@conference { ISI:000382147300038,
	title = {Development of genetic programming based softsensor model for styrene polymerization process and its application in model based control},
	booktitle = {2016 Indian Control Conference (ICC)},
	year = {2016},
	note = {2nd Indian Control Conference (ICC), Indian Inst Technol, Hyderabad, INDIA, JAN 04-06, 2016},
	month = {JAN},
	pages = {238-244},
	publisher = {IEEE Control Syst Soc; Honeywell; Mathworks; ABB R \& D Div; GE; Mahindra Ecole Centrale},
	organization = {IEEE Control Syst Soc; Honeywell; Mathworks; ABB R \& D Div; GE; Mahindra Ecole Centrale},
	address = {345 E 47th ST, New York, NY 10017 USA},
	abstract = {<p>In recent years, soft sensors have been established as a valuable alternative to the traditional hardware sensors for the acquisition of critical information regarding {\textquoteleft}{\textquoteleft}difficult-to-measure{\textquoteright}{\textquoteright} 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 {\textquoteleft}{\textquoteleft}first principles{\textquoteright}{\textquoteright} 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.</p>},
	keywords = {Artificial intelligence, genetic programming, Model Predictive Control, process identification, Styrene Polymerization},
	isbn = {978-1-4673-7993-9},
	author = {Ghugare, Suhas B. and Tambe, Sanjeev S.}
}
