@article { ISI:000290357000002,
	title = {Modeling \& simulation of micro reactor with nitration of 2-methyl-4,6 dihydroxy-pyrimidine},
	journal = {Science and Technology of Energetic Materials},
	volume = {72},
	number = {1-2},
	year = {2011},
	month = {JAN},
	pages = {9-20},
	publisher = {JAPAN EXPLOSIVES SOC},
	address = {C/O JAPAN EXPLOSIVES INDUSTRY ASSOC, ICHIJOJI BLDG., 2-3-22 AZABUDAI, MINATO-KU, TOKYO, 106-0041, JAPAN},
	abstract = {<p>Nitration of 2-methyl-4,6-dihydroxypyrimidine (MDP) using concentrated sulfuric acid and nitric acid as nitrating mixture is a highly exothermic and hazardous reaction. Conducting such reaction in a batch reactor follow an unsteady state and its trajectory depends on various important parameters such as initial reactor temperature, initial composition of reaction mass, temperature of circulating coolant, etc. However, over all productivity, process control and safety of the batch process is highly restricted due to lower surface to volume ratio. In the present work, an effort has been made to over come the limitations of batch reactor by using the novel micro reactor device. Micro reactor is having extremely high surface to volume ratio, which has been explored to carry out nitration of MDP both numerically as well as experimentally and the results were compared with conventional batch reactor. The micro reaction system has been modeled using two dimensional (2-D) heat flow and mass transfer equations. The kinetic rate equation for nitration of MDP has evaluated experimentally by differential method which is used in modeling of the micro reactor. The numerical results from the 2-D model for conversion and temperature profile along the length and radius of micro reactor have been compared with corresponding results obtained from batch reactor. In order to validate the model, several experiments were conducted in micro reactor set-up with the variation of flow rate, residence time, concentration, temperature, etc. The experimental results from micro reactor revealed that nitration of MDP takes place even at much lower concentration and lower residence time with better control of temperature profile. Also, the reaction takes place in laminar region compared to turbulent region in corresponding batch reactor setup.</p>},
	keywords = {2-D modeling, 2-methyl-4, 6 dihydroxypyrimidine, Batch reactor, micro reactor},
	issn = {1347-9466},
	author = {Mandal, Alok Kumar and Pandey, Raj Kishore and Asthana, Nandan and Kulkarni, Amol and Kulkarni, Bhaskar Dattatraya}
}
@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.}
}
