Optimization of fermentation media for exopolysaccharide production from lactobacillus plantarum using artificial intelligence-based techniques - Desai, Kiran M.; Akolkar, S. K.; Badhe, Yogesh P.; Tambe, S. S.; Lele, S. S. (Process Biochemistry, 2006)
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

Soft-sensor development for fed-batch bioreactors using support vector regression - Desai, Kiran M.; Badhe, Yogesh P.; Tambe, S. S.; Kulkarni, B. D. (Biochemical Engineering Journal, 2006)
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.
Conference on Atomic Molecular and Optical Physics, Calcutta, INDIA, DEC 13-15, 2005
Keywords: deprotection; Microwave; stannous chloride; tert-butyldimethylsilyl ethers

Estimation of gross calorific value of coals using artificial neural networks - Patel, Shagufta U.; Kumar, B. Jeevan; Badhe, Yogesh P.; Sharma, B. K.; Saha, Sujan; Biswas, Subhasish; Chaudhury, Asim; Tambe, Sanjeev S.; Kulkarni, Bhaskar D. (Fuel, 2007)
The gross calorific value (GCV) is an important property defining the energy content and thereby efficiency of fuels, such as coals. There exist a number of correlations for estimating the GCV of a coal sample based upon its proximate and/or ultimate analyses. These correlations are mainly linear in character although there are indications that the relationship between the GCV and a few constituents of the proximate and ultimate analyses could be nonlinear. Accordingly, in this paper a total of seven nonlinear models have been developed using the artificial neural networks (ANN) methodology for the estimation of GCV with a special focus on Indian coals. The comprehensive ANN model developed here uses all the major constituents of the proximate and ultimate analyses as inputs while the remaining six sub-models use different combinations of the constituents of the stated analyses. It has been found that the GCV prediction accuracy of all the models is excellent with the comprehensive model being the most accurate GCV predictor. Also, the performance of the ANN models has been found to be consistently better than that of their linear counterparts. Additionally, a sensitivity analysis of the comprehensive ANN model has been performed to identify the important model inputs, which significantly affect the GCV. The ANN-based modeling approach illustrated in this paper is sufficiently general and thus can be gainfully extended for estimating the GCV of a wide spectrum of solid, liquid and gaseous fuels. (c) 2006 Elsevier Ltd. All rights reserved.
Keywords: Artificial neural network; gross calorific value (GCV); proximate and ultimate analyses

Gas sorption and transport in polyarylates: effect of substituent symmetry and polarity - Karadkar, Prasad B.; Kharul, Ulhas K.; Bhole, Yogesh S.; Badhe, Yogesh P.; Tambe, Sanjeev S.; Kulkarni, Bhaskar D. (Journal of Membrane Science, 2007)
The gas sorption properties of polyarylates based on bisphenol-A possessing symmetric/asymmetric substitution by nonpolar -CH3 group and symmetrically linked terephthalic acid were examined. The effects of substitution of polar bromine on terephalic acid moiety of polyarylate based on asymmetrically substituted bisphenol were also studied for physical, sorption and transport properties. The estimation of dual mode sorption parameters, solubility and diffusion coefficient revealed that nature of the substituent and substitution type plays a crucial role in depicting permeation properties. An asymmetric substitution by -CH3 group increased solubility coefficient of pure gases (N-2, O-2, CH4 and CO2) up to 27% and symmetric substitution increased the same up to 106%. This was coupled with 7-35% increase in solubility selectivity in both cases of substitution, which ascertained the usefulness of methyl group substitution in polyarylates based on terephalic acid. The pressure dependency of solubility coefficients and solubility selectivity was also investigated for these polyarylates. The sorption and transport properties of these polyarylates correlated well with physical properties of polyarylates and gases studied. The sorption/desorption kinetics of symmetrically substituted TMBisA-T was performed in order to deduce time dependent sorption behavior and to evaluate diffusivity coefficient. The apparent diffusion coefficients from sorption kinetics, desorption kinetics and from steady-states permeation-sorption were compared. The diffusion coefficients of CH4 and N-2 deduced by these methods correlated well with each other. (C) 2007 Elsevier B.V. All rights reserved.
Keywords: Diffusion; kinetics; permeation; polyarylates; Sorption

Improve polyethylene process control and product quality - using artificial intelligence-based sensors can improve costs - Badhe, Yogesh P.; Lonari, J.; Tambe, S. S.; Kulkarni, B. D.; Valecha, N. K.; Deshmukh, S. V.; Ravichandran, S. (Hydrocarbon Processing, 2007)

Study of a laboratory-scale froth flotation process using artificial neural networks - Kalyani, V. K.; Pallavika; Chaudhuri, Sanjay; Charan, T. Gouri; Haldar, D. D.; Kamal, K. P.; Badhe, Yogesh P.; Tambe, S. S.; Kulkarni, B. D. (Mineral Processing and Extractive Metallurgy Review, 2008)
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

Self-organizing maps: a tool to ascertain taxonomic relatedness based on features derived from 16S rDNA sequence - Raje, D. V.; Purohit, H. J.; Badhe, Yogesh P.; Tambe, S. S.; Kulkarni, B. D. (Journal of Biosciences, 2010)
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

Classification of Indian power coals using K-means clustering and self organizing map neural network - Pandit, Yogesh P.; Badhe, Yogesh P.; Sharma, B. K.; Tambe, Sanjeev S.; Kulkarni, Bhaskar D. (Fuel, 2011)
The present study reports results of the classification of Indian coals used in thermal power stations across India. For classifying these power coals a classical unsupervised clustering technique, namely ``K-Means Clustering'' and an artificial intelligence (AI) based nonlinear clustering formalism known as ``Self-Organizing Map (SOM)'' have been used for the first time. To conduct the said classification, five coal descriptor variables namely moisture, ash, volatile matter, carbon and gross calorific value (GCV) have been used. The classification results thereof indicate that Indian power coals from different geographical origins can be classified optimally into seven classes. It has also been found that the K-means and SOM based classification results exhibit similarity in close to 75% coal samples. Further, K-means and SOM based seven coal categories have been compared with as many grades of a commonly employed Useful Heat Value (UHV) based Indian non-coking coal grading system. Here, it was observed that a number of UHV-based grades exhibit similarity with the categories identified by the K-means and SOM methods. The classification of Indian power coals as provided here can be gainfully used in selecting application-specific coals as also in their grading and pricing. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords: Coal classification; Indian power coals; K-means clustering; Self-Organizing Map

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