02012nas a2200205   4500008004100000020002200041245014000063210006900203260013600272300001200408520107600420653002801496653002401524653002901548653002701577653002701604100002301631700002301654856012901677       2016                        eng d  a978-1-4673-7993-900aDevelopment of genetic programming based softsensor model for styrene polymerization process and its application in model based control0 aDevelopment of genetic programming based softsensor model for st  a345 E 47th ST, New York, NY 10017 USAbIEEE Control Syst Soc; Honeywell; Mathworks; ABB R & D Div; GE; Mahindra Ecole CentralecJAN  a238-2443 a<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 ``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.</p>10aArtificial intelligence10agenetic programming10aModel Predictive Control10aprocess identification10aStyrene Polymerization1 aGhugare, Suhas, B.1 aTambe, Sanjeev, S.  uhttps://library.ncl.res.in/content/development-genetic-programming-based-softsensor-model-styrene-polymerization-process-and