02628nas a2200217   4500008004100000022001400041245016400055210006900219260010600288300001400394490000700408520168500415653002602100653002402126653003002150100002202180700002702202700002602229700002602255856012902281       2007                        eng d  a0009-250900aDevelopment of support vector regression (SVR)-based correlation for prediction of overall gas hold-up in bubble column reactors for various gas-liquid systems0 aDevelopment of support vector regression SVRbased correlation fo  aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLANDbPERGAMON-ELSEVIER SCIENCE LTDcDEC  a7078-70890 v623 a<p>The objective of this study was to develop a unified data-driven correlation for the overall gas hold-up for various gas-liquid systems using support vector regression (SVR)-based modeling technique. Over the years, researchers have amply quantified the hydrodynamics of bubble column reactors in terms of the overall gas hold-up. In this work, about 1810 experimental points were collected from 40 open sources spanning the years 1965-2007. The model for overall gas hold-up was established as a function of several parameters which include superficial gas velocity, superficial liquid velocity, gas density, molecular weight of gas, sparger type, sparger hole diameter, number of sparger holes, liquid viscosity, liquid density, liquid surface tension, operating temperature, operating pressure and column diameter of the gas-liquid system. For understanding the hold-up behavior, the data used for training the model was grouped into various gas-liquid systems viz., air-water, gas-aqueous viscous liquids, gas-organic liquids, gas-aqueous electrolyte solutions and gas-liquid systems operated over a wide range of pressure. A generalized model established using SVR was evaluated for its performance for various gas-liquid systems. Statistical analysis showed that the proposed generalized SVR-based correlation for overall gas hold-up has prediction accuracy of 97% with average absolute relative error (% AARE) of 12.11%. A comparison of this correlation with the selected system specific correlations in the literature showed that the developed SVR-based correlation significantly gives enhanced prediction of overall gas hold-up. (C) 2007 Published by Elsevier Ltd.</p>10abubble column reactor10aoverall gas hold-up10asupport vector regression1 aGandhi, Ankit, B.1 aJoshi, Jyeshtharaj, B.1 aJayaraman, Valadi, K.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/development-support-vector-regression-svr-based-correlation-prediction-overall-gas-hold-002624nas a2200265   4500008004100000022001400041245016000055210006900215260008600284300001400370490000800384520154200392653003901934653002501973653002401998653004102022653003002063653003202093100002402125700001602149700002202165700002302187700002602210856012202236       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<p>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.</p>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.  uhttps://library.ncl.res.in/content/high-ash-char-gasification-thermo-gravimetric-analyzer-and-prediction-gasification