Neural network model-based control of distillation columns

Distillation control is difficult because of its nonlinear, interactive and nonstationary behavior; but, unproved distillation control techniques can have a significant impact on improving product quality and environmental resource protection. Advanced control strategies use a model of the process to select the desired control action. While phenomenological models have demonstrated efficient control of highly nonlinear and interactive distillation colunms, they can get complicated and computationally intensive. Further, these models may require frequent reparametrization to eliminate any processmodel mismatch that may have accrued with time. Neural networks provide an alternate approach to modeling process behavior, and have received much attention because of their wide range of applicability, and their ability to handle complex and nonlinear problems. The main advantage in using neural networks is that neural network models are computationally simple, and possess enormous processing power, speed, and generality. In this study, neural network process-inverse models were developed for two different methanol-water distillation columns: (i) a lab-scale column; and (ii) an industrialscale high-purity column. The data required for "training" and "testing" the neural networks for the two distillation columns were obtained from steady-state simulations of the two distillation columns developed using a commercial steady-state simulation package. The neural networks were trained using a very efficient nonlinear optimization algorithm based on the Levenberg-Marquardt method. The neural network steady-state process-inverse models were used in conjunction with a reference system synthesis based on first-order dynamics. The neural network model-based controllers were tested on dynamic simulations of the two distillation columns for both servo and regulatory modes of operation, and their performances were compared with conventional static feedforward Proportional-Integral controllers. The simplicity and directness of the novel approach presented in this study addresses issues such as obtaining training and testing data from steady-state simulation packages, training the neural networks with a more robust and efficient nonlmear optimization algorithm, the use of steady-state process-inverse neural network models, and incorporating the model with a reference system synthesis to formulate a very simple multivariable control structure that make it distinct and better when compared with conventional Proportional-Integral controllers. The methodology offers the advantages of easy implementation and a practical solution to difficult control problems.