Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network

This paper introduces a new approach to controlling Pressure Swing Adsorption (PSA) using neural network controller based on Model Predictive Control (MPC) process. We use Hammerstein–Wiener (HW) model representing the real PSA process data. Then, we design an MPC-controlled HW maintain bioethanol purity near 99% molar fraction. work proposes Artificial Neural Network (ANN) that captures dynamics of controlled by MPC strategy. Both controllers are validated process, showing great performance and robustness against disturbances. The results show can follow desired trajectory attenuate disturbances, achieving at fraction value 0.99 ANN strategy with 94% fit in control signal 97% signal, so conclude our be used disturbances