@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.}
}
@article {44432,
	title = {Co-gasification of high ash coal{\textendash}biomass blends in a fluidized bed gasifier: experimental study and computational intelligence-based modeling},
	journal = {Waste and Biomass Valorization},
	volume = {11},
	year = {2018},
	month = {JUN},
	pages = {1-19},
	type = {Article},
	abstract = {Co-gasification (COG) is a clean-coal technology that uses a binary blend of coal and biomass for generating the product gas; it is environment-friendly since it emits lesser quantities of pollutants compared to the coal gasification process. Although coals found in many countries contain high percentages of ash, co-gasification studies involving such coals, and the process modeling thereof, are rare. Accordingly, this study presents results of the co-gasification experiments conducted in a fluidized-bed gasifier (FBG) pilot plant using as a feed the blends of high ash Indian coals with three biomasses, namely, rice husk, press mud, and sawdust. Since the underlying physicochemical phenomena are complex and nonlinear, modeling of the COG process has been performed using three computational intelligence (CI)-based methods namely, genetic programming, artificial neural networks, and support vector regression. Each of these formalisms was employed separately to develop models predicting four COG performance variables, namely, total gas yield, carbon conversion efficiency, heating value of product gas, and cold gas efficiency. All the CI-based models exhibit an excellent prediction accuracy and generalization performance. The co-gasification experiments and their modeling presented here for a pilot-plant FBG can be gainfully utilized in the efficient design and operation of the corresponding commercial scale co-gasifiers utilizing high ash coals.},
	keywords = {Artificial neural networks, Co-gasification, Computational intelligence, Fluidized bed gasifier, genetic programming, support vector regression},
	issn = {1877-2641},
	doi = {10.1007/s12649-018-0378-7},
	author = {Tiwary, Shishir and Ghugare, Suhas B. and Chavan, Prakash D. and Saha, Sujan and Datta, Sudipta and Sahu, Gajanan and Tambe, Sanjeev S.}
}
