02623nas a2200265 4500008004100000022001400041245016000055210006900215260008600284300001400370490000800384520154200392653003901934653002501973653002401998653004102022653003002063653003202093100002402125700001602149700002202165700002302187700002602210856012102236 2016 eng d a0098-644500aHigh ash char gasification in thermo-gravimetric analyzer and prediction of gasification performance parameters using computational intelligence formalisms0 aHigh ash char gasification in thermogravimetric analyzer and pre a530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USAbTAYLOR & FRANCIS INCcJAN a1029-10440 v2033 a

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.

10aChar gasification kinetic modeling10aData-driven modeling10agenetic programming10aMultilayer perceptron neural network10asupport vector regression10aThermo-gravimetric analyzer1 aPatil-Shinde, Veena1 aSaha, Sujan1 aSharma, Bijay, K.1 aTambe, Sanjeev, S.1 aKulkarni, Bhaskar, D. uhttp://library.ncl.res.in/content/high-ash-char-gasification-thermo-gravimetric-analyzer-and-prediction-gasification02411nas a2200289 4500008004100000022001400041245015200055210006900207260007900276300001200355490000700367520132100374653002401695653003401719653002401753653002101777653002401798100001801822700002401840700001701864700002701881700001501908700002301923700001901946700002601965856013001991 2016 eng d a2194-574800aPrediction of reactivity ratios in free radical copolymerization from monomer resonance-polarity (Q-e) parameters: genetic programming-based models0 aPrediction of reactivity ratios in free radical copolymerization aGENTHINER STRASSE 13, D-10785 BERLIN, GERMANYbWALTER DE GRUYTER GMBHcFEB a361-3720 v143 a

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 ``genetic programming (GP)'' 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.

10aAlfrey-Price scheme10afree radical copolymerization10agenetic programming10areactivity ratio10asymbolic regression1 aShrinivas, K.1 aKulkarni, Rahul, P.1 aShaikh, Saif1 aGhorpade, Ravindra, V.1 aVyas, Renu1 aTambe, Sanjeev, S.1 aPonrathnam, S.1 aKulkarni, Bhaskar, D. uhttp://library.ncl.res.in/content/prediction-reactivity-ratios-free-radical-copolymerization-monomer-resonance-polarity-q-e-102245nas a2200289 4500008004100000022001400041245013800055210006900193260000800262300001000270490000700280520126700287653001901554653001101573653001201584653002401596653002101620653003301641653002401674100001501698700001801713700001601731700003301747700002301780700002601803856012601829 2018 eng d a1545-596300aApplication of genetic programming (GP) formalism for building disease predictive models from protein-protein interactions (PPI) data0 aApplication of genetic programming GP formalism for building dis cFEB a27-370 v153 a

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.

10aBinding energy10acancer10aDisease10agenetic programming10amachine learning10aprotein-protein interactions10asymbolic regression1 aVyas, Renu1 aBapat, Sanket1 aGoel, Purva1 aKarthikeyan, Muthukumarasamy1 aTambe, Sanjeev, S.1 aKulkarni, Bhaskar, D. uhttp://library.ncl.res.in/content/application-genetic-programming-gp-formalism-building-disease-predictive-models-protein