An artificial neural network (ANN) is an artificial intelligence technique in which performance can be improved by adapting to the changes in the environment. The efficient manipulation of large amounts of data and the ability to generalize results are the main advantages of neural networks. Considering the advantages of this technique, this present paper aims to perform a comparison between linear methods like Multivariate Regression Analysis (MVRA) and different ANN techniques such as back propagation with regression analysis (BPNN), layer recurrent neural network (LRNN), generalized regression neural network (GRNN) and radial basis neural network (RBNN). This comparison was performed to predict the approximate values of Langmuir volume constant (LVC) and Langmuir pressure constant (LPC) for CO 2 adsorption in coal using proximate and maceral properties of India's major coalfield as input parameters. It is found that RMSE value for RBNN is least followed by GRNN, LRNN, BPNN and MVRA for both LVC and LPC models. Based on the best network, it is found that coal seams from Narayankuri coal mine has highest adsorbing capacity of CO 2 (0.0019791 mol/gm) as compared to other coal seams of this study.
Langmuir volume constant and Langmuir pressure constant are parameters used in determining the nature of Langmuir isotherm. They are the basis for determining the adsorption capacity of any coal samples at different pressure and temperature and also the amount of gas released from these coal samples. The parameters of coal such as moisture, volatile matter, ash content, fixed carbon composition, vitrinite, semi-vitrinite, liptinite, Inertinite, mineral matter, mean and depth are used to relate with Langmuir isotherm constants and estimate sorption capacity. Due to the multiplicity of effective parameters and complexities of these parameters prediction of sorption capacity of coal using the regression analysis is not suitable. In this paper, artificial neural network (ANN) has been proposed to analysis these parameters and to learn which coal sample has more sorption capacity can be. In this paper, a correlation has been formed with parameters of coal samples from different coal mines with the help of regression analysis and ANN tool.
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