@article { ISI:000238398000019, title = {Optimization of fermentation media for exopolysaccharide production from lactobacillus plantarum using artificial intelligence-based techniques}, journal = {Process Biochemistry}, volume = {41}, number = {8}, year = {2006}, month = {AUG}, pages = {1842-1848}, publisher = {ELSEVIER SCI LTD}, type = {Article}, address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}, abstract = {

A Lactobacillus strain was isolated from the fermented Eleusine coracana. This strain was characterized as Lactobacillus plantarum and was found to produce an exopolysaccharide (EPS) in quantitative amounts. The objective of the present paper is to determine optimum media composition and inoculum volume for the stated fermentative production of the EPS. A hybrid methodology comprising the Plackett-Burman (PB) design method, artificial neural networks (ANN) and genetic algorithms (GA) was utilized. Specifically, the PB, ANN and GA forrnalisms were used for identifying influential media components, modeling non-linear process and optimizing the process, respectively. More specifically, the PB method was used to determine those media components, which significantly influence the EPS yield. By ignoring the less influential media components, the dimensionality of the input space of the process model could be reduced significantly. Out of the five media components only three were found influential namely, lactose, casein hydrolysate and triammonium citrate. Next, an ANN-based process model was developed for approximating the non-linear relationship between the fermentation operating variables and the EPS yield. The average \% error and correlation coefficient for the developed ANN model were 4.8 and 0.999, respectively. The input parameters of ANN model were subsequently optimized using the GA formalism for obtaining maximum EPS yield in batch fermentation. The optimized media composition has predicted the yield of 7.01 g/l. The GA-optimized solution comprising media composition and inoculum volume was verified experimentally and it comes out be 7.14 g/l. (c) 2006 Elsevier Ltd. All rights reserved.

}, keywords = {Artificial neural network, exopolysaccharide, Fermentation, Genetic algorithm, Lactobacillus plantarum, Media optimization, Plackett-Burman}, issn = {1359-5113}, doi = {10.1016/j.procbio.2006.03.037}, author = {Desai, Kiran M. and Akolkar, S. K. and Badhe, Yogesh P. and Tambe, S. S. and Lele, S. S.} } @article {24}, title = {Soft-sensor development for fed-batch bioreactors using support vector regression}, journal = {Biochemical Engineering Journal}, volume = {27}, number = {3}, year = {2006}, note = {Conference on Atomic Molecular and Optical Physics, Calcutta, INDIA, DEC 13-15, 2005}, month = {JAN}, pages = {225-239}, publisher = {Indian Assoc Cultivat Sci}, type = {Article}, address = {DR K S KRISHNAN MARG, NEW DELHI 110 012, INDIA}, abstract = {

In the present paper, a state-of-the-art machine learning based modeling formalism known as "support vector regression (SVR)", has been introduced for the soft-sensor applications in the fed-batch processes. The SVR method possesses a number of attractive properties such as a strong statistical basis, convergence to the unique global minimum and an improved generalization performance by the approximated function. Also, the structure and parameters of an SVR model can be interpreted in terms of the training data. The efficacy of the SVR formalism for the soft-sensor development task has been demonstrated by considering two simulated bio-processes namely, invertase and streptokinase. Additionally, the performance of the SVR based soft-sensors is rigorously compared with those developed using the multilayer perceptron and radial basis function neural networks. The results presented here clearly indicate that the SVR is an attractive alternative to artificial neural networks for the development of soft-sensors in bioprocesses.

}, keywords = {deprotection, Microwave, stannous chloride, tert-butyldimethylsilyl ethers}, isbn = {978-981-270-379-8}, issn = {0376-4699}, doi = {10.1016/j.bej.2005.08.002}, author = {Desai, Kiran M. and Badhe, Yogesh P. and Tambe, S. S. and Kulkarni, B. D.} } @article { ISI:000244902800037, title = {Improve polyethylene process control and product quality - using artificial intelligence-based sensors can improve costs}, journal = {Hydrocarbon Processing }, volume = {86}, number = {3}, year = {2007}, month = {MAR}, pages = {53+}, publisher = {GULF PUBL CO}, type = {Article}, address = {BOX 2608, HOUSTON, TX 77252-2608 USA}, issn = {0018-8190}, author = {Badhe, Yogesh P. and Lonari, J. and Tambe, S. S. and Kulkarni, B. D. and Valecha, N. K. and Deshmukh, S. V. and Ravichandran, S.} } @article { ISI:000252354300005, title = {Study of a laboratory-scale froth flotation process using artificial neural networks}, journal = {Mineral Processing and Extractive Metallurgy Review}, volume = {29}, number = {2}, year = {2008}, month = {DEC}, pages = {130-142}, publisher = {TAYLOR \& FRANCIS INC}, address = {325 CHESTNUT ST, SUITE 800, PHILADELPHIA, PA 19106 USA}, abstract = {

A three-layer feed-forward artificial neural network (ANN) model, trained using the error back propagation algorithm, has been established to simulate the froth flotation process for the beneficiation of coal fines. The network model validates the experimentally observed qualitative and quantitative trends. The optimal model parameters in terms of network weights have been estimated and can be used to compute the parameters of the coal flotation process over wide-ranging experimental conditions.

}, keywords = {back propagation algorithm, froth flotation, laboratory-scale, neural network}, issn = {0882-7508}, doi = {10.1080/08827500701421912}, author = {Kalyani, V. K. and Pallavika and Chaudhuri, Sanjay and Charan, T. Gouri and Haldar, D. D. and Kamal, K. P. and Badhe, Yogesh P. and Tambe, S. S. and Kulkarni, B. D.} } @article { ISI:000285027100017, title = {Self-organizing maps: a tool to ascertain taxonomic relatedness based on features derived from 16S rDNA sequence}, journal = {Journal of Biosciences}, volume = {35}, number = {4}, year = {2010}, month = {DEC}, pages = {617-627}, publisher = {INDIAN ACAD SCIENCES}, address = {C V RAMAN AVENUE, SADASHIVANAGAR, P B \#8005, BANGALORE 560 080, INDIA}, abstract = {

Exploitation of microbial wealth, of which almost 95\% or more is still unexplored, is a growing need. The taxonomic placements of a new isolate based on phenotypic characteristics are now being supported by information preserved in the 16S rRNA gene. However, the analysis of 16S rDNA sequences retrieved from metagenome, by the available bioinformatics tools, is subject to limitations. In this study, the occurrences of nucleotide features in 16S rDNA sequences have been used to ascertain the taxonomic placement of organisms. The tetra- and penta-nucleotide features were extracted from the training data set of the 16S rDNA sequence, and was subjected to an artificial neural network (ANN) based tool known as self-organizing map (SOM), which helped in visualization of unsupervised classification. For selection of significant features, principal component analysis (PCA) or curvilinear component analysis (CCA) was applied. The SOM along with these techniques could discriminate the sample sequences with more than 90\% accuracy, highlighting the relevance of features. To ascertain the confidence level in the developed classification approach, the test data set was specifically evaluated for Thiobacillus, with Acidiphilium, Paracocus and Starkeya, which are taxonomically reassigned. The evaluation proved the excellent generalization capability of the developed tool. The topology of genera in SOM supported the conventional chemo-biochemical classification reported in the Bergey manual.

}, keywords = {Curvilinear component analysis, Principal component analysis, self-organizing maps}, issn = {0250-5991}, doi = {10.1007/s12038-010-0070-y}, author = {Raje, D. V. and Purohit, H. J. and Badhe, Yogesh P. and Tambe, S. S. and Kulkarni, B. D.} } @article { ISI:000318292400036, title = {Photopolymerization of bis-aromatic and alicyclic based solid urethane acrylate macromonomer in the presence of large excess of reactive diluent Kinetics and modeling}, journal = {Journal of Thermal Analysis and Calorimetry}, volume = {112}, number = {2}, year = {2013}, month = {MAY}, pages = {805-813}, publisher = {SPRINGER}, address = {VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS}, abstract = {

A solid urethane acrylate macromonomer with bis-aromatic as well as alicyclic moieties was synthesized and the kinetics of photopolymerization reactions were studied in the presence of varying concentration of photoinitiator and large excess of reactive diluent using photo DSC. The studies show that the rate of maximum polymerization was found to increase with increase in concentration of photoinitiator while a decrease was observed by an increase in temperature. The final conversion showed a decrease at highest isothermal condition due to vitrification. Estimation of kinetic parameters including applicability of autocatalytic and modified autocatalytic models were investigated by nonlinear regression. It was observed that the modified models gave a better fit with the experimental data and kinetic parameters showed a decrease with increase in temperature and an increase with increase in concentration of photoinitiator.

}, keywords = {Autocatalytic model, kinetics, Levenberg-Marquardt method, Photopolymerization}, issn = {1388-6150}, doi = {10.1007/s10973-012-2619-5}, author = {Harikrishna, Reghunathan and Ponrathnam, S. and Rajan, C. R. and Tambe, S. S.} } @article { ISI:000341643300008, title = {Pharmacokinetic modeling of caco-2 cell permeability using genetic programming (GP) method}, journal = {Letters in Drug Design \& Discovery}, volume = {11}, number = {9}, year = {2014}, month = {NOV}, pages = {1112-1118}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

An accurate prediction of the pharmacokinetic properties of orally administered drugs is of paramount importance in pharmaceutical industry. Caco-2 cell permeability is a well established parameter for assessing the drug absorption profiles of lead molecules. Due to the restrictions on animal testing, prohibitive in situ models and ethical issues, the development of predictive models is essential. Genetic programming (GP) is an artificial intelligence (AI)-based exclusively data driven modeling paradigm. Given an example input-output data, it searches and optimizes, both the structure and parameters of a well fitting linear/non-linear input-output model. Despite this novelty, GP has not been widely exploited in drug design. Accordingly, in this study we propose a GP based approach for the in silico prediction of Caco-2 cell permeability using a diverse set of molecules. The predictions yielded a high magnitude for the training and test set correlation coefficient with low RMSE, indicating accurate Caco-2 permeability prediction and generalization performance by the GP model. The predictions were better or comparable to artificial neural networks (ANN) and support vector regression (SVR) methods. The GP based modeling approach illustrated will find diverse applications in (QSAR, QSPR and QSTR) modeling for the virtual screening of large libraries.

}, keywords = {ADME modeling, Caco-2 cell permeability, genetic programming, MLP, SVR}, issn = {1570-1808}, author = {Vyas, Renu and Goel, Purva and Karthikeyan, Muthukumarasamy and Tambe, S. S. and Kulkarni, B. D.} } @article { ISI:000335400800021, title = {Prediction of higher heating value of solid biomass fuels using artificial intelligence formalisms}, journal = {Bioenergy Research}, volume = {7}, number = {2}, year = {2014}, month = {JUN}, pages = {681-692}, publisher = {SPRINGER}, address = {233 SPRING ST, NEW YORK, NY 10013 USA}, abstract = {

The higher heating value (HHV) is an important property defining the energy content of biomass fuels. A number of proximate and/or ultimate analysis based predominantly linear correlations have been proposed for predicting the HHV of biomass fuels. A scrutiny of the relationships between the constituents of the proximate and ultimate analyses and the corresponding HHVs suggests that all relationships are not linear and thus nonlinear models may be more appropriate. Accordingly, a novel artificial intelligence (AI) formalism, namely genetic programming (GP) has been employed for the first time for developing two biomass HHV prediction models, respectively using the constituents of the proximate and ultimate analyses as the model inputs. The prediction and generalization performance of these models was compared rigorously with the corresponding multilayer perceptron (MLP) neural network based as also currently available high-performing linear and nonlinear HHV models. This comparison reveals that the HHV prediction performance of the GP and MLP models is consistently better than that of their existing linear and/or nonlinear counterparts. Specifically, the GP- and MLP-based models exhibit an excellent overall prediction accuracy and generalization performance with high (\> 0.95) magnitudes of the coefficient of correlation and low (\< 4.5 \%) magnitudes of mean absolute percentage error in respect of the experimental and model-predicted HHVs. It is also found that the proximate analysis-based GP model has outperformed all the existing high-performing linear biomass HHV prediction models. In the case of ultimate analysis-based HHV models, the MLP model has exhibited best prediction accuracy and generalization performance when compared with the existing linear and nonlinear models. The AI-based models introduced in this paper due to their excellent performance have the potential to replace the existing biomass HHV prediction models.

}, keywords = {Artificial intelligence, Artificial neural networks, Biomass fuels, genetic programming, Higher heating value, Multilayer perceptron}, issn = {1939-1234}, doi = {10.1007/s12155-013-9393-5}, author = {Ghugare, Suhas B. and Tiwary, S. and Elangovan, V. and Tambe, S. S.} } @article { ISI:000329385800009, title = {Reaction kinetics and modeling of photoinitiated cationic polymerization of an alicyclic based diglycidyl ether}, journal = {Nuclear Instruments \& Methods in Physics Research Section B-Beam Interactions with Materials and Atoms}, volume = {318}, number = {B}, year = {2014}, month = {JAN}, pages = {263-268}, publisher = {ELSEVIER SCIENCE BV}, address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}, abstract = {

Photoinitiated cationic polymerization of cycloaliphatic diepoxides had received tremendous attention, while studies with lesser polymerizable diglycidyl ethers are comparatively less reported. The present work deals with the photoinitiated cationic polymerization of cyclohexane dimethanol diglycidyl ether followed by estimation of kinetic parameters. The effects of concentration of photoinitiator and temperature on curing performance were studied using photo differential scanning calorimeter or photo DSC with polychromatic radiation. It was observed that the rate of polymerization as well as ultimate conversion increased with increasing concentration of photoinitiator and temperature. The influences of gelation as well as diffusional restrictions have remarkable effect on cure performance. The kinetic parameters as per autocatalytic kinetic model were studied by Levenberg-Marquardt nonlinear regression method instead of conventional linear method for obtaining more accurate values of apparent rate constant. It was observed that the model fits with data from initial stages to almost towards the end of the reaction. The activation energy was found to be higher than the values reported for more reactive cycloaliphatic diepoxides. The value of pre-exponential factor increased with increase in activation energy showing influence of gelation at early stages of reaction. (C) 2013 Elsevier B.V. All rights reserved.

}, keywords = {Autocatalytic model, kinetics, Levenberg-Marquardt method, Photopolymerization}, issn = {0168-583X}, doi = {10.1016/j.nimb.2013.10.004}, author = {Harikrishna, Reghunathan and Ponrathnam, S. and Tambe, S. S.} } @article {43588, title = {Adsorption of strontium (II) metal ions using phosphonate-functionalized polymer}, journal = {Bulletin of Materials Science}, volume = {39}, year = {2016}, month = {OCT}, pages = {1541-1556}, abstract = {Diethyl[3-(methoxydimethylsilyl)propyl]phosphonate (DMPP) polymer was synthesized for the strontium (II) metal ion recovery using diethylallylphosphonate as staring material. Diethylallylphosphonate was reacted with poly(methylhydro)siloxane (MW 1900-2000 g mol (-1) ) in the presence of Speier{\textquoteright}s catalyst. The synthesized monomer was characterized by IR, (1) H NMR, (1 3) C NMR and FT-IR spectroscopy techniques, and the synthesized polymers were characterized by IR and NMR spectroscopy, differential scanning calorimetry, thermogravimetric analysis and solubility. The synthesized polymer was used for sequestering strontium metal from the aqueous solution. The metal binding was examined by the energy dispersive spectroscopy and scanning electron microscopy for the adsorbed Sr(II). Batch adsorption studies were performed by varying three parameters, namely initial pH, adsorbent dose and the contact time. The reaction kinetics was determined by the Langmuir, Freundlich, and pseudo-first- and second-order models. Results of this study indicate that the synthesized polymer DMPP has been effective in removing Sr(II) from the aqueous solution.}, doi = {10.1007/s12034-016-1295-7}, author = {Bhosle, S. M. and Ponrathnam, S. and Tambe, S. S. and Chavan, N. N.} } @article {45877, title = {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 = {Journal of environmental chemical engineering}, volume = {4}, year = {2016}, month = {DEC}, pages = {4275-4286}, type = {Article}, abstract = {Being significantly toxic, removal of arsenic forms an important part of the drinking- and waste-water treatment. Tannin is a polyphenol-rich substrate that efficiently and adsorptively binds to the multivalent metal ions. In this study, tannin-formaldehyde (TFA) and tannin-aniline-formaldehyde (TAFA) resins were synthesized and employed successfully for an adsorptive removal of arsenite [As(III)] and arsenate [As(V)] ions from the contaminated water. Next, a computational intelligence (CI) based hybrid strategy was used to model and optimize the resin-based adsorption of As(III) and As(V) ions for securing optimal reaction conditions. This strategy first uses an exclusively reaction data driven modeling strategy, namely, genetic programming (GP) to predict the extent (\%) of As(III)/As(V) adsorbed on TFA and TAFA resins. Next, the input space of the GP-based models consisting of the reaction condition variables/parameters was optimized using genetic algorithm (GA) method; the objective of this optimization was to maximize the adsorption of As(III) and As(V) ions on the two resins. Finally, the sets of optimal reaction conditions provided by GP-GA hybrid method were verified experimentally the results of which indicate that the optimized conditions have lead to 0.3\% and 1.3\% increase in the adsorption of As(III) and As(V) ions on TFA resin. More significantly, the optimized conditions have increased the adsorption of As(III) and As(V) on TAFA resin by 3.02\% and 12.77\%, respectively. The GP-GA based strategy introduced here can be gainfully utilized for modeling and optimization of similar type of contaminant-removal processes. }, doi = {10.1016/j.jece.2016.09.030}, author = {Patil-Shinde, V. and Mulani, K. B. and Donde, K. and Chavan, N. N. and Ponrathnam, S. and Tambe, S. S.} } @article {43918, title = {Genetic programming based drag model with improved prediction accuracy for fluidization systems}, journal = {International journal of Chemical Reactor Engineering}, volume = {15}, year = {2017}, month = {APR}, issn = {2016-0210}, doi = {10.1515/ijcre-2016-0210}, author = {Sonolikar, R. R. and Patil, M. P. and Mankar, R. B. and Tambe, S. S. and Kulkarni, B. D.} } @article {45468, title = { Bubble size prediction in gas-solid fluidized beds using genetic programming }, journal = {Current Science }, volume = {115}, year = {2018}, month = {NOV }, pages = {1904-1912}, type = {Article}, abstract = {The hydrodynamics of a gas-solid fluidized bed (FB) is affected by the bubble diameter, which in turn strongly influences the performance of a fluidized bed reactor (FBR). Thus, determining the bubble diameter accurately is of crucial importance in the design and operation of an FBR. Various equations are available for calculating the bubble diameter in an FBR. It has been found in this study that these models show a large variation while predicting the experimentally measured bubble diameters. Accordingly, the present study proposes a new equation for computing the bubble diameter in a fluidized bed. This equation has been developed using an efficient, yet infrequently employed computational intelligence (CI)-based data-driven modelling method termed genetic programming (GP). The prediction and generalization performance of the GP-based equation has been compared with that of a number of currently available equations for computing the bubble diameter in a fluidized bed and the results obtained show a good performance by the newly developed equation.}, issn = {0011-3891}, doi = {10.18520/cs/v115/i10/1904-1912}, author = {Sonolikar, R. R. and Patil, M. P. and Mankar, R. B. and Tambe, S. S. and Kulkarni, B. D.} }