Parameters optimization in Chlor-Alkali ion-exchange membrane electrolysis process with artificial neural network

Abstract

Since ion-exchange membrane electrolysis cell has developed for producing Chlor-Alkali products. New�higher efficiency and lower consumption�technology are released from licensors yearly, which made the process correlation deviate from the original design. The machine learning�is used with “Neural Network Fitting Tool (nftool)” in MATLAB. To find a correlation between 5 inputs consisting of current density (CD, KA/m2), operation day (DOL, day), feed brine flow rate (QFB, m3/h), feed caustic flow rate (QHD, m3/h),�cell temperature (T, degC) and one output which is cell voltage (CV, V). Datasets were collected from�the plant information management system “exaquantum” historian database. The result is shown only on CD as the predictor gives RMSE at 0.0167 V. In 2 predictors, DOL as the second gave RMSE at 0.0065 V, which can conclude that DOL (or clogging factor) has the most impact on CV increasing. In 3 predictors, T as the third gave RMSE at 0.0043 V, from controlled temperature set point change. Developed ANN optimization model can be used to optimize controlled parameters to predict suitable CV after a long run (high DOL) or to compare electrolysis effectiveness by regressing CV for comparing at the same condition.