rate brine concentration and cathode oxygen rate, applied density are taken as parameters. At second step pre-scaled experimental data were used train artificial neural networks (ANNs). ANNs approach is estimate efficiency (CCE). training back-propagation learning algorithm several methods used. minimum error found be that Levenberg-Marquardt (LM) algorithm. Excellent prediction mean square 1.1e–4 made. The...