@article { ISI:000337885300011,
	title = {Biodegradable iron chelate for H2S abatement: modeling and optimization using artificial intelligence strategies},
	journal = {Chemical Engineering Research \& Design},
	volume = {92},
	number = {6},
	year = {2014},
	month = {JUN},
	pages = {1119-1132},
	publisher = {INST CHEMICAL ENGINEERS},
	type = {Article},
	address = {165-189 RAILWAY TERRACE, DAVIS BLDG, RUGBY CV21 3HQ, ENGLAND},
	abstract = {<p>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.</p>},
	keywords = {Artificial immune systems, Artificial neural networks, Batch reactor, Genetic algorithms, Sensitivity analysis},
	issn = {0263-8762},
	doi = {10.1016/j.cherd.2013.10.017},
	author = {Hamid, Aashti and Deshpande, Aniruddha S. and Badhe, Yogesh P. and Barve, Prashant P. and Tambe, Sanjeev S. and Kulkarni, Bhaskar D.}
}
@conference { ISI:000382147300037,
	title = {Use genetic programming for selecting predictor variables and modeling in process identification},
	booktitle = {2016 Indian Control Conference (ICC)},
	year = {2016},
	note = {2nd Indian Control Conference (ICC), Indian Inst Technol, Hyderabad, INDIA, JAN 04-06, 2016},
	month = {JAN},
	pages = {230-237},
	publisher = {IEEE Control Syst Soc; Honeywell; Mathworks; ABB R \& D Div; GE; Mahindra Ecole Centrale},
	organization = {IEEE Control Syst Soc; Honeywell; Mathworks; ABB R \& D Div; GE; Mahindra Ecole Centrale},
	address = {345 E 47th ST, New York, NY 10017 USA},
	abstract = {<p>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.</p>},
	keywords = {dynamic model, genetic programming, predictor variable, process identification, Sensitivity analysis},
	isbn = {978-1-4673-7993-9},
	author = {Verma, Devendra and Goel, Purva and Patil-Shinde, Veena and Tambe, Sanjeev S.}
}
