02812nas a2200253   4500008004100000022001400041245009500055210006900150260013500219300000900354490000700363520185400370653001702224653001502241653002602256653001802282653001802300100002402318700002502342700002002367700001902387700003402406856011802440       2011                        eng d  a1347-946600aModeling & simulation of micro reactor with nitration of 2-methyl-4,6 dihydroxy-pyrimidine0 aModeling simulation of micro reactor with nitration of 2methyl46  aC/O JAPAN EXPLOSIVES INDUSTRY ASSOC, ICHIJOJI BLDG., 2-3-22 AZABUDAI, MINATO-KU, TOKYO, 106-0041, JAPANbJAPAN EXPLOSIVES SOCcJAN  a9-200 v723 a<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>10a2-D modeling10a2-methyl-410a6 dihydroxypyrimidine10aBatch reactor10amicro reactor1 aMandal, Alok, Kumar1 aPandey, Raj, Kishore1 aAsthana, Nandan1 aKulkarni, Amol1 aKulkarni, Bhaskar, Dattatraya  uhttps://library.ncl.res.in/content/modeling-simulation-micro-reactor-nitration-2-methyl-46-dihydroxy-pyrimidine-002522nas 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<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>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-0