Comparison of Model Predictive Controller and Neural Network Controller on Nonconventional Distillation Demethanizer/Deethanizer Rectifier/Column System

Abstract

Nonconventional distillation demethanizer/deethanizer rectifier/column offers an economic advantage compared to conventional configurations by using sidestream from second column to remove the use of reboiler. However, this configuration brings a complex system that needs attention in its process control to ensure its operability. This work explores the use of neural network model predictive control (NNMPC) for this system. Aspen HYSYS was used to simulate the process, generate input-output data to develop the plant model and to conduct performance tests. MATLAB was used to conduct model identification, design the MPC and implement the multivariable control action. Sufficient time series data, where reflux valve positions and distillate compositions from each column act as input and output respectively, can be obtained from simulation and then utilized to train a nonlinear autoregressive exogenous (NARX) neural network which will be used as a predictive model. Model predictive Control based on this neural network model is designed to control distillate compositions for each column by the changes of reflux valve positions for each column as control signal. Least square algorithm from MATLAB function wass employed to optimize the cost function. A comparison has been made between NNMPC and conventional MPC configurations with first order plus dead time (FOPDT) model to test the performance of the controller based on disturbance rejection and set-point tracking test. Quantitative calculation was done using integral absolute error (IAE) with trapezoidal rule used as numerical integral method. NNMPC shows better performance than MPC with FOPDT model in set-point tracking test. However, NNMPC has a disadvantage on the cost function calculation algorithm, which did not account for the difference between plant and model value thus cause the MPC with FOPDT model to give better performance on disturbance rejection. Bias addition on NNMPC cost function algorithm made the controller has better responses to disturbance rejection compared to MPC.