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Rapid Determination of Gross Calorific Value of Coal Using Artificial Neural Network and Particle Swarm Optimization

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Abstract

In this study, the gross calorific value (GCV) of coal was accurately and rapidly determined using eight artificial intelligence models based on big data of 2583 observations of coal samples in the Mong Duong underground coal mine (Vietnam). Accordingly, the volatile matter, moisture, and ash were considered as the key variables (inputs) for determining GCV. Seven artificial neural network (ANN) models were developed to estimate GCV as the first stage. Subsequently, the best ANN model (with the highest performance) was selected as the initial ANN model for the optimization process, i.e., ANN 3-12-9-1 model. The particle swarm optimization (PSO) algorithm was applied to perform a global search for the optimal weights/biases of the selected ANN model. This novel procedure is denoted as PSO-ANN. A variety of performance metrics was used to assess the quality of the training process, as well as the models’ performance in the testing dataset. The results revealed that the models developed in this study could determine GCV rapidly and accurately. Of those, the PSO-ANN model provided the highest accuracy in estimating GCV of coal with a root-mean-squared error of 182.476, the correlation coefficient of 0.964, the variance accounted for of 96.411, and mean absolute percentage error of 0.016. Besides, the analyzed and compared results also indicated that the PSO algorithm played a significant role in improving the accuracy of the ANN model. It was introduced as an alternative solution to determine the GCV of coal in practical engineering rapidly.

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Acknowledgments

This paper was supported by the Center for Mining, Electro-Mechanical research of Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam, and the research team of Innovations for Sustainable and Responsible Mining (ISRM) of HUMG.

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Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Hoang Nguyen

  2. Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, 100000, Vietnam

    Hoang-Bac Bui

  3. Center for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, 100000, Vietnam

    Hoang-Bac Bui

  4. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi, 100000, Vietnam

    Xuan-Nam Bui

  5. Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi, 100000, Vietnam

    Xuan-Nam Bui

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Nguyen, H., Bui, HB. & Bui, XN. Rapid Determination of Gross Calorific Value of Coal Using Artificial Neural Network and Particle Swarm Optimization. Nat Resour Res 30, 621–638 (2021). https://doi.org/10.1007/s11053-020-09727-y

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