Applications of neural networks for distillation control

Date

1996-05

Journal Title

Journal ISSN

Volume Title

Publisher

Texas Tech University

Abstract

Distillation control is difificult because of its nonlinear, interactive, and nonstationary behavior; but, improved distillation control techniques can have a significant impact on improving product quality and protecting environmental resources. 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 columns, they are often computationally intensive.

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 network models is that they are simple, and computationally extremely efficient.

In this study, neural networks were used as models in an advanced model-based control framework. Feedforward neural network models were developed using both steady-state and dynamic data to model three distiUation case studies: (i) a propylenepropane (C3) splitter; (ii) a toluene-xylene splitter; and (ui) an industrial multicomponent distillation column. Rigorous simulators were developed for these three processes which provided the data for training the networks. The neural networks were trained using a nonlinear optimization algorithm.

Description

Keywords

Neural networks (Computer science), Distillation

Citation