<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ghugare, Suhas B.</style></author><author><style face="normal" font="default" size="100%">Tambe, Sanjeev S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of genetic programming based softsensor model for styrene polymerization process and its application in model based control</style></title><secondary-title><style face="normal" font="default" size="100%">2016 Indian Control Conference (ICC)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Artificial intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">genetic programming</style></keyword><keyword><style  face="normal" font="default" size="100%">Model Predictive Control</style></keyword><keyword><style  face="normal" font="default" size="100%">process identification</style></keyword><keyword><style  face="normal" font="default" size="100%">Styrene Polymerization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE Control Syst Soc; Honeywell; Mathworks; ABB R &amp; D Div; GE; Mahindra Ecole Centrale</style></publisher><pub-location><style face="normal" font="default" size="100%">345 E 47th ST, New York, NY 10017 USA</style></pub-location><pages><style face="normal" font="default" size="100%">238-244</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4673-7993-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">2nd Indian Control Conference (ICC), Indian Inst Technol, Hyderabad, INDIA, JAN 04-06, 2016</style></notes></record></records></xml>