biblio

Export 13 results:
Filters: Author is Tambe, S. S.  [Clear All Filters]
Journal Article
S. M. Bhosle, Ponrathnam, S., Tambe, S. S., and Chavan, N. N., “Adsorption of strontium (II) metal ions using phosphonate-functionalized polymer”, Bulletin of Materials Science, vol. 39, no. 6, pp. 1541-1556, 2016.
R. R. Sonolikar, Patil, M. P., Mankar, R. B., Tambe, S. S., and Kulkarni, B. D., “ Bubble size prediction in gas-solid fluidized beds using genetic programming ”, Current Science , vol. 115, no. 10, pp. 1904-1912, 2018.
R. R. Sonolikar, Patil, M. P., Mankar, R. B., Tambe, S. S., and Kulkarni, B. D., “Genetic programming based drag model with improved prediction accuracy for fluidization systems”, International journal of Chemical Reactor Engineering, vol. 15, no. 2, 2017.
Y. P. Badhe, Lonari, J., Tambe, S. S., Kulkarni, B. D., Valecha, N. K., Deshmukh, S. V., and Ravichandran, S., “Improve polyethylene process control and product quality - using artificial intelligence-based sensors can improve costs”, Hydrocarbon Processing , vol. 86, no. 3, p. 53+, 2007.
K. M. Desai, Akolkar, S. K., Badhe, Y. P., Tambe, S. S., and Lele, S. S., “Optimization of fermentation media for exopolysaccharide production from lactobacillus plantarum using artificial intelligence-based techniques”, Process Biochemistry, vol. 41, no. 8, pp. 1842-1848, 2006.
R. Vyas, Goel, P., Karthikeyan, M., Tambe, S. S., and Kulkarni, B. D., “Pharmacokinetic modeling of caco-2 cell permeability using genetic programming (GP) method”, Letters in Drug Design & Discovery, vol. 11, no. 9, pp. 1112-1118, 2014.
R. Harikrishna, Ponrathnam, S., Rajan, C. R., and Tambe, S. S., “Photopolymerization of bis-aromatic and alicyclic based solid urethane acrylate macromonomer in the presence of large excess of reactive diluent Kinetics and modeling”, Journal of Thermal Analysis and Calorimetry, vol. 112, no. 2, pp. 805-813, 2013.
S. B. Ghugare, Tiwary, S., Elangovan, V., and Tambe, S. S., “Prediction of higher heating value of solid biomass fuels using artificial intelligence formalisms”, Bioenergy Research, vol. 7, no. 2, pp. 681-692, 2014.
R. Harikrishna, Ponrathnam, S., and Tambe, S. S., “Reaction kinetics and modeling of photoinitiated cationic polymerization of an alicyclic based diglycidyl ether”, Nuclear Instruments & Methods in Physics Research Section B-Beam Interactions with Materials and Atoms, vol. 318, pp. 263-268, 2014.
V. Patil-Shinde, Mulani, K. B., Donde, K., Chavan, N. N., Ponrathnam, S., and Tambe, S. S., “Removal of arsenite [As(III)] and arsenate [As(V)] ions from wastewater using TFA and TAFA resins: computational intelligence based reaction modeling and optimization”, Journal of environmental chemical engineering, vol. 4, no. 4, pp. 4275-4286, 2016.
D. V. Raje, Purohit, H. J., Badhe, Y. P., Tambe, S. S., and Kulkarni, B. D., “Self-organizing maps: a tool to ascertain taxonomic relatedness based on features derived from 16S rDNA sequence”, Journal of Biosciences, vol. 35, no. 4, pp. 617-627, 2010.
K. M. Desai, Badhe, Y. P., Tambe, S. S., and Kulkarni, B. D., “Soft-sensor development for fed-batch bioreactors using support vector regression”, Biochemical Engineering Journal, vol. 27, no. 3, pp. 225-239, 2006.
V. K. Kalyani, Pallavika,, Chaudhuri, S., T. Charan, G., Haldar, D. D., Kamal, K. P., Badhe, Y. P., Tambe, S. S., and Kulkarni, B. D., “Study of a laboratory-scale froth flotation process using artificial neural networks”, Mineral Processing and Extractive Metallurgy Review, vol. 29, no. 2, pp. 130-142, 2008.