<?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><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%">Mehta, S.</style></author><author><style face="normal" font="default" size="100%">Ramani, H.</style></author><author><style face="normal" font="default" size="100%">Yelgatte, N. N.</style></author><author><style face="normal" font="default" size="100%">Rahman, I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Recursive orthogonal least square based soft sensor for batch distillation</style></title><secondary-title><style face="normal" font="default" size="100%">Chemical product and process modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">SEP</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">241-263</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A multiple-input and multiple-output (MIMO) model, namely Recursive Orthogonal Least Square (ROLS) based radial basis function (RBF) is developed to estimate product compositions in a batch distillation process from temperature measurements. The process data is generated by simulating the differential equations of the batch distillation process, changing the initial feed composition and boiluprate from batch to batch. Moreover, the reflux ratio is also randomly varied within each batch to represent the exact dynamics of the batch distillation. Temperature and distillate composition is correlated by the RBF trained by ROLS algorithm. A Single RBF network estimate the quality of products in real-time. The results show that ROLS based estimator give correct composition estimations for a batch distillation process. The robustness of the ROLS algorithm and low computational requirement makes the estimator attractive for on-line use. </style></abstract><issue><style face="normal" font="default" size="100%">3</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%"> 0.347</style></custom4></record><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%">Adjay Sagar, A.</style></author><author><style face="normal" font="default" size="100%">Rahman, I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimization of pressure-Swing distillation by evolutionary techniques: separation of ethanol-water and acetonitrile-water mixtures</style></title><secondary-title><style face="normal" font="default" size="100%">Chemical Product and Process Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JUN</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">13</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Complete separation can be achieved in selective homogeneous azeotropic mixtures by exploiting the pressure sensitive nature of the system. In the present work the optimal number of trays, feed location and reflux ratio for sequential column systems encountered in continuous pressure swing distillation (PSD) have been determined by use of two evolutionary techniques. Two industrially relevant systems: ethanol-water and acetonitrile-water have been considered. The Napthali-Sandholm model is solved to obtain the concentration and temperature profiles. The objective is to minimize the total cost using Genetic Algorithm (GA) and Differential Evolution (DE) for the two azeotropic systems. The techniques offer attractive features like applicability to discontinuous and non-differentiable search spaces.</style></abstract><issue><style face="normal" font="default" size="100%">2</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%">0.44</style></custom4></record><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%">Tiwari, S.</style></author><author><style face="normal" font="default" size="100%">Sawant, P.</style></author><author><style face="normal" font="default" size="100%">Rahman, I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Recursive orthogonal least squares based adaptive control of a polymerisation reactor</style></title><secondary-title><style face="normal" font="default" size="100%">Indian Chemical Engineer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">NOV</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A Nonlinear Internal Model Control (NIMC) scheme, based on a recursively updated Radial Basis Function Network (RBFN), is applied to control the nonlinear Polymerisation process. Recursive Orthogonal Least Squares (ROLS) algorithm recursively updates the weighting matrix of RBFN in real time, such that modelling errors are minimised. In addition to the ROLS based adaptive IMC, a non-adaptive IMC, based on the fixed Orthogonal Least Squares (OLS) algorithm and a normal PID controller are also applied to control the polymerisation process. The simulated results show the effectiveness of the ROLS based controller in terms of response time to step changes and disturbance rejection capabilities over the classical PID controller and the fixed OLS based IMC. Also, the proposed algorithm fits well in the IMC framework</style></abstract><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%">0.29</style></custom4></record></records></xml>