Model Predictive Inferential Control of a Distillation Column

Abstract

Typical production objectives in distillation process require the delivery of products whose compositions meet certain specifications. The distillation control system, therefore, must hold product compositions as near the set points as possible in faces of upset. In this project, inferential model predictive control, that utilizes an artificial neural network estimator and model predictive controller, is developed for an industrial multicomponent distillation column. First, composition control by direct measuring composition is used. This method because of large sampling delay has a poor performance. The selection of the temperature measurement points is done for indirect control of the column. The use of temperature loop leads to an offset in the composition; due to the fact that the temperature set-point must be changed when feed disturbances occurred. An artificial neural network estimator is designed to estimate the product compositions from tray temperature measurements. A model predictive controller is used to control column composition based on composition estimates. The performance of the developed inferential model predictive control system is tested for set-point tracking and load rejection.