Estimation of gross calorific value of coals using artificial neural networks - Patel, Shagufta U.; Kumar, B. Jeevan; Badhe, Yogesh P.; Sharma, B. K.; Saha, Sujan; Biswas, Subhasish; Chaudhury, Asim; Tambe, Sanjeev S.; Kulkarni, Bhaskar D. (Fuel, 2007)
The gross calorific value (GCV) is an important property defining the energy content and thereby efficiency of fuels, such as coals. There exist a number of correlations for estimating the GCV of a coal sample based upon its proximate and/or ultimate analyses. These correlations are mainly linear in character although there are indications that the relationship between the GCV and a few constituents of the proximate and ultimate analyses could be nonlinear. Accordingly, in this paper a total of seven nonlinear models have been developed using the artificial neural networks (ANN) methodology for the estimation of GCV with a special focus on Indian coals. The comprehensive ANN model developed here uses all the major constituents of the proximate and ultimate analyses as inputs while the remaining six sub-models use different combinations of the constituents of the stated analyses. It has been found that the GCV prediction accuracy of all the models is excellent with the comprehensive model being the most accurate GCV predictor. Also, the performance of the ANN models has been found to be consistently better than that of their linear counterparts. Additionally, a sensitivity analysis of the comprehensive ANN model has been performed to identify the important model inputs, which significantly affect the GCV. The ANN-based modeling approach illustrated in this paper is sufficiently general and thus can be gainfully extended for estimating the GCV of a wide spectrum of solid, liquid and gaseous fuels. (c) 2006 Elsevier Ltd. All rights reserved.
Keywords: Artificial neural network; gross calorific value (GCV); proximate and ultimate analyses

High ash char gasification in thermo-gravimetric analyzer and prediction of gasification performance parameters using computational intelligence formalisms - Patil-Shinde, Veena; Saha, Sujan; Sharma, Bijay K.; Tambe, Sanjeev S.; Kulkarni, Bhaskar D. (Chemical Engineering Communications, 2016)
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

Co-gasification of high ash coal–biomass blends in a fluidized bed gasifier: experimental study and computational intelligence-based modeling - Tiwary, Shishir; Ghugare, Suhas B.; Chavan, Prakash D.; Saha, Sujan; Datta, Sudipta; Sahu, Gajanan; Tambe, Sanjeev S. (Waste and Biomass Valorization, 2018)
Co-gasification (COG) is a clean-coal technology that uses a binary blend of coal and biomass for generating the product gas; it is environment-friendly since it emits lesser quantities of pollutants compared to the coal gasification process. Although coals found in many countries contain high percentages of ash, co-gasification studies involving such coals, and the process modeling thereof, are rare. Accordingly, this study presents results of the co-gasification experiments conducted in a fluidized-bed gasifier (FBG) pilot plant using as a feed the blends of high ash Indian coals with three biomasses, namely, rice husk, press mud, and sawdust. Since the underlying physicochemical phenomena are complex and nonlinear, modeling of the COG process has been performed using three computational intelligence (CI)-based methods namely, genetic programming, artificial neural networks, and support vector regression. Each of these formalisms was employed separately to develop models predicting four COG performance variables, namely, total gas yield, carbon conversion efficiency, heating value of product gas, and cold gas efficiency. All the CI-based models exhibit an excellent prediction accuracy and generalization performance. The co-gasification experiments and their modeling presented here for a pilot-plant FBG can be gainfully utilized in the efficient design and operation of the corresponding commercial scale co-gasifiers utilizing high ash coals.
Keywords: Artificial neural networks; Co-gasification; Computational intelligence; Fluidized bed gasifier; genetic programming; support vector regression

Co-gasification of high ash coal–biomass blends in a fluidized bed gasifier: - Tiwary, Shishir; Ghugare, Suhas B.; Chavan, Prakash D.; Saha, Sujan; Datta, Sudipta; Sahu, Gajanan; Tambe, Sanjeev S. (Waste and Biomass Valorization, 2020)
Co-gasification (COG) is a clean-coal technology that uses a binary blend of coal and biomass for generating the product gas; it is environment-friendly since it emits lesser quantities of pollutants compared to the coal gasification process. Although coals found in many countries contain high percentages of ash, co-gasification studies involving such coals, and the process modeling thereof, are rare. Accordingly, this study presents results of the co-gasification experiments conducted in a fluidized-bed gasifier (FBG) pilot plant using as a feed the blends of high ash Indian coals with three biomasses, namely, rice husk, press mud, and sawdust. Since the underlying physicochemical phenomena are complex and nonlinear, modeling of the COG process has been performed using three computational intelligence (CI)-based methods namely, genetic programming, artificial neural networks, and support vector regression. Each of these formalisms was employed separately to develop models predicting four COG performance variables, namely, total gas yield, carbon conversion efficiency, heating value of product gas, and cold gas efficiency. All the CI-based models exhibit an excellent prediction accuracy and generalization performance. The co-gasification experiments and their modeling presented here for a pilot-plant FBG can be gainfully utilized in the efficient design and operation of the corresponding commercial scale co-gasifiers utilizing high ash coals.