Artificial neural network based identification of process dynamics and neural network controller design for continuous distillation column

In this study, an equation based nonlinear autoregressive with exogenous input neural network model for a multiple input and output process was proposed to enhance the accuracy of the model, a control system's speed, and robustness. A continuous lab-scale binary distillation column was considered for a case study, and an experiment was conducted. The forward and inverse neural network models have been developed and incor-porated into a multivariable internal model control strategy to control the distillate and bottom compositions of the column simultaneously. These models demonstrated excellent performances, as supported by lower mean square error values of 2.73E-05 and 1.26E-04 for the forward and inverse models. Then, the proposed control method was applied to control both set point changes and disturbance changes, and in each case, it is contrasted with the traditional proportional-integral-derivative (PID) technique. The integrated absolute and integrated square errors for the proposed control were 0.1603 and 2.32E-03, respectively, while for PID control, these performance indexes were 0.7822 and 8.67E-03, respectively, for set-point tracking in top composition. Simi-larly, for bottom composition in tracking set points and rejecting disturbance, these performance indexes were also very less for the neural network control scheme.