<?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%">Hamid, Aashti</style></author><author><style face="normal" font="default" size="100%">Deshpande, Aniruddha S.</style></author><author><style face="normal" font="default" size="100%">Badhe, Yogesh P.</style></author><author><style face="normal" font="default" size="100%">Barve, Prashant P.</style></author><author><style face="normal" font="default" size="100%">Tambe, Sanjeev S.</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Bhaskar D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Biodegradable iron chelate for H2S abatement: modeling and optimization using artificial intelligence strategies</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%">Artificial immune systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Artificial neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Batch reactor</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JUN</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">6</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%">92</style></volume><pages><style face="normal" font="default" size="100%">1119-1132</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 batch reactor process for the abatement of a common pollutant, namely, H2S using Fe3+-malic acid chelate (Fe3+-MA) catalyst has been developed. Further, process modeling and optimization was conducted in the three stages with a view to maximize the H2S conversion: (i) sensitivity analysis of process inputs was performed to select the most influential process operating variables and parameters, (ii) an artificial neural network (ANN)-based data-driven process model was developed using the influential process variables and parameters as model inputs, and H2S conversion (%) as the model output, and (iii) the input space of the ANN model was optimized using the artificial immune systems (AIS) formalism. The AIS is a recently proposed stochastic nonlinear search and optimization method based on the human biological immune system and has been introduced in this study for chemical process optimization. The AIS-based optimum process conditions have been compared with those obtained using the genetic algorithms (GA) formalism. The AIS-optimized process conditions leading to high (approximate to 97%) H2S conversion, were tested experimentally and the results obtained thereby show an excellent match with the AIS-maximized H2S conversion. It was also observed that the AIS required lesser number of generations and function evaluations to reach the convergence when compared with the GA. (C) 2013 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Verma, Devendra</style></author><author><style face="normal" font="default" size="100%">Goel, Purva</style></author><author><style face="normal" font="default" size="100%">Patil-Shinde, Veena</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%">Use genetic programming for selecting predictor variables and modeling in process identification</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%">dynamic model</style></keyword><keyword><style  face="normal" font="default" size="100%">genetic programming</style></keyword><keyword><style  face="normal" font="default" size="100%">predictor variable</style></keyword><keyword><style  face="normal" font="default" size="100%">process identification</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity analysis</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%">230-237</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;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.&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><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%">Ghuge, Pravin D.</style></author><author><style face="normal" font="default" size="100%">Mali, Nilesh A.</style></author><author><style face="normal" font="default" size="100%">Sirsam, R. S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Study of the effect of operating parameters on the extractive distillation of isopropyl alcohol–water mixture using dimethyl sulphoxide as an entrainer</style></title><secondary-title><style face="normal" font="default" size="100%">Indian Chemical Engineer</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aspen Plus Simulators</style></keyword><keyword><style  face="normal" font="default" size="100%">Azeotrope</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer software</style></keyword><keyword><style  face="normal" font="default" size="100%">Distillation</style></keyword><keyword><style  face="normal" font="default" size="100%">Energy requirements</style></keyword><keyword><style  face="normal" font="default" size="100%">Energy utilization</style></keyword><keyword><style  face="normal" font="default" size="100%">Extractive distillation</style></keyword><keyword><style  face="normal" font="default" size="100%">Isopropyl alcohol/water</style></keyword><keyword><style  face="normal" font="default" size="100%">Isopropyl alcohols</style></keyword><keyword><style  face="normal" font="default" size="100%">Operating parameters</style></keyword><keyword><style  face="normal" font="default" size="100%">Optimum operating conditions</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">SimulationSteady-state simulations</style></keyword><keyword><style  face="normal" font="default" size="100%">Solvents</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">MAY</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">60</style></volume><pages><style face="normal" font="default" size="100%">141-161</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Abstract: In this paper, the simulation analysis of separation of isopropyl alcohol (IPA)–water via extractive distillation by using dimethyl sulphoxide as an entrainer is presented. The steady-state simulation for this process was done using Aspen Plus simulator. The aim of this work is to study the effect of various operating parameters such as number of stages, molar reflux ratio, binary feed stage number, entrainer feed stage number, temperature of entrainer feed and entrainer to feed molar ratio on the purity and energy consumption. The sensitivity of these parameters serves as a basis to choose the optimum operating conditions to maximize the IPA purity and minimize the energy requirement. In this analysis, among various operating parameters, those which directly affect the IPA purity and the parameters which affect the reboiler duty, for the given separation task, are identified. Simulation results with optimum operating conditions and their economic analysis are also presented.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><work-type><style face="normal" font="default" size="100%">Journal Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Indian&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;0.145&lt;/p&gt;</style></custom4><section><style face="normal" font="default" size="100%">1-21</style></section></record></records></xml>