Hybrid model using bond graph-TCN network and event triggered predictive control of pH neutralization process

The pH neutralization process is a concrete decisive unit in major reactor units of industrial process control loops. This article presents a new fuzzy-based hybrid ‘Bond Graph-Temporal Convolution Network’ (BG-TCN) model, structured for the convoluted dynamics of a real-time pH neutralization unit, known for its complexity and high nonlinearity. The TCN scheme suggested in this article, exploits a one-dimensional causal convolution strategy within a residual learning framework to execute dilated causal convolutions through time series data analysis. Conversely, Bond Graph (BG) is a graphical tool, designed on an energy-centric approach, to represent energy transfer and interactions across different compartments of the nonlinear pH neutralization system. Furthermore, a linguistic fuzzy rule -based inference system is encompassed to handle uncertainties from BG and TCN models, allowing smooth integration and flexible transition between these two approaches. Additionally, the performance of the hybrid BG-TCN model is assessed against the individual TCN and BG models in a Python environment. On top of that, this article also envisions an event-triggered predictive control utilizing a fuzzy event handler mechanism to demonstrate the efficacy of the proposed hybrid BG-TCN in attaining precise set point tracking for closed-loop servo and regulatory problems. • A deep Temporal Convolution Network (TCN) scheme is proposed that allows one-dimensional causal convolution for dilated causal convolutions within a residual learning framework. • A new fuzzy-based hybrid ‘Bond Graph-Temporal Convolution Neural Network’(BG-TCN) model is proposed for automatic-decision making. • An energy flow based pseudo bond graph method, TCN and BG-TCN methods are applied to predict the nonlinear dynamics of a complex real-time pH neutralization process. • Fuzzy rule-based inference system adeptly integrates BG and TCN information for predicting nonlinear dynamics. • A state-of-art Event-triggered predictive control with fuzzy event handler demonstrates hybrid BG-TCN effectiveness in closed-loop servo and regulatory tracking for pH neutralization process.