02522nas a2200265 4500008004100000022001400041245011700055210006900172260009500241300001400336490000700350520150500357653003001862653003101892653001801923653002301941653002501964100001801989700002902007700002202036700002402058700002302082700002602105856012502131 2014 eng d a0263-876200aBiodegradable iron chelate for H2S abatement: modeling and optimization using artificial intelligence strategies0 aBiodegradable iron chelate for H2S abatement modeling and optimi a165-189 RAILWAY TERRACE, DAVIS BLDG, RUGBY CV21 3HQ, ENGLANDbINST CHEMICAL ENGINEERScJUN a1119-11320 v923 a

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.

10aArtificial immune systems10aArtificial neural networks10aBatch reactor10aGenetic algorithms10aSensitivity analysis1 aHamid, Aashti1 aDeshpande, Aniruddha, S.1 aBadhe, Yogesh, P.1 aBarve, Prashant, P.1 aTambe, Sanjeev, S.1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/biodegradable-iron-chelate-h2s-abatement-modeling-and-optimization-using-artificial-002333nas a2200229 4500008004100000020002200041245010100063210006900164260013600233300001200369520139300381653001801774653002401792653002301816653002701839653002501866100002001891700001601911700002401927700002301951856012901974 2016 eng d a978-1-4673-7993-900aUse genetic programming for selecting predictor variables and modeling in process identification0 aUse genetic programming for selecting predictor variables and mo a345 E 47th ST, New York, NY 10017 USAbIEEE Control Syst Soc; Honeywell; Mathworks; ABB R & D Div; GE; Mahindra Ecole CentralecJAN a230-2373 a

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.

10adynamic model10agenetic programming10apredictor variable10aprocess identification10aSensitivity analysis1 aVerma, Devendra1 aGoel, Purva1 aPatil-Shinde, Veena1 aTambe, Sanjeev, S. uhttps://library.ncl.res.in/content/use-genetic-programming-selecting-predictor-variables-and-modeling-process-identification02227nas a2200349 4500008004100000022001400041245015800055210006900213260000800282300001200290490000700302520101400309653002601323653001401349653002201363653001701385653002401402653002301426653002801449653002801477653002301505653002501528653003301553653002501586653001501611653003901626653001301665100002201678700002101700700001901721856013701740 2017 eng d a0019-450600aStudy of the effect of operating parameters on the extractive distillation of isopropyl alcohol–water mixture using dimethyl sulphoxide as an entrainer0 aStudy of the effect of operating parameters on the extractive di cMAY a141-1610 v603 a

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.

10aAspen Plus Simulators10aAzeotrope10aComputer software10aDistillation10aEnergy requirements10aEnergy utilization10aExtractive distillation10aIsopropyl alcohol/water10aIsopropyl alcohols10aOperating parameters10aOptimum operating conditions10aSensitivity analysis10asimulation10aSimulationSteady-state simulations10aSolvents1 aGhuge, Pravin, D.1 aMali, Nilesh, A.1 aSirsam, R., S. uhttps://library.ncl.res.in/content/study-effect-operating-parameters-extractive-distillation-isopropyl-alcohol%E2%80%93water-mixture