@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 = {<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>},
	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.}
}
@conference { ISI:000382147300037,
	title = {Use genetic programming for selecting predictor variables and modeling in process identification},
	booktitle = {2016 Indian Control Conference (ICC)},
	year = {2016},
	note = {2nd Indian Control Conference (ICC), Indian Inst Technol, Hyderabad, INDIA, JAN 04-06, 2016},
	month = {JAN},
	pages = {230-237},
	publisher = {IEEE Control Syst Soc; Honeywell; Mathworks; ABB R \& D Div; GE; Mahindra Ecole Centrale},
	organization = {IEEE Control Syst Soc; Honeywell; Mathworks; ABB R \& D Div; GE; Mahindra Ecole Centrale},
	address = {345 E 47th ST, New York, NY 10017 USA},
	abstract = {<p>Availability of an accurate and robust dynamic model is essential for implementing the model dependent process control. When first principles based modeling becomes difficult, tedious and/or costly, a dynamic model in the black-box form is obtained (process identification) by using the measured input-output process data. Such a dynamic model frequently contains a number of time delayed inputs and outputs as predictor variables. The determination of the specific predictor variables is usually done via a trial and error approach that requires an extensive computational effort. The computational intelligence (CI) based data-driven modeling technique, namely, genetic programming (GP) can search and optimize both the structure and parameters of a linear/nonlinear dynamic process model. It is also capable of choosing those predictor variables that significantly influence the model output. Thus usage of GP for process identification helps in avoiding the extensive time and efforts involved in the selection of the time delayed input-output variables. This advantageous GP feature has been illustrated in this study by conducting process identification of two chemical engineering systems. The results of the GP-based identification when compared with those obtained using the transfer function based identification clearly indicates the outperformance by the former method.</p>},
	keywords = {dynamic model, genetic programming, predictor variable, process identification, Sensitivity analysis},
	isbn = {978-1-4673-7993-9},
	author = {Verma, Devendra and Goel, Purva and Patil-Shinde, Veena and Tambe, Sanjeev S.}
}
