02521nas a2200265   4500008004100000022001400041245011700055210006900172260009500241300001400336490000700350520150500357653003001862653003101892653001801923653002301941653002501964100001801989700002902007700002202036700002402058700002302082700002602105856012402131       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.  uhttp://library.ncl.res.in/content/biodegradable-iron-chelate-h2s-abatement-modeling-and-optimization-using-artificial-002502nas a2200289   4500008004100000022001400041245014800055210007100203260000800274300000900282490000700291520147100298653003101769653002001800653003101820653002701851653002401878653003001902100002001932700002301952700002401975700001601999700001902015700001802034700002302052856013702075       2018                        eng d  a1877-264100aCo-gasification of high ash coal–biomass blends in a fluidized bed gasifier: experimental study and computational intelligence-based modeling0 aCogasification of high ash coal–biomass blends in a fluidized be  cJUN  a1-190 v113 aCo-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.10aArtificial neural networks10aCo-gasification10aComputational intelligence10aFluidized bed gasifier10agenetic programming10asupport vector regression1 aTiwary, Shishir1 aGhugare, Suhas, B.1 aChavan, Prakash, D.1 aSaha, Sujan1 aDatta, Sudipta1 aSahu, Gajanan1 aTambe, Sanjeev, S.  uhttp://library.ncl.res.in/content/co-gasification-high-ash-coal%E2%80%93biomass-blends-fluidized-bed-gasifier-experimental-study-and