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Prediction of Thermal Coal Ash Behavior of South African Coals: Comparative Applications of ANN, GPR, and SVR

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Abstract

The coal ash fusion characteristics are a significant factor to consider while designing a boiler to match a coal or different coal range. Characterization of coal ash provides the fundamental mechanism that controls the heating efficiency of the coal in a pulverized coal-fired boiler, regarding the coal minerals association. Changes in coal properties could lead to changes in ash properties, i.e., ash fusion temperatures (AFT) and ash elemental composition, which could lead to slagging or fouling issues inside the boiler. The main cause could be attributed to the high temperature of the combustion process and the low melting point of ash, or vice versa. This study aimed to develop AFT prediction models using coal samples from different coalfields to predict the initial deformation temperature (DT), softening temperature (ST), hemispherical temperature (HT) and fluid temperature (FT) of coal ash. The artificial neural network (ANN), Gaussian process regression (GPR) and support vector regression (SVR) are the three machine learning tools used in this modeling. The resulting AFT predictive models indicated that the ANN model predicted DT, ST, HT and FT more adequately and reliably than the GPR and SVR. The Taylor’s diagram, which enabled easy identification of the closeness of the model predictions to the measured data, also indicated that the ANN outperformed all the other models.

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Authors and Affiliations

  1. Department of Mining Engineering, Federal University of Technology, Akure, Nigeria

    Abiodun Ismail Lawal

  2. Department of Civil and Mining Engineering, University of Namibia, Ongwediva, Namibia

    Moshood Onifade & Amtenge Penda Shivute

  3. Dsi-Nrf SARChI Clean Coal Technology Research Group, School of Chemical and Metallurgy, Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, South Africa

    Samson Oluwaseyi Bada & Jibril Abdulsalam

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  1. Abiodun Ismail Lawal
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  2. Moshood Onifade
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  3. Samson Oluwaseyi Bada
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  5. Jibril Abdulsalam
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Correspondence to Abiodun Ismail Lawal.

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Lawal, A.I., Onifade, M., Bada, S.O. et al. Prediction of Thermal Coal Ash Behavior of South African Coals: Comparative Applications of ANN, GPR, and SVR. Nat Resour Res 32, 1399–1413 (2023). https://doi.org/10.1007/s11053-023-10192-6

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