Artificial neural networks applied to potentiometric titration of multi-component polybasic acid mixtures

A three-layer artificial neural network model with back-propagation of error is used to treat potentiometric acid-base titration data for estimating the concentrations of individual components in polybasic weak acid mixtures. The network's architecture and parameters were optimized and an empirical rule for dynamically adjusting the learning rate is put forward to improve the network's performance. Satisfactory prediction results were obtained for three-component samples containing maleic acid, propandioic acid and succinic acid with an average relative error of 4.5%.