Arrhythmia classification using local Holder exponents and support vector machine - Joshi, Aniruddha; Rajshekhar; Chandran, S.; Phadke, S.; Jayaraman, Valadi K.; Kulkarni, B. D. (1st International Conference on Pattern Recognition and Machine Intelligence, 2005)
We propose a novel hybrid Holder-SVM detection algorithm for arrhythmia classification. The Holder exponents are computed efficiently using the wavelet transform modulus maxima (WTMM) method. The hybrid system performance is evaluated using the benchmark MIT-BIH arrhythmia database. The implemented model classifies 160 of Normal sinus rhythm, 25 of Ventricular bigeminy, 155 of Atrial fibrillation and 146 of Nodal (A-V junctional) rhythm with 96.94% accuracy. The distinct scaling properties of different types of heart rhythms may be of clinical importance.

Identification of coding and non-coding sequences using local holder exponent formalism - Kulkarni, O. C.; Vigneshwar, R.; Jayaraman, Valadi K.; Kulkarni, B. D. (Bioinformatics, 2005)
Motivation: Accurate prediction of genes in genomes has always been a challenging task for bioinformaticians and computational biologists. The discovery of existence of distinct scaling relations in coding and non-coding sequences has led to new perspectives in the understanding of the DNA sequences. This has motivated us to exploit the differences in the local singularity distributions for characterization and classification of coding and non-coding sequences. Results: The local singularity density distribution in the coding and non-coding sequences of four genomes was first estimated using the wavelet transform modulus maxima methodology. Support vector machines classifier was then trained with the extracted features. The trained classifier is able to provide an average test accuracy of 97.7%. The local singularity features in a DNA sequence can be exploited for successful identification of coding and non-coding sequences.

Knowledge incorporated support vector machines to detect faults in Tennessee Eastman Process - Kulkarni, A.; Jayaraman, Valadi K.; Kulkarni, B. D. (Computers & Chemical Engineering, 2005)
A support vector machine with knowledge incorporation is applied to detect the faults in Tennessee Eastman Process, a benchmark problem in chemical engineering. The knowledge incorporated algorithm takes advantage of the information on horizontal translation invariance in tangent direction of the instances in dataset. This essentially changes the representation of the input data while training the algorithm. These local translations do not alter the class membership of the instances in the dataset. The results on binary as well as multiple fault detection justify the use of knowledge incorporation. (c) 2005 Elsevier Ltd. All rights reserved.
Keywords: fault detection; knowledge; support vector machines; Tennessee Eastman Process

Regression models using pattern search assisted least square support vector machines - Patil, N. S.; Shelokar, P. S.; Jayaraman, Valadi K.; Kulkarni, B. D. (Chemical Engineering Research and Design, 2005)
Least Square Support Vector Machines (LS-SVM), a new machine-learning tool has been employed for developing data driven models of non-linear processes. The method is firmly rooted in the statistical learning theory and transforms the input data to a higher dimensional feature space where the use of appropriate kernel functions avoid computational difficulty. Further, a pattern search algorithm, which explores multiple directions and utilizes coordinate search with fixed step size, is employed for selecting optimal LS-SVM model that produces a minimum possible prediction error. To show the efficacy and efficiency of the fully automated pattern search assisted LS-SVM methodology, we have tested it on several benchmark examples. The study suggests that proposed paradigm can be a useful and viable tool in building data driven models of non-linear processes.
Keywords: equality constraints; LS-SVM; model selection; Optimization; pattern search

SVM classifier incorporating simultaneous noise reduction and feature selection: illustrative case examples - Kumar, R.; Jayaraman, Valadi K.; Kulkarni, B. D. (Pattern Recognition, 2005)
A hybrid technique involving symbolization of data to remove noise and use of conditional entropy minima to extract relevant and non-redundant features is proposed in conjunction with support vector machines to obtain more robust classification algorithm. The technique tested on three data sets shows improvements in classification efficiencies. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keywords: classification; conditional entropy; SVM; symbolization

Comparative assessment of downstream processing options for lactic acid - Joglekar, H. G.; Rahman, Imran; Babu, Suresh; Kulkarni, B. D.; Joshi, Ajit (Separation and Purification Technology, 2006)
The possibility of manufacturing a biodegradable polymer from lactic acid has led to extensive research in recovery of lactic acid produced by fermentation, by different downstream processing routes. This paper assesses the suitability of different downstream processing options such as reactive extraction, adsorption, electrodialysis, esterification and reactive distillation. It compares the costs of different process routes. The assessment indicates that the conventional precipitation of calcium lactate, followed by acidification, esterification and hydrolysis will be the most economical route although it generates large quantity of gypsum sludge. (c) 2006 Elsevier B.V. All rights reserved.
Keywords: Adsorption; electrodialysis; Esterification; Lactic acid; Reactive distillation; reactive extraction

Improved time series prediction with a new method for selection of model parameters - Jade, A. M.; Jayaraman, Valadi K.; Kulkarni, B. D. (Journal of Physics A-Mathematical and General, 2006)
A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Holder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data.

Reverse microemulsion mediated sol-gel synthesis of lithium silicate nanoparticles under ambient conditions: Scope for CO2 sequestration - Khomane, Ramdas B.; Sharma, B. K.; Saha, S.; Kulkarni, B. D. (Chemical Engineering Science, 2006)
We report on the synthesis of nanocrystalline lithium silicate by coupling of sol-gel method in reverse microemulsion. The sample calcined at 800 degrees C gives pure phase lithium metasilicate nanocrystallites. X-ray diffraction and transmission electron microscopy confirmed the formation of nanocrystalline lithium silicate particles with a narrow size distribution. The nanoparticle prepared in the microemulsion shows enhanced CO2 sorption capacity and shorter retention times at higher temperature (similar to 131 ml/g at STP at 610 degrees C which are better than the best known results. (c) 2005 Elsevier Ltd. All rights reserved.
Keywords: Adsorption; Carbon dioxide; Microemulsion; particle; separations; sol-gel; surfactant

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.
Keywords: deprotection; Microwave; stannous chloride; tert-butyldimethylsilyl ethers

Development of a soft sensor for a batch distillation column using support vector regression techniques - Jain, P.; Rahman, I.; Kulkarni, B. D. (Chemical Engineering Research & Design, 2007)
A support vector regression (SVR)-based model is developed for a batch distillation process in order to estimate the product compositions from temperature measurements. Kernel function such as linear, polynomial and RBF are employed for SVR modelling. The original process data was generated by simulating the batch distillation process, varying the initial feed composition and boilup rate from batch to batch. Within each batch reflux ratio was also randomly changed to represent the true dynamics of the batch distillation. The results show the potential of the method for developing softsensor for chemical processes.
Keywords: Batch distillation; composition estimation; soft sensor; support vector regression

Effect of sparger design on the flow in a bubble column: symbolic analysis of LDA data - Kulkarni, A. A.; Deshpande, Suvarna S.; Joshi, J. B.; Kumar, V. Ravi; Kulkarni, B. D. (Industrial & Engineering Chemistry Research, 2007)
This paper aims at developing a methodology that helps in the characterization of the effects of a single point and multipoint sparger on the flow patterns in a bubble column reactor. The methodology uses the simultaneously measured instantaneous velocity-time data (two orthogonal velocity components) using LDA at different measurement locations. The data sets are transformed into their equivalent description in terms of polar coordinates, viz., resultant velocity vector and angle. This information is further transformed in terms of symbolic representation in a binary number format. The covariance matrices obtained from the binary data are analyzed for their eigenvalues, and the spectrum is used for the identification of the dominant, energy containing modes in the data. In the vicinity of the sparger, the nature of covariance matrix and the eigenvalue spectrum for the single point sparger (SPS) data was significantly different from that of the multipoint sparger (MPS). In the bulk region, for both types of spargers, the nature of the eigenvalues and the number of dominant modes is seen to be different from the near sparger region. In general, more numbers of eigenvalues are required to capture the total energy in the data for a multipoint sparger when compared to a single point sparger. This suggests a higher level of complexities existing due to the intricate hydrodynamics for the multipoint sparger. The possible use of this methodology for practical purposes, e.g., identification of malfunctioning of gas-liquid sparger reactors is discussed.

Exhaustive analysis of frontal copolymerization of functionalized monovinyl and divinyl monomers - Pujari, Narahari S.; Inamdar, Satish R.; Ambekar, Jalindar D.; Kulkarni, B. D.; Ponrathnam, Surendra (Chemistry-A European Journal, 2007)
A series of copolymers of 2-hydroxyethyl methacrylate (HEMA)/ glycidyl methacrylate (GMA) and ethylene dimethacrylate (EGDM) were synthesized by frontal polymerization (FP). This study was conducted to investigate the effect of crosslink density, type and concentration of initiator, the use of a complex initiator system, porogen, and diluent on the most relevant parameters of FP, such as sustainability of the front, temperature profile, front velocity, and yield. The products were also characterized for intruded pore volume, pore-size distribution, epoxy-functionality number, and surface morphology. Higher crosslink densities (CLDs) and initiator concentration produced higher front velocities, whereas no trend in front temperature was noted. A complex initiation system was effective in stabilizing and increasing the polymerization yield. Relative to suspension polymerization (SP), FP products synthesized without a solvent were microporous, whereas micro-to-macroporous products were obtained in the presence of a solvent (for HEMA-EGDM polymers). We also present, explain, and discuss the exotic patterns observed under a microscope. We observed two basic types of spatial patterns, namely, planar and nonplanar patterns. The type of planar pattern observed under scanning electron microscopy (SEM) has a spatial impulse that appears as a loop followed by regular periodic motion in the radial and axial directions. This behavior gives rise to a repeating pattern that is a few microns thick. Also, nonplanar patterns, namely, layered concentric rings and winding staircase patterns, were observed under SEM.
Keywords: copolymerization; patterns; Polymers; porosity

Fault diagnosis using dynamic time warping - Rajshekhar; Gupta, Ankur; Samanta, A. N.; Kulkarni, B. D.; Jayaraman, Valadi K. (Pattern Recognition and Machine Intelligence, Proceedings, 2007)
Owing to the superiority of Dynamic Time Warping as a similarity measure of time series, it can become an effective tool for fault diagnosis in chemical process plants. However, direct application of Dynamic Time Warping can be computationally inefficient, given the complexity involved. In this work we have tackled this problem by employing a warping window constraint and a Lower Bounding measure. A novel methodology for online fault diagnosis with Dynamic Time Warping has been suggested and its performance has been investigated using two simulated case studies.

Granular support vector machine based method for prediction of solubility of proteins on overexpression in Escherichia coli - Kumar, Pankaj; Jayaraman, Valadi K.; Kulkarni, B. D. (Pattern Recognition and Machine Intelligence, Proceedings, 2007)
We employed a granular support vector Machines(GSVM) for prediction of soluble proteins on over expression in Escherichia coli. Granular computing splits the feature space into a set of subspaces (or information granules) such as classes, subsets, clusters and intervals [14]. By the principle of divide and conquer it decomposes a. bigger complex problem into smaller and computationally simpler problems. Each of the granules is then solved independently and all the results are aggregated to form the final solution. For the purpose of granulation association rules was employed. The results indicate that a difficult imbalanced classification problem can be successfully solved by employing GSVM.

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)

Role of ionic diffusion in polymer gel mediated growth (PMG) technique for the synthesis of nanoparticulate fillers - Sonawane, Pradip S.; Biradar, Santoshkumar S.; Radhakrishnan, S.; Kulkarni, B. D. (Materials Chemistry and Physics, 2007)
Polymer mediated growth (PMG), which forms an important method for synthesis of nanoparticles, has one of the ions bound by polymer matrix while the other is allowed to diffuse and react to form desired compound. The exact role of ionic diffusion in the formation of nanoparticles has been investigated by taking specific case studies. Typically calcium carbonate, calcium phosphate and calcium sulfate have been formed in nano-size by PMG route using polyacrylamide gel. The extent of cross-linking of the polymer matrix in the formation of gel, affects the diffusivity of ions into polymer matrix and the size of particles formed. The rates of diffusion for different extents of cross-linking of polymer are estimated during the synthesis of nanoparticles using Fick's law. The inter-correlation between the particle size formation and the diffusion length has been clearly brought out in these studies. (c) 2007 Elsevier B.V. All rights reserved.
Keywords: ionic diffusion; nanoparticulate fillers; polymeric gels

Spinning wave motion in frontal polymerization - Inamdar, Satish R.; Pujari, N. S.; Karimi, I. A.; Ponrathnam, S.; Tayal, R. K.; Kulkarni, B. D. (Chemical Engineering Science, 2007)
We present a mathematical model describing dynamics of spinning waves which propagate during frontal polymerization reaction taking place in a cylindrical reactor tube. The self-organization of spatio-temporal solution of wave equations due to interplay between thermal diffusion and kinetics gives rise to pattern formation. We begin with a fundamental equation of motion of radial coordinate after defining an asymptotic phase for spinmode. The motion is analyzed near a critical (Hopf) point and a perturbation solution is used to obtain patterns for a case of preparation of poly(2-hydroxyethyl methacrylate) (PHEMA) via frontal polymerization reaction. The model uses the distance as seen in photograph taken using scanning electron microscope (SEM) from which motion begins around core of spiral and calculates pitch of spiral which matches closely with experimental observation in micrograph. Also the model predicts qualitatively the ramp wave and spiral wave motion as observed under SEM. Both these results are reported in open literature for the first time to our best knowledge. (c) 2006 Elsevier Ltd. All rights reserved.
Keywords: frontal polymerization; spinning wave; spiral motion; wave dynamics

Using recurrence quantification analysis descriptors for protein sequence classification with support vector machines - Mitra, Joydeep; Mundra, Piyushkumar; Kulkarni, B. D.; Jayaraman, Valadi K. (Journal of Biomolecular Structure & Dynamics, 2007)
In this work, we integrate a non-linear signal analysis method, recurrence quantification analysis (RQA), with the well-known machine-learning algorithm, support vector machines for the binary classification of protein sequences. Two different classification problems were selected, discriminating between aggregating and non-aggregating proteins and mostly disordered and completely ordered proteins, respectively. It has also been shown that classification performance of SVM models improve on selection of the most informative RQA descriptors as SVM input features.

Identification and characterization of flow structures in chemical process equipment using multiresolution techniques - Deshpande, Sagar S.; Joshi, Jyeshtharaj B.; Kumar, V. Ravi; Kulkarni, B. D. (Chemical Engineering Science, 2008)
Planar information of velocity from 2D particle image velocimetry (PIV) and large eddy simulation (LES) data have been studied using multiresolution wavelet transform (WT) formalisms, i.e., discrete and continuous WT. Identification of dominant energy containing structures with their characterization in terms of fractal spectra have been carried out for industrially important equipment exhibiting turbulent behavior. These include annular centrifugal contactor, jet loop reactor, ultrasound reactor, channel flow, stirred tank and bubble column reactor. The characterization of their dynamics based on denoising the data and studying the local energy along the WT scales show sensitive variation and this helps in identifying the size and shape of structures. A dependency is seen between mixing time and the higher order moments of length scale distribution, viz., skewness and kurtosis and a generalized correlation has been built up for important types of equipment and associated flow parameters. The correlation is not only based on the knowledge of reactor geometry and operating conditions but also on the flow structures via their statistical parameters. Wavelet transform modulus maxima (WTMM) methodology has been used to study the evolution of structures and their interaction in a reduced dimensionality by evaluating the fractal spectra. Classification studies have been carried out using principal component analysis (PCA) of the fractal spectra. The results obtained show clear classes for the six types of equipments and delineate regimes to obtain benchmark patterns of flow hydrodynamics based on PCA co-ordinates. This methodology offers a generalized way for the optimal design and operation of different types of reactors. (c) 2008 Published by Elsevier Ltd .
Keywords: Chemical reactors; hydrodynamics; Length scale distribution; mixing; Model reduction; Turbulence; Wavelet transform

Immobilization of Candida rugosa lipase on poly(allyl glycidyl ether-co-thylene glycol dimethacrylate) macroporous polymer particles - Vaidya, Bhalchandra K.; Ingavle, Ganesh C.; Ponrathnam, S.; Kulkarni, B. D.; Nene, Sanjay N. (Bioresource Technology, 2008)
Macroporous polymer particles containing surface epoxy groups were synthesized for immobilization of Candida rugosa lipase (CRL). The effect of incorporation of two different sets of monomers [allyl glycidyl ether (AGE) and glycidyl methacrylate (GMA)] and the effect of crosslinking density on immobilization of lipase were studied. AGE-co-EGDM polymers gave higher binding and expression of lipase than GMA-co-EGDM polymers. Optimization of immobilization parameters was done with respect to immobilization time and enzyme loading. Amongst AGE-co-EGDM polymer series, AGE-150 polymer found to give maximum lipase activity yield and therefore evaluated for temperature, pH and storage stability. Under optimum conditions, AGE-150 polymer gave 78.40% of activity yield. Immobilized lipase on AGE-150 showed a broader pH, higher temperature and excellent storage stability. (C) 2007 Published by Elsevier Ltd.
Keywords: Candida rugosa lipase; Enzyme immobilization; Epoxy-activated support

Multicanonical jump walk annealing assisted by tabu for dynamic optimization of chemical engineering processes - Shelokar, P. S.; Jayaraman, Valadi K.; Kulkarni, B. D. (European Journal of Operational Research, 2008)
A hybrid methodology, viz., multicanonical jump walk annealing assisted by tabu list (MJWAT) is proposed for solving dynamic optimization problems in chemically reacting systems. This method combines the power of multicanonical sampling with the beneficial features of simulated annealing. Incorporating tabu list further enhances the efficiency of the method. The superior performance of the MJWAT is highlighted with the help of five benchmark case studies. (C) 2006 Elsevier B.V. All rights reserved.
Keywords: dynamic optimization; metaheuristics; Monte Carlo simulations; multicanonical algorithms; simulated annealing; tabu conditions

Optimal xylanase production using Penicilium janthinellum NCIM 1169: a model based approach - Meshram, Mukesh; Kulkarni, Abhijit; Jayaraman, Valadi K.; Kulkarni, B. D.; Lele, S. S. (Biochemical Engineering Journal, 2008)
Xylanases are an industrially important class of hydrolytic enzymes that degrade xylans. Production of xylanase from a fungal culture by submerged fermentation and optimization of the operating conditions for maximum activity are the two aims of the present study. Penicillium janthinellum NCIM 1169 with Mandels-Weber medium, sugarcane bagassse (40#) as a carbon source and beef extract as a nitrogen source were used in the experiments. We did 41 experiments to see the effect of variations in carbon, nitrogen source, pH, and inoculum on xylanase activity. This data was then used to build an input/output model using multiple linear regression, back propagation neural network and lazy learning algorithm. It was found that lazy learning model correlated well in mapping input/output data. This model was then utilized as an objective function in genetic algorithm to find the optimal combination of the operating conditions to get the maximum xylanase activity. It was observed that with carbon source, 1.63%, nitrogen source, 0.16%, pH, 4.1, and inoculum, 5.5%, maximum xylanase activity of 28.98 +/- 1.73 U/ml was achieved. (C) 2008 Elsevier B.V. All rights reserved.
Keywords: Artificial intelligence; Enzyme activity; Fermentation; Modeling; Optimization; Xylanase

Preparation and electrochemical characterization of lithium cobalt oxide nanoparticles by modified sol-gel method - Khomane, Ramdas B.; Agrawal, Amit C.; Kulkarni, B. D.; Gopukumar, S.; Sivashanmugam, A. (Materials Research Bulletin, 2008)
Uniformly distributed nanoparticles of LiCoO2 have been synthesized through the simple sol-gel method in presence of neutral surfactant (Tween-80). The powders were characterized by X-ray diffractometry, transmission electron microscopy and electrochemical method including charge-discharge cycling performance. The powder calcined at a temperature of 900 degrees C for 5 h shows pure phase layered LiCoO2. The results show that the particle size is reduced in presence of surfactant as compared to normal sol-gel method. Also, the sample prepared in presence of surfactant and calcined at 900 degrees C for 5 h shows the highest initial discharge capacity (106 mAh g(-1)) with good cycling stability as compared to the sample prepared without surfactant which shows the specific discharge capacity of 50 mAh g(-1). (C) 2007 Elsevier Ltd. All rights reserved.
Keywords: Electrochemical properties; intercalation reactions; Layered compounds; Sol-gel chemistry; X-ray diffraction

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

SVR-based prediction of point gas hold-up for bubble column reactor through recurrence quantification analysis of LDA time-series - Gandhi, Ankit B.; Joshi, J. B.; Kulkarni, A. A.; Jayaraman, Valadi K.; Kulkarni, B. D. (International Journal of Multiphase Flow, 2008)
Recurrence quantification analysis (RQA) has emerged as a useful tool for detecting singularities in nonstationary time-series data. In this paper, we use RQA to analyze the velocity-time data acquired using laser doppler anemometry (LDA) signals in a bubble column reactor for Single point and Multipoint point spargers. The recurring dynamical states within the velocity-time-series occurring due to the bubble and the liquid passage at the point of measurement, are quantified by RQA features (namely % Recurrence, % Determinism, % Laminarity and Entropy), which in turn are regressed using support vector regression (SVR) to predict the point gas hold-up values. It has been shown that SVR-based model for the bubble column reactor can be potentially useful for online prediction and monitoring of the point gas hold-up for different sparging conditions. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords: Bubble column; Gas hold-up; LDA; Recurrence quantification analysis (RQA); Support vector regression (SVR)

Synthesis and magnetic properties of barium-calcium hexaferrite particles prepared by sol-gel and microemulsion techniques - Jotania, R. B.; Khomane, Ramdas B.; Chauhan, C. C.; Menon, Shamal K.; Kulkarni, B. D. (Journal of Magnetism and Magnetic Materials, 2008)
The preparation of W-type hexaferrite particles with the composition BaCa2Fe16O27 by microemulsion and a stearic acid sol - gel method with and without surfactant has been investigated at various sintering temperatures. The structural and magnetic characteristics have been studied by X-ray diffraction ( XRD), a vibrating sample magnetometer ( VSM), scanning electron microscopy ( SEM), thermogravimetric analysis ( TGA), differential scanning calorimetric ( DSC) and Fourier transform infrared ( FTIR) techniques. The effect of sintering temperature on the properties of BaCa2Fe16O27 hexaferrites has been studied. The value of saturation magnetization ( M-s) depends on types of surfactant used. The sample prepared in the presence of polyoxyethylene ( 20) sorbitan monooleat ( Tween 80) shows low saturation magnetization ( M-s 15.10 emu/g), whereas the other sample prepared in the presence of a surfactant cetyltrimethylammonium bromide (CTAB) exhibits high saturation magnetization (M-s = 24.60 emu/g) compared to the normal sample. (C) 2007 Elsevier B. V. All rights reserved.
Keywords: BaCa2Fe16O27; hexaferrite; magnetic property; Microemulsion; Sol-gel method; surfactant

Temperature dependent structural studies and magnetic properties of BaCa2Fe16O27 hexaferrites prepared by stearic acid gel route in presence of SDS surfactant - Joyania, R. B.; Khomane, Ramdas B.; Chauhan, C. C.; Kulkarni, B. D. (Synthesis and Reactivity in Inorganic Metal-Organic and Nano-Metal Chemistry, 2008)
The preparation of BaCa2Fe16O27 hexaferrite powders by a stearic acid gel method with SDS surfactant has been investigated at various sintering temperatures. The samples were characterized by X-ray diffraction, scanning electron microscopy (SEM), TGA, FTIR and vibrating sample magnetometer (VSM). The effect of sintering temperature on properties of BaCa2Fe16O27 hexaferrites was studied. In presence of sodium dodecyl sulfate (SDS) surfactant, the magnetic parameters, like saturation magnetization, coercivity and remanent magnetization, have been calculated from hysteresis loop.
Keywords: BaCa2Fe16O27; magnetic properties; stearic acid gel method

Autocatalysis: a unit process of biological systems - Gadgil, Chetan J.; Kulkarni, B. D. (Chemical Engineering Progress, 2009)

Autocatalysis in biological systems - Gadgil, Chetan J.; Kulkarni, B. D. (Aiche Journal, 2009)

Evaluation of repulsive particle swarm method for phase equilibrium and phase stability problems - Rahman, Imran; Das, Anwesh Kr.; Mankar, Raju B.; Kulkarni, B. D. (Fluid Phase Equilibria, 2009)
Phase equilibrium and stability problems are of crucial importance in simulation, design and optimization of several separation processes. Recently, these problems have been solved using minimization of Gibbs free energy, using global optimization techniques. In this paper, repulsive particle swarm (RPS), a recent global optimization technique is explored for the Solution of phase stability and phase equilibrium. (C) 2009 Elsevier B.V. All rights reserved.
Keywords: Liquid-liquid equilibria; Phase equilibrium; Phase stability analysis; Repulsive particle swarm method

Identification of N-glycosylation sites with sequence and structural features employing random forests - Karnik, Shreyas; Mitra, Joydeep; Singh, Arunima; Kulkarni, B. D.; Sundarajan, V.; Jayaraman, Valadi K. (Pattern Recognition and Machine Intelligence, Proceedings, 2009)
N-Glycosylation plays a very important role in various processes like quality control of proteins produced in ER, transport of proteins and in disease control. The experimental elucidation of N-Glycosylation sites is expensive and laborious process. In this work we build models for identification of potential N-Glycosylation sites in proteins based on sequence and structural features. The best model has cross validation accuracy rate of 72.81%.

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

Sharp cut algorithm for optimization - Inamdar, Satish R.; Karimi, I. A.; Parulekar, S. J.; Kulkarni, B. D. (Computers & Chemical Engineering, 2011)
In this paper, we introduce a new cutting plane algorithm which is computationally less expensive and more efficient than Kelley's algorithm. This new cutting plane algorithm uses an intersection cut of three types of cutting planes. We find from numerical results that the global search method formed using successive linear programming and a new intersection set is at least twice as fast as Kelley's cutting planes. The necessary mathematical analysis and convergence theorem are provided. The key findings are illustrated via optimization of a cascade of three CSTRs. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords: Convergence theorem; Cutting plane; Sharp cut; Successive linear programming

L. K. Doraiswamy (1927-2012) - Kulkarni, B. D. (Current Science, 2012)

Thermoneutral design aspects of gasoline chemical looping reformer - Kale, G. R.; Kulkarni, B. D. (Fuel Cells, 2013)
Chemical looping reforming (CLR) is a new technology for syngas generation. The theoretical process design aspects of syngas generation using CLR of isooctane (gasoline) are studied in this paper to assess its ability for fuel processor development for solid oxide fuel cells. The fuel processor operating conditions for maximum syngas generation at thermoneutral conditions are determined in this study using nickel oxide as oxygen carrier for different inputs of oxygen carrier within the temperature range of 600-1,000 degrees C at 1bar pressure. The thermoneutral temperatures for the dual reactor fuel processor were calculated using the hot product gas stream and exothermic CLR process enthalpy to completely balance the endothermic process requirements. The thermoneutral point of 879.5 degrees C (NiO input of 7moles) delivered maximum syngas (13.92moles) using lowest amount of air (26.13moles) in the process was found to be the most suitable thermoneutral temperature for the fuel processor operation. The novel fuel processor design can also be used for other fuels and oxygen carriers.
Keywords: Chemical Looping Reformer; Fuel Processor; Isooctane to Syngas; Oxygen Carrier; Thermoneutral Temperature

Artificial intelligence-based modeling of high ash coal gasification in a pilot plant scale fluidized bed gasifier - Patil-Shinde, Veena; Kukarni, Tejas; Kulkarni, Rahul; Chavan, Prakash D.; Sharma, Tripurari; Sharma, Bijay Kumar; Tambe, Sanjeey S.; Kulkarni, B. D. (Industrial & Engineering Chemistry Research, 2014)
The quality of coalespecially its high ash contentsignificantly affects the performance of coal-based processes. Coal gasification is a cleaner and an efficient alternative to the coal combustion for producing the syngas. The high-ash coals are found in a number of countries, and they form an important source for the gasification. Accordingly, in this study, extensive gasification experiments were conducted in a pilot-plant scale fluidized-bed coal gasifier (FBCG) using high-ash coals from India. Specifically, the effects of eight coal and gasifier process related parameters on the four gasification performance variables, namely CO+H-2 generation rate, syngas production rate, carbon conversion, and heating value of the syngas, were rigorously studied. The data collected from these experiments were used in the FBCG modeling, which was conducted by utilizing two artificial intelligence (AI) strategies namely genetic programming (GP) and artificial neural networks (ANNs). The novelty of the GP formalism is that it searches and optimizes both the form and parameters of an appropriate linear/nonlinear function that best fits the given process data. The original eight-dimensional input space of the FBCG models was reduced to three-dimensional space using the principal component analysis (PCA) and the PCA-transformed three variables were used in the AI-based FBCG modeling. A comparison of the GP and ANN-based models reveals that their output prediction accuracies and the generalization performance vary from good to excellent as indicated by the high training and test set correlation coefficient magnitudes lying between 0.92 and 0.996. This study also presents results of the sensitivity analysis performed to identify those coal and process related parameters, which significantly affect the FBCG process performance.

Pharmacokinetic modeling of caco-2 cell permeability using genetic programming (GP) method - Vyas, Renu; Goel, Purva; Karthikeyan, Muthukumarasamy; Tambe, S. S.; Kulkarni, B. D. (Letters in Drug Design & Discovery, 2014)
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

Conversion of dried aspergillus candidus mycelia grown on waste whey to biodiesel by in situ acid transesterification - Kakkad, Hardik; Khot, Mahesh; Smita S. Zinjarde; RaviKumar, Ameeta; Kumar, V. Ravi; Kulkarni, B. D. (Bioresource Technology, 2015)
This study reports optimization of the transesterification reaction step on dried biomass of an oleaginous fungus Aspergillus candidus grown on agro-dairy waste, whey. Acid catalyzed transesterification was performed and variables affecting esterification, viz., catalyst methanol and chloroform concentrations, temperature, time, and biomass were investigated. Statistical optimization of the transesterification reaction using Plackett-Burman Design showed biomass to be the predominant factor with a 12.5-fold increase in total FAME from 25.6 to 320 mg. Studies indicate that the transesterification efficiency in terms of conversion is favored by employing lower biomass loadings. A. candidus exhibited FAME profiles containing desirable saturated (30.2%), monounsaturated (31.5%) and polyunsaturated methyl esters (38.3%). The predicted and experimentally determined biodiesel properties (density, kinematic viscosity, iodine value, cetane number, TAN, water content, total and free glycerol) were in accordance with international (ASTM D6751, EN 14214) and national (IS 15607) standards. (C) 2015 Published by Elsevier Ltd.
Keywords: Aspergillus candidus; Biodiesel; In situ acid transesterification; Plackett-Burman Design; Whey

Embedded multiple shooting methodology in a genetic algorithm framework for parameter estimation and state identification of complex systems - Sarode, Ketan Dinkar; Kumar, V. Ravi; Kulkarni, B. D. (Chemical Engineering Science, 2015)
A novel parameter estimation and state identification algorithm for nonlinear dynamical systems from data by embedding a multiple shooting methodology in the framework of a genetic algorithm (EMSGA) is described. The advantages of EMSGA are brought out by studies with two highly nonlinear examples, viz., the chaotic dynamics of a non-isothermal CSTR and the glycolysis regulation in Lactococcus lactis for production of lactic acid. For the chaotic dynamics with extremely high sensitivity to parameter values and initial conditions, EMSGA accurately estimates all process parameters while at the same time recovering the true dynamics of monitored and unmonitored variables from limited extents of noisy dynamic data. The superiority in accuracy and computational time of EMSGA over standalone genetic or multiple shooting algorithms for parameter estimation is shown for comparison purposes. In fact, EMSGA adapts well to the use of generalized canonical models, e.g., the S-system, where use of the fundamental Taylor series method of any order for integration becomes possible. This makes EMSGA highly efficient and exemplified here by estimating all the parameters of the derived higher dimensional 5-system model for the CSTR. Comparative studies with a well-known global-local enhanced scatter search method corroborate the suitability and advantages of the EMSGA methodology. For the glycolysis regulation example the numerical robustness of EMSGA is brought out by estimating a large number of model parameters when a majority of them are raised to the power of the state variables. Interestingly, we show that from noisy in vivo dynamic data it becomes possible to completely recover the noise-free dynamical behavior of unmonitored species concentrations using EMSGA. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords: Canonical models; Chaotic dynamics; Genetic algorithm; Multiple shooting; Noise reduction; parameter estimation

Freezing tendency towards 4-coordinated amorphous networks causes an increase in the heat capacity of supercooled Stillinger-Weber silicon - Apte, Pankaj A.; Pingua, Nandlal; Gautam, Arvind Kumar; Kumar, Uday; Willow, Soohaeng Yoo; Zeng, Xiao Cheng; Kulkarni, B. D. (RSC Advances, 2015)
Supercooled liquid silicon (Si), modeled by the Stillinger-Weber (SW) potential, has been shown to undergo transition to low density amorphous phases at 1060 K in previous studies. Furthermore, the constant pressure heat capacity C-p has been found to exhibit a large increase as the liquid is cooled to 1060 K. In this work, we examine the nature of the equilibrium and the relaxation process of supercooled SW Si in the temperature range of 1060 K to 1070 K at zero pressure. We find that the relaxation of the supercooled liquid leads to a sharp irreversible decrease in the fluctuation of the two body energy of the largest connected network of 4-coordinated particles. Such a process implies a tightening of the bonds (i.e. freezing or jamming) of the network, and is accompanied by a sharp increase in the fraction of the 4-coordinated particles in the system. We find that the jamming (or freezing) process shows a sudden acceleration across a dynamical instability point that occurs at a unique potential energy state of the network. Further, we find that the occurrence of the dynamical instability is associated with the appearance of a straight line region in the cumulative potential energy distributions with a configurational temperature close to 1060 K. We conclude that the supercooled liquid state must be regarded as a constrained equilibrium state, since the accessible microstates are constrained by the inherent tendency of the system to approach the dynamical instability point. Thus all properties of supercooled liquid SW-Si, including the rise in C-p at 1060 K, can be attributed to the freezing tendency of the 4-coordinated particle network.

Role of surfactants in promoting gas hydrate formation - Kumar, Asheesh; Bhattacharjee, Gaurav; Kulkarni, B. D.; Kumar, Rajnish (Industrial & Engineering Chemistry Research, 2015)
Gas hydrates have been proposed as a potential technology for a number of applications, such as separation of gas mixtures, CO2 capture, transportation, and sequestration, methane storage and transport, and seawater desalination. Most of these applications will benefit from reduced induction time of hydrate nucleation, enhanced hydrate growth rate, and maximum water-to-hydrate conversion. The addition of surfactants to the gas-water system serves this purpose in a very effective manner. This review focuses on different surfactants that were utilized for gas hydrate formation studies; insights have been provided on the possible mechanisms of action through which these surfactants affect hydrate formation kinetics. A thorough analysis of the existing literature on surfactants suggests that enhanced rate of hydrate nucleation and growth kinetics may not be directly linked to micelle formation. Conversely, reduced surface tension in the presence of surfactants not only enhances the mass transfer but also changes the morphology of hydrate formation, which in turn enhances gas-water interactions for faster hydrate growth rate.

Effect of different temperature on structural, magnetic and dielectric properties of strontium hexaferrite powder synthesised using aloe vera plant extracts - Solanki, Neha; Jotania, R. B.; Khomane, Ramdas B.; Kulkarni, B. D. (Advanced Science Letters, 2016)
M-type SrFe12O19 hexaferrite particles synthesized using the aloe vera plant extract. Biological materials of microorganisms, plants, enzymes have been suggested as an ecological alternative for conventional reagents in physical and chemical methods. In present paper we report effect of temperature on structural, morphology, magnetic and dielectric properties of M-type SrFe12O19 particles synthesised by Green synthesis technique using Aloe vera plant extract. The metal nitrates (Sr and Fe) were dissolved in aloe vera extract solution and then mixture was kept in oil bath at 100 degrees C till it become dry. The obtain dried powder calcined in a wide temperature range, from 450 degrees C to 1150 degrees C for 4 hours in a static air atmosphere. The calcined powders were characterised using various instrumental techniques like Fourier transform infrared spectroscopy (FTIR) X-ray diffractometry (XRD), Scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), Vibrating Sample Magnetometry (VSM), and dielectric measurements. XRD, FTIR, SEM, Magnetic and dielectric measurements were carried out at room temperature. XRD analysis confirms the formation of mono phase of strontium hexaferrite at 1150 degrees C.
Keywords: Aloe Vera Extract; green synthesis; M-Type Hexaferrite; Strontium Hexaferrite

Inverse problem studies of biochemical systems with structure identification of S-systems by embedding training functions in a genetic algorithm - Sarode, Ketan Dinkar; Kumar, V. Ravi; Kulkarni, B. D. (Mathematical Biosciences, 2016)
An efficient inverse problem approach for parameter estimation, state and structure identification from dynamic data by embedding training functions in a genetic algorithm methodology (ETFGA) is proposed for nonlinear dynamical biosystems using S-system canonical models. Use of multiple shooting and decomposition approach as training functions has been shown for handling of noisy datasets and computational efficiency in studying the inverse problem. The advantages of the methodology are brought out systematically by studying it for three biochemical model systems of interest. By studying a small-scale gene regulatory system described by a S-system model, the first example demonstrates the use of ETFGA for the multifold aims of the inverse problem. The estimation of a large number of parameters with simultaneous state and network identification is shown by training a generalized S-system canonical model with noisy datasets. The results of this study bring out the superior performance of ETFGA on comparison with other metaheuristic approaches. The second example studies the regulation of cAMP oscillations in Dictyostelium cells now assuming limited availability of noisy data. Here, flexibility of the approach to incorporate partial system information in the identification process is shown and its effect on accuracy and predictive ability of the estimated model are studied. The third example studies the phenomenological toy model of the regulation of circadian oscillations in Drosophila that follows rate laws different from S-system power-law. For the limited noisy data, using a priori information about properties of the system, we could estimate an alternate S-system model that showed robust oscillatory behavior with predictive abilities. (C) 2016 Elsevier Inc. All rights reserved.

Genetic programming based drag model with improved prediction accuracy for fluidization systems - Sonolikar, R. R.; Patil, M. P.; Mankar, R. B.; Tambe, S. S.; Kulkarni, B. D. (International journal of Chemical Reactor Engineering, 2017)

Valorization of pomegranate peels: a biorefinery approach - Pathak, P. D.; Mandavgane, S. A.; Kulkarni, B. D. (Waste and Biomass Valorization, 2017)
Recent studies have shown that pomegranate peels (PP) is a valuable source of valuable bioactive compounds, most of which can be converted into value-added products. We herein review this conversion process and provide an outline on the chemical composition of PP and the possible value-added products that can be produced from this commonly discarded agro-waste. We have also discussed its use as a substrate to produce medicinal compounds and bioactive phenolic compounds (e.g., phenolic acids, flavonoids, vitamin C, fertilizers, dietary fibers, and tannins) as well as its use in nanomaterial synthesis. Based on available evidence, it is obvious that PP has a wide variety of applications, and thus, developing an efficient system to utilize PP adequately will help to completely utilize its potential benefits. The biorefinery approach shows promising potential for efficient utilization of PP; however, additional studies should be conducted in this area. © 2016, Springer Science+Business Media Dordrecht.

Valorization of potato peel: a biorefinery approach - Pathak, P. D.; Mandavgane, S. A.; Puranik, N. M.; Jambhulkar, S. J; Kulkarni, B. D. (Critical Reviews in Biotechnology, 2017)
Potato is the fourth main crop consumed worldwide and is an important constituent in the human diet. Consequently, potato is widely used in food-processing industries. However, these industries generate massive amounts of potato peel (PP) as a by-product, which is usually considered a waste, and is discarded. Interestingly, recent research suggests that PP is a valuable source of bioactive compounds, which can be converted into value-added products. In this study, we review the physicochemical composition and valorization of PP. In addition to being used as a dietary fiber or medicine, the value-added products obtained by the fermentation of PP have multiple uses, including their use as adsorbents, biocomposites and packaging materials. These products can also be used in energy production, biopolymer film development, corrosion inhibition and the synthesis of cellulose nanocrystals. The biorefinery approach for PP will increase the value of this waste by producing an array of value-added products and reducing extensive waste generation.
Keywords: Bioactive compounds; Biorefinery; Potato peel; Valorization; Value-added products

Bubble size prediction in gas-solid fluidized beds using genetic programming - Sonolikar, R. R.; Patil, M. P.; Mankar, R. B.; Tambe, S. S.; Kulkarni, B. D. (Current Science, 2018)
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.

Optimization of the in situ transesterification step for biodiesel production using biomass of Yarrowia lipolytica NCIM 3589 grown on waste cooking oil - Katre, Gouri; Raskar, Shubham; Zinjarde, Smita; Kumar, V. Ravi; Kulkarni, B. D.; RaviKumar, Ameeta (Energy, 2018)
The in situ (one-step) acid-catalyzed transesterification step for conversion to biodiesel of biomass from oleaginous yeast Yarrowia lipolytica grown on waste cooking oil (WCO) is studied. The process yield of biodiesel was optimized by investigating effects of various parameters, namely, biomass, methanol, chloroform, catalyst, temperature, time and sonication. A Plackett-Burman statistical design of experiments revealed that biomass is the most significant factor influencing biodiesel (FAME, fatty acid methyl ester) production. Subsequently, a one variable design (OVD) of experiments for increased biomass loadings showed higher yields of FAME with no additional requirement of reactants, solvents or special equipment. The biomass grown on WCO had a lipid productivity of 0.042 g L-1 h(-1) and 4 g of this loading gave a high FAME yield of 0.88 gin 8 hat 50 degrees C with methanol: chloroform (10:1) and acid catalyst (0.2 M H2SO4,1.0 ml g(-1)). The FAME profile had desirable amounts of saturated (32.81%), monounsaturated (36.41%), polyunsaturated (30.59%) methyl esters. The predicted and experimentally determined physico-chemical properties of FAME were found in accordance with specified international standards. Thus, the direct one-pot in situ transesterification reaction using Y. lipolytica biomass grown on WCO provides a high yield of biodiesel with potential applicability while simultaneously addressing the management of this pollutant. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords: Biodiesel; FAME; In situ transesterification; One -step; Waste cooking oil; Yarrowia lipolytica

Sustainability assessment of brick work for low-cost housing: a comparison between waste based bricks and burnt clay bricks - Joglekar, S. N. (Sustainable Cities and Society, 2018)
Manufacturing of bricks, using clay or fly ash, is one of the major contributors to greenhouse gas emissions as their manufacturing involves utilization of coal and cement. To overcome this limitation, alternative construction materials are developed by author using industrial and agro wastes like cotton mill waste, recycled paper mill waste, and rice husk ash. This work aims at performing a sustainability assessment of burnt clay bricks and bricks made of industrial and agro wastes used for brickwork in a low-cost house. The criteria considered for the assessment are economic, environmental, social, and technical aspects for manufacture of bricks and use of different bricks for brickwork. For the evaluation of environmental criterion, a life cycle assessment (LCA) tool is used. Overall sustainability index (SI) is calculated for alternatives based on the various criteria using MIVES approach. The relative SIs of clay and fly ash bricks, were 0.25 and 0.26, respectively. Overall, bricks made of industrial and agro wastes are found more sustainable with the highest SI for cotton waste bricks (0.94). Sensitivity analysis also confirmed that brickwork from waste based bricks is more sustainable compared to brickwork made from clay brick or fly ash brick.
Keywords: Life cycle assessment; Low cost housing; Multicriteria decision; Sustainability index; Sustainable construction material

Process of fruit peel waste biorefinery: a case study of citrus waste biorefinery, its environmental impacts and recommendations - Joglekar S.N.; Pathak, P. D.; Mandavgane, S. A.; Kulkarni, B. D. (Environmental Science and Pollution Research, 2019)
Fruit peels are a rich source of cellulose, hemicellulose, phenolic compounds, and terpenic compounds. Thus, they have the potential to be a novel renewable, sustainable, and low-cost raw material (source) for the production of several value-added products based on framework and concepts such as waste hierarchy that includes biofertilizers, dietary fiber, animal feed, industrial enzymes, substrate for the bioactive compounds production, synthesis of nanomaterials, and clean energy (from residual biomass). With a view of evaluating the environmental burden of biorefinery, a life cycle assessment (LCA) is performed for a representative citrus waste (CW) biorefinery. The functional unit used for LCA was set as 2500 kg of CW processed. The overall GWP was observed to be 937.3 kg CO2 equivalent per 2500 kg of CW processed. On further analysis of the environmental impact, it was found that different steps contributed significantly, as shown by the various environmental indicator values. Alternative advanced process intensification technologies like microwave and ultrasound-assisted steps replacing the conventional steps when implemented show considerable reduction in environmental indicator values. The variations in the contribution to environmental indicators should be considered during the design and process selection of biorefineries.

Biomass ashes as potent adsorbent for pesticide: prediction of adsorption capacity by artificial neural network - Gokhale, N. A.; Trivedi, N. S.; Mandavgane, S. A.; Kulkarni, B. D. (International Journal of Environmental Science and Technology, 2020)
Biomass ashes are used for adsorption of herbicides from aqueous solution. A relationship between physicochemical properties of biomass ashes such as carbon-hydrogen-nitrogen content (CHN analysis), silica content and BET surface area with their adsorption capacity was established and modeled using artificial neural network. 2,4-Dichlorophenoxyacetic acid (2,4-D) a commonly used herbicide is chosen a representative for this study. The artificial neural network model was trained, validated and tested using 35 data sets and was equipped with nine neuron hidden layers having tansig (tangent sigmoid) transfer function and an output layer with purelin (purely linear) transfer function. This model can be used to predict 2,4-D removal efficacy of any biomass ash by knowing its physicochemical properties like C, H, N, Si and BET surface area.
Keywords: 2; 4-D; Adsorption capacity; Artificial neural network; Biochar; biomass ash

Valorization of cow urine and dung: A model biorefinery (2018) - Mandavgane, S. A.; Kulkarni, B. D. (Waste and Biomass Valorization, 2020)
Cow is one of the most common livestock of Indian farmers. Small farm households are strongly dependent on livestock for income as it contributes nearly 16% of their total earnings. As much as two-thirds of rural community depends on livestock for their livelihood. Farmers in India maintain a hybrid farming system, that is, a combination of crop cultivation and livestock, which are complementary to each other. Traditionally, the output of one enterprise is the input of another enterprise. Recent research suggests that in addition to milk, urine and dung obtained from cow are valuable resources of bioactive compounds, which can be converted into value-added products. In this review, we provide an outline for the physicochemical composition and valorization of cow urine and dung. They are utilized in agriculture as a pesticide, manure, and acts as a soil rejuvenator. Cow dung is used for bio energy production by fermentation and gasification. Medicinal and cosmetic products are prepared following ayurvedic formulations described in ‘Panchagavya’. Cow dung ash is used as adsorbent, construction material, mosquito repellent and electrolyte. A case study of manufacturing unit producing arrays of products from cow urine and dung is also presented.
Keywords: Bio gas; Cow dung; Cow urine; Waste utilization