@article { ISI:000250122000026, title = {Gas sorption and transport in polyarylates: effect of substituent symmetry and polarity}, journal = {Journal of Membrane Science}, volume = {303}, number = {1-2}, year = {2007}, month = {OCT}, pages = {244-251}, publisher = {ELSEVIER SCIENCE BV}, type = {Article}, address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}, abstract = {

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}, issn = {0376-7388}, doi = {10.1016/j.memsci.2007.07.018}, author = {Karadkar, Prasad B. and Kharul, Ulhas K. and Bhole, Yogesh S. and Badhe, Yogesh P. and Tambe, Sanjeev S. and Kulkarni, Bhaskar D.} } @article { ISI:000364264800011, title = {Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices}, journal = {Journal of Chromatography A}, volume = {1420}, year = {2015}, month = {NOV}, pages = {98-109}, publisher = {ELSEVIER SCIENCE BV}, address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}, abstract = {

The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (\>0.9) values of the coefficient of determination (R-2) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science. (C) 2015 Elsevier B.V. All rights reserved.

}, keywords = {Artificial intelligence, Gas chromatography, genetic programming, Kovats retention index, Molecular descriptors, Quantitative structure-retention relationships}, issn = {0021-9673}, doi = {10.1016/j.chroma.2015.09.086}, author = {Goel, Purva and Bapat, Sanket and Vyas, Renu and Tambe, Amruta and Tambe, Sanjeev S.} } @article {43658, title = {Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies}, journal = {Journal of the Energy Institute}, volume = {90}, year = {2017}, month = {JUN}, pages = {476-484}, type = {Article}, abstract = {

The higher heating value (HHV) is the most important indicator of a coal{\textquoteright}s potential energy yield. It is commonly used in the efficiency and optimal design calculations pertaining to the coal combustion and gasification processes. Since the experimental determination of coal{\textquoteright}s HHV is tedious and time-consuming, a number of proximate and/or ultimate analyses based correlations which are mostly linear have been proposed for its estimation. Owing to the fact that relationships between some of the constituents of the proximate/ultimate analyses and the HHV are nonlinear, the linear models make suboptimal predictions. Also, a majority of the currently available HHV models are restricted to the coals of specific ranks or particular geographical regions. Accordingly, in this study three proximate and ultimate analysis based nonlinear correlations have been developed for the prediction of HHV of coals by utilizing the computational intelligence (CI) based genetic programming (GP) formalism. Each of these correlations possesses following noteworthy characteristics: (i) the highest HHV prediction accuracy and generalization capability as compared to the existing models, (ii) wider applicability for coals of different ranks and from diverse geographies, and (iii) structurally lower complex than the other CI-based existing HHV models. It may also be noted that in this study, the GP technique has been used for the first time for developing coal specific HHV models. Owing to the stated attractive features, the GP-based models proposed here possess a significant potential to replace the existing models for predicting the HHV of coals. (C) 2016 Energy Institute. Published by Elsevier Ltd. All rights reserved.

}, doi = {10.1016/j.joei.2016.03.002}, author = {Ghugare, Suhas B. and Tambe, Sanjeev S.} } @article {44483, title = {Genetic programming based models for prediction of vapor-liquid equilibrium}, journal = {Calphad-Computer Coupling of Phase Diagrams and Thermochemistry}, volume = {60}, year = {2018}, month = {MAR}, pages = {68-80}, abstract = {The design, operation, and control of chemical separation processes heavily rely on the knowledge of the vapor liquid equilibrium (VLE). Often, conducting experiments to gain an insight into the separation behavior becomes tedious and expensive. Thus, standard thermodynamic models are used in the VLE prediction. Sometimes, exclusively data-driven models are also used in VLE prediction although this method too possesses drawbacks such as a trial and error approach in specifying the data-fitting function. For overcoming these difficulties, this paper employs a machine learning (ML) formalism namely "genetic programming (GP)" possessing certain attractive features for the VLE prediction. Specifically, three case studies have been performed wherein GP-based models have been developed using experimental data, for predicting the vapor phase composition of a ternary, and a group of non ideal binary systems. The inputs to models consists of three pure component attributes (acentric factor, critical temperature, and critical pressure), and as many intensive thermodynamic parameters (liquid phase composition, pressure, and temperature). A comparison of the VLE prediction and generalization performance of the GP-based models with the corresponding standard thermodynamic models reveals that the former class of models possess either superior or closely comparable performance vis-a-vis thermodynamic models. Noteworthy features of this study are: (i) a single GP-based model can predict VLE of a group of binary systems, and (ii) applicability of a GP-based model trained on an alcohol-acetate series data for its higher homolog. The VLE modeling approach exemplified here can be gainfully extended to other ternary and non-ideal binary systems, and for designing corresponding experiments in different pressure and temperature ranges.}, doi = {10.1016/j.calphad.2017.11.002}, author = {Patil-Shinde, Veena and Tambe, Sanjeev S.} }