Deep Learning Based System Identification and Nonlinear Model Predictive Control of pH Neutralization Process

An essential step in the progression of nonlinear system identification is the inception of recurrent and convolution-type deep learning methods in industrial units. Many chemical/pharmaceutical/wastewater process units employ pH neutralization schemes to check the acidity and alkalinity of the product before bringing them for industrial production. The inherent nonlinear dynamics, especially during the neutralization of strong acid by a strong base, pose rigorous difficulties to model uncertainties, making it highly challenging to implement automatic pH control. This research endeavor focuses on building a deep Temporal Convolution Network (TCN) with a larger receptive field to learn the dynamics of the pH neutralization process. This method uses a 1-D causal convolution strategy with a residual learning framework to perform dilated causal convolutions. The simulation studies of the proposed TCN-based identification-scheme are adopted and executed in a Python environment for two important case studies, namely, (a) single tank pH process and (b) ETP (effluent treatment plant)-pH process. The proposed TCN framework manifested supremacy over predicted model responses of LSTM (long short-term memory) and MLP (multilayer-perceptron) architectures in terms of accuracy while requiring less training time. Furthermore, in this research, a state-of-art TCN-based NMPC (nonlinear model predictive control) and LSTM-based NMPC schemes are designed, implemented, and investigated where the control performance revealed the precision of the former.