Development of genetic programming based softsensor model for styrene polymerization process and its application in model based control

TitleDevelopment of genetic programming based softsensor model for styrene polymerization process and its application in model based control
Publication TypeConference Paper
Year of Publication2016
AuthorsGhugare, SB, Tambe, SS
Conference Name2016 Indian Control Conference (ICC)
Date PublishedJAN
PublisherIEEE Control Syst Soc; Honeywell; Mathworks; ABB R & D Div; GE; Mahindra Ecole Centrale
Conference Location345 E 47th ST, New York, NY 10017 USA
ISBN Number978-1-4673-7993-9
KeywordsArtificial intelligence, genetic programming, Model Predictive Control, process identification, Styrene Polymerization
Abstract

In recent years, soft sensors have been established as a valuable alternative to the traditional hardware sensors for the acquisition of critical information regarding ``difficult-to-measure'' process variables and/or parameters in chemical process monitoring and control. Soft sensors can also be modified as a novel process identification tool for process monitoring and model based control. Often, in polymer industries the main polymerization reaction is highly nonlinear and complex to model accurately by the conventional ``first principles'' approach. In such cases, genetic programming (GP)-a novel artificial intelligence-based exclusively data driven modeling technique-can be employed for process identification. In this work GP-based soft sensors have been developed for a continuous styrene polymerization reactor. The resulting GP-based models (soft sensor) showed high prediction and generalization performances. The best performing model was successfully utilized in designing a model predictive control (MPC) scheme for the polymerization reactor.

Divison category: 
Chemical Engineering & Process Development