On the dependence of predictive models on experimental dataset: a spontaneous combustion studies scenario

Khadija Omar Saida Mining and Mineral Processing Engineering Department, Taita Taveta University, Voi, Kenya

Moshood Onifadeb Department of Mining and Metallurgical Engineering, University of Namibia, WindhoekNamibia;c The School of Mining Engineering, University of the Witwatersrand, Johannesburg, South AfricaCorrespondence[email protected]

Bekir Gencc The School of Mining Engineering, University of the Witwatersrand, Johannesburg, South Africa

https://orcid.org/0000-0002-3943-5103

Abiodun Ismail Lawald Department of Mining Engineering, Federal University of Technology, Akure, Nigeria

Jibril Abdulsalame School of Chemical and Metallurgical Engineering, Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, South Africa

Joseph Muchiri Githiriaa Mining and Mineral Processing Engineering Department, Taita Taveta University, Voi, Kenya

Samson Badae School of Chemical and Metallurgical Engineering, Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, South Africa

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ABSTRACT

Spontaneous combustion has been a long-standing challenge affecting coal mines globally as it jeopardises the financial and safety aspect of mining operations. It does not only damage assets but can also injure mineworkers due to inhalation of the noxious emissions or extreme burning. To manage this problem, different experimental methods and predictive models to foresee the propensity of coal to spontaneous combustion were reviewed. The review shows that most predictive models created are dependent on experimental analysis to ascertain their reliability and validity.

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On the dependence of predictive models on experimental dataset: a spontaneous combustion studies scenario

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On the dependence of predictive models on experimental dataset: a spontaneous combustion studies scenario

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