Biodegradable iron chelate for H2S abatement: modeling and optimization using artificial intelligence strategies - Hamid, Aashti; Deshpande, Aniruddha S.; Badhe, Yogesh P.; Barve, Prashant P.; Tambe, Sanjeev S.; Kulkarni, Bhaskar D. (Chemical Engineering Research & Design, 2014)
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.
Keywords: Artificial immune systems; Artificial neural networks; Batch reactor; Genetic algorithms; Sensitivity analysis

Behaviour of biomass multicomponent ashes as adsorbents - Deokar, Sunil K.; Mandavgane, Sachin A.; Kulkarni, Bhaskar D. (Current Science, 2016)
Physico-chemical characteristics of rice husk ash and baggase fly ash, commonly referred to as biomass ashes enable their use as adsorbents. Contrary to normal expectations, it is observed that larger particles have more number, narrower and deeper pores than smaller particles. As a consequence they have higher pore volume, total surface area and hence adsorption capacity. Also, the uptake rate of adsorption depends on the silica to carbon ratio, which is seen to be smaller for larger particles and hence they take a longer time to reach equilibrium. The extent of carbon content determines the capacity, whereas silica to carbon ratio determines the kinetics of adsorption. Removal of 2,4-dichlorophenoxyacetic acid, from aqueous solution was chosen as a representative case for study and the results obtained are compared with earlier reported results.
Keywords: Adsorption capacity; bagasse; biomass ash; rice husk; silica to carbon ratio

Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis - Vyas, Renu; Bapat, Sanket; Jain, Esha; Karthikeyan, Muthukumarasamy; Tambe, Sanjeev; Kulkarni, Bhaskar D. (Computational Biology and Chemistry, 2016)
In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein protein interactions therefore assume significance. 1296 binary fingerprints that encode a combination of structural and geometric properties were developed using the crystallographic data of 15,000 protein complexes in the pdb server. In a case study, these fingerprints were created for proteins implicated in the Type 2 diabetes mellitus disease. The fingerprints were input into a SVM based model for discriminating disease proteins from non disease proteins yielding a classification accuracy of 78.2% (AUC value of 0.78) on an external data set composed of proteins retrieved via text mining of diabetes related literature. A PPI network was constructed and analysed to explore new disease targets. The integrated approach exemplified here has a potential for identifying disease related proteins, functional annotation and other proteomics studies. (C) 2016 Elsevier Ltd. All rights reserved.