@article { ISI:000379250900005, title = {High ash char gasification in thermo-gravimetric analyzer and prediction of gasification performance parameters using computational intelligence formalisms}, journal = {Chemical Engineering Communications}, volume = {203}, number = {8}, year = {2016}, month = {JAN}, pages = {1029-1044}, publisher = {TAYLOR \& FRANCIS INC}, address = {530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA}, abstract = {

The coal gasification is a cleaner and more efficient process than the coal combustion. Although high ash coals are commonly utilized in the energy generation, systematic gasification kinetic studies using chars derived from these coals are scarce. Accordingly, this paper reports the development of the data-driven models for the gasification of chars derived from the high ash coals. Specifically, the models predict two important gasification performance parameters, viz. gasification rate constant and reactivity index. These models have been constructed using three computational intelligence (CI) methods, namely genetic programming (GP), multilayer perceptron (MLP) neural network (NN), and support vector regression (SVR). The inputs to the CI-based models consist of seven parameters representing the gasification reaction conditions and properties of high ash coals and chars. The data used in the modeling were collected by performing extensive gasification experiments in the CO2 atmosphere in a thermo-gravimetric analyzer (TGA) using char samples derived from the Indian coals containing high ash content. Values of the two gasification performance parameters were obtained by fitting the experimental data to the shrinking unreacted core (SUC) model. It has been observed that all the CI-based models possess an excellent prediction accuracy and generalization capability. Accordingly, these models can be gainfully employed in the design and operation of the fixed and fluidized bed gasifiers using high ash coals.

}, keywords = {Char gasification kinetic modeling, Data-driven modeling, genetic programming, Multilayer perceptron neural network, support vector regression, Thermo-gravimetric analyzer}, issn = {0098-6445}, doi = {10.1080/00986445.2015.1135795}, author = {Patil-Shinde, Veena and Saha, Sujan and Sharma, Bijay K. and Tambe, Sanjeev S. and Kulkarni, Bhaskar D.} } @article { ISI:000371228200032, title = {Prediction of reactivity ratios in free radical copolymerization from monomer resonance-polarity (Q-e) parameters: genetic programming-based models}, journal = {International Journal of Chemical Reactor Engineering}, volume = {14}, number = {1}, year = {2016}, month = {FEB}, pages = {361-372}, publisher = {WALTER DE GRUYTER GMBH}, address = {GENTHINER STRASSE 13, D-10785 BERLIN, GERMANY}, abstract = {

The principal deficiency of the widely utilized Alfrey-Price (AP) scheme for computing reactivity ratios in the widely used free radical copolymerization is that it ignores important factors, such as the steric effects. This often leads to inaccurate reactivity ratio predictions by AP model. Accordingly, in this study, exclusively data-driven, Q-e parameter-based new models have been developed for the reactivity ratio prediction in free radical copolymerization. In the model development, a novel artificial intelligence formalism known as {\textquoteleft}{\textquoteleft}genetic programming (GP){\textquoteright}{\textquoteright} that performs symbolic regression has been employed. The GP-based models possess a different functional form than AP model. Further, parameters of GP-based models were fine-tuned using Levenberg-Marquardt (LM) nonlinear regression method. A comparison of AP, GP and GP-LM as well as artificial neural network (ANN)-based models indicates that GP and GP-LM models exhibit superior reactivity ratio prediction accuracy and generalization performance (with correlation coefficient magnitudes close to or greater than 0.9) when compared with AP and ANN models. The GPbased reactivity ratio prediction models developed here due to their higher accuracy and generalization capability have the potential of replacing the widely used AP models.

}, keywords = {Alfrey-Price scheme, free radical copolymerization, genetic programming, reactivity ratio, symbolic regression}, issn = {2194-5748}, doi = {10.1515/ijcre-2014-0039}, author = {Shrinivas, K. and Kulkarni, Rahul P. and Shaikh, Saif and Ghorpade, Ravindra V. and Vyas, Renu and Tambe, Sanjeev S. and Ponrathnam, S. and Kulkarni, Bhaskar D.} } @article {44229, title = {Application of genetic programming (GP) formalism for building disease predictive models from protein-protein interactions (PPI) data}, journal = {IEEE-ACM Transactions on Computational Biology and Bioinformatics}, volume = {15}, year = {2018}, month = {FEB}, pages = {27-37}, type = {Article}, abstract = {

Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approach for predicting PPIs related to a disease. In this case study, a dataset consisting of 135 PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalization ability. A high correlation coefficient (CC) magnitude of 0.893, and low root mean square error (RMSE), and mean absolute percentage error (MAPE) values of 478.221 and 0.239, respectively, were achieved for both the training and test set outputs. To validate the discriminatory nature of the model, it was applied on a dataset of diabetes complexes where it yielded significantly low CC values. Thus, the GP model developed here serves a dual purpose: (a) a predictor of the binding energy of cancer related PPI complexes, and (b) a classifier for discriminating PPI complexes related to cancer from those of other diseases.

}, keywords = {Binding energy, cancer, Disease, genetic programming, machine learning, protein-protein interactions, symbolic regression}, issn = {1545-5963}, doi = {10.1109/TCBB.2016.2621042}, author = {Vyas, Renu and Bapat, Sanket and Goel, Purva and Karthikeyan, Muthukumarasamy and Tambe, Sanjeev S. and Kulkarni, Bhaskar D.} }