Application of adaptive neurofuzzy control using soft sensors to continuous distillation

Abstract

Recent years have seen a rapidly growing number of hybrid neurofuzzy based applications in the process engineering field, covering estimation, modeling and control among others. In fact, proper operation of a distillation column requires knowledge of products compositions during the entire duration of the operation. The use of inferential composition estimators (soft sensors) has long been suggested to assist the monitoring and control of continuous distillation columns. In this paper we describe the application of an adaptive network based fuzzy inference system (ANFIS) predictor to the estimation of the product compositions in a binary methanol-water continuous distillation column from available on-line temperature measurements. This soft sensor is then applied to the composition dual control of the distillation column. Genetic algorithms are used to automatically selection of the optimum control signal based on an ANFIS model of the plant. The performance of the developed ANFIS estimator is further tested by observing the performance of the dual control system for both set point tracking and disturbance rejection cases. © 2008 Elsevier B.V. All rights reserved.