Improved ANN-Based Approach Using Relative Impact for the Prediction of Thermal Coal Elemental Composition Using Proximate Analysis

[Image: see text] The basic properties of coal influence various procedures of power generation, such as the design of power generation plants, estimation of the current condition of boilers, and total efficiency of power plants. The elemental composition is a needed factor in evaluating the process of chemical conversion and predicting the flow of flue gas and the quality of air in coal combustion. In the past, several relationships have been established using ultimate and proximate analyses. This study aims to predict the elemental compositions of 104 thermal coals used for coal-fired power plants in South Korea using an artificial neural network (ANN) that uses proximate analysis values as input parameters. The ANN-based model was optimized with six activation functions and four hidden layers after evaluating various performance indices, including R(2), mean square error (MSE), and epoch, then additional calculations were derived to compare performances from previous research using the mean absolute error (MAE), average absolute error, and average bias error. It was found that the best topology was established using the Levenberg–Marquardt activation function and 10 hidden layers, resulting in the highest R(2) value and smallest MSE of all topologies tested. As a result, the relative impact on calculation accuracy was derived from ANN hidden layers to analyze prediction accuracies of carbon, hydrogen, and oxygen compositions. Accuracy was improved over previous results by 4.71–0.91% via coal rank division topology optimization. Based on the MAE, the current results are even close in performance to those of adaptive neuro-fuzzy inference systems. They also outperformed previous research models by 5.40 and 7.39% in terms of MAE accuracy. Applicability of the ANN was also analyzed with limitations of the chemical composition of ANNs and present reinforcement measures in the future studies through qualitative analysis comparisons based on Fourier transform infrared spectroscopy. Consequently, the relative effect was derived from the ANN hidden layer weight for specific calculation of the relationship between elemental composition and proximate analysis properties. As a result, it was possible to qualitatively analyze how the proximate analysis value affects the composition of elements and calculate the ratio accordingly. The findings of this study provide an improved and efficient approach to predicting the elemental composition of thermal coal, based on data from South Korean power plants. Also, further research can follow schematics from this study with the applicability and accessibility of the ANN.