@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.}
}
@conference { ISI:000382147300037,
	title = {Use genetic programming for selecting predictor variables and modeling in process identification},
	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 = {230-237},
	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>Availability of an accurate and robust dynamic model is essential for implementing the model dependent process control. When first principles based modeling becomes difficult, tedious and/or costly, a dynamic model in the black-box form is obtained (process identification) by using the measured input-output process data. Such a dynamic model frequently contains a number of time delayed inputs and outputs as predictor variables. The determination of the specific predictor variables is usually done via a trial and error approach that requires an extensive computational effort. The computational intelligence (CI) based data-driven modeling technique, namely, genetic programming (GP) can search and optimize both the structure and parameters of a linear/nonlinear dynamic process model. It is also capable of choosing those predictor variables that significantly influence the model output. Thus usage of GP for process identification helps in avoiding the extensive time and efforts involved in the selection of the time delayed input-output variables. This advantageous GP feature has been illustrated in this study by conducting process identification of two chemical engineering systems. The results of the GP-based identification when compared with those obtained using the transfer function based identification clearly indicates the outperformance by the former method.</p>},
	keywords = {dynamic model, genetic programming, predictor variable, process identification, Sensitivity analysis},
	isbn = {978-1-4673-7993-9},
	author = {Verma, Devendra and Goel, Purva and Patil-Shinde, Veena and Tambe, Sanjeev S.}
}
