Modelling Coal Dust Explosibility of Khyber Pakhtunkhwa Coal Using Random Forest Algorithm

Amad Ullah Khan, Saad Salman, Khan Muhammad and Mudassar Habib
Additional contact information
Amad Ullah Khan: Department of Chemical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
Saad Salman: National Centre of Artificial Intelligence, Intelligent Information Processing Laboratory, University of Engineering and Technology, Peshawar 25000, Pakistan
Khan Muhammad: National Centre of Artificial Intelligence, Intelligent Information Processing Laboratory, University of Engineering and Technology, Peshawar 25000, Pakistan
Mudassar Habib: Department of Chemical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan

Energies, 2022, vol. 15, issue 9, 1-20

Abstract: Coal dust explosion constitutes a significant hazard in underground coal mines, coal power plants and other industries utilising coal as fuel. Knowledge of the explosion mechanism and the factors causing coal explosions is essential to investigate for the identification of the controlling factors for preventing coal dust explosions and improving safety conditions. However, the underlying mechanism involved in coal dust explosions is rarely studied under Artificial Intelligence (AI) based modelling. Coal from three different regions of Khyber Pakhtunkhwa, Pakistan, was tested for explosibility in 1.2 L Hartmann apparatus under various particle sizes and dust concentrations. First, a random forest algorithm was used to model the relationship between inputs (coal dust particle size, coal concentration and gross calorific value (GCV)), outputs (maximum pressure ( P max ) and the deflagration index ( K st )). The model reported an R 2 value of 0.75 and 0.89 for P max and K st . To further understand the impact of each feature causing explosibility, the random forest AI model was further analysed for sensitivity analysis by SHAP (Shapley Additive exPlanations). The study revealed that the most critical parameter affecting the explosibility of coal dust were particle size > GCV > concentration for P max and GCV > Particle size > Concentration for Kst. Mutual interaction SHAP plots of two variables at a time revealed that with <200 gm/L concentration, −73 µm size and a high GCV coal was the most explosive at a high concentration (>400 gm/L), explosibility is relatively lower irrespective of GCV and particle sizes.

Keywords: coal dust explosibility; random forest; SHAP (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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