<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jain, P.</style></author><author><style face="normal" font="default" size="100%">Rahman, I.</style></author><author><style face="normal" font="default" size="100%">Kulkarni, B. D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of a soft sensor for a batch distillation column using support vector regression techniques</style></title><secondary-title><style face="normal" font="default" size="100%">Chemical Engineering Research &amp; Design </style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Batch distillation</style></keyword><keyword><style  face="normal" font="default" size="100%">composition estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">soft sensor</style></keyword><keyword><style  face="normal" font="default" size="100%">support vector regression</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">FEB</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">A2</style></number><publisher><style face="normal" font="default" size="100%">INST CHEMICAL ENGINEERS</style></publisher><pub-location><style face="normal" font="default" size="100%">165-189 RAILWAY TERRACE, DAVIS BLDG, RUGBY CV21 3HQ, ENGLAND</style></pub-location><volume><style face="normal" font="default" size="100%">85</style></volume><pages><style face="normal" font="default" size="100%">283-287</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A support vector regression (SVR)-based model is developed for a batch distillation process in order to estimate the product compositions from temperature measurements. Kernel function such as linear, polynomial and RBF are employed for SVR modelling. The original process data was generated by simulating the batch distillation process, varying the initial feed composition and boilup rate from batch to batch. Within each batch reflux ratio was also randomly changed to represent the true dynamics of the batch distillation. The results show the potential of the method for developing softsensor for chemical processes.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">A2</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">2.525</style></custom4></record></records></xml>