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Habib, M.; Muhammad, K.; , .; Salman, S. Modelling Coal Dust Explosibility of Khyber Pakhtunkhwa Coal. Encyclopedia. Available online: https://encyclopedia.pub/entry/23339 (accessed on 14 July 2025).
Habib M, Muhammad K,  , Salman S. Modelling Coal Dust Explosibility of Khyber Pakhtunkhwa Coal. Encyclopedia. Available at: https://encyclopedia.pub/entry/23339. Accessed July 14, 2025.
Habib, Muddasar, Khan Muhammad,  , Saad Salman. "Modelling Coal Dust Explosibility of Khyber Pakhtunkhwa Coal" Encyclopedia, https://encyclopedia.pub/entry/23339 (accessed July 14, 2025).
Habib, M., Muhammad, K., , ., & Salman, S. (2022, May 25). Modelling Coal Dust Explosibility of Khyber Pakhtunkhwa Coal. In Encyclopedia. https://encyclopedia.pub/entry/23339
Habib, Muddasar, et al. "Modelling Coal Dust Explosibility of Khyber Pakhtunkhwa Coal." Encyclopedia. Web. 25 May, 2022.
Modelling Coal Dust Explosibility of Khyber Pakhtunkhwa Coal
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This entry is adapted from the peer-reviewed paper 10.3390/en15093169

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 (Pmax) and the deflagration index (Kst)). The model reported an R2 value of 0.75 and 0.89 for Pmax and Kst. 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 most critical parameter affecting the explosibility of coal dust were particle size > GCV > concentration for Pmax 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.

coal dust explosibility random forest SHAP

1. Introduction

The explosion is “an event that once initiated, grows rapidly and initially unbounded” [1]. Therefore, the need for coal dust explosion investigation is a factor for safety in the chemical process industries and its storage for potential energy management [2]. Furthermore, coal dust explosion in a confined environment (coal mine and chemical process industries) results in the production of high pressure due to heating and the expansion of air and gases produced, which leads to destruction and human loss.
Therefore, understanding coal dust explosions is significant to finding the governing factors to mitigate them for increasing safety in industrial working environments. It has been reported that coal dust explosibility is affected by particles size, amount of fines [3], ignition temperature [4][5], air quantity [6] and concentration of coal [7][8][9] in an explosive environment [10]. The Pmax (in MPa or bar) indicates the maximum destructive pressure released from a coal dust explosion, and the deflagration index (Kst in MPa or bar-m/s) is reported as the measure of explosibility [11]. The strength of explosibility is represented by the Kst values from no explosion (0) to weak (0–200), intense (200–300) and powerful (> 300) [12].
Sensors are available to monitor dust concentration and size [13], supported by communication through cloud computing [14]. With the emerging fields of IoT and real-time prediction, e.g., water in-rush [15], tailing dam stability [16], coal fire in sealed off regions [17], and stress monitoring in underground mines [18], available sensors for measuring coal quality, size and dust concentration could be used to predict explosibility as an early warning to prevent explosions. However, apart from other causes such as lack of awareness and safety hazard violations, many coal mining accidents are caused by a lack of calibration of sensors and the non-availability of coal dust prediction systems [19].
In the past few years coal dust explosibility has been studied extensively [5][6][7][8][9][10][11][12][13][14][15]. Coal dust emanates from heavy cutting machines (e.g., longwall mining), crushers and during loading on conveyor belts [20]. The particles may remain suspended where air ventilation velocity is high and later settle on the surface after some time in the accessways and mine entries [21]. The water spray lets the dust settle down, which is powdered with an inert material to reduce its explosibility [22]. Recent trends follow the use of AI to model flame propagation [23] of settled coal dust in galleries. Computational fluid dynamics (CFD) based models have been used to model flame propagation using airflow and coal dust measures [24][25][26][27]. Multilinear regression models have been used to model moisture vs. coal explosibility [3]. A generalised model for understanding how different types of explosible coal dust are affected by the coal characteristics and other coal parameters remains a challenge. Rarely has explosibility been modelled using an AI algorithm for investigating various aspects of coal explosibility. Particle dispersion and turbulence [24][28] are vital factors governing dust explosibility, which is dependent on particle concentration, size and shape [26][27][28][29]. Therefore, measuring coal dust characteristics during the air suspension phase can enable early monitoring and warning, and it can be an indirect measure to estimate the inert material required after dust suppression. This work is carried out to address the modelling part for its possible subsequent use in these environments in connection with IoT sensors to predict explosibility before time.
The phenomenon of explosibility was modelled using data collected from three different regions of Khyber Pakhtunkhwa (KP) and tested in a 1.2 L Hartman equipment. Using the fractional factorial design, the required number of tests have been conducted to generate data for modelling explosibility by an AI algorithm. A random forest regression algorithm was used to model the effect of coal properties on the response, i.e., the maximum pressure (Pmax) and the deflagration index (Kst). A game theory-based method, Shapley Additive exPlanations (SHAP) [30], explains how the variation in coal dust properties affects the response. Furthermore, sensitivity analysis is performed to quantify this effect and to identify the safe limits for each parameter to mitigate coal explosibility in the KP coal mines.

2. History and Development

Many researchers have investigated coal dust explosibility to measure the main factors influencing explosibility [2][4][5][6][7][8][9][10][11][31][32][33][34]. Moradi et al. [35] investigated the effect of sizes of coal particles from different mines on the burning rate of coal using a 2-litre closed chamber. The coal dust concentration, pressure and initial temperature were constant at 10000 g/m3, 1.5 bar and 25 °C, respectively. The response of varying particle sizes was recorded, keeping all the parameters stable. The maximum pressure rate and the explosibility index reported an inverse relationship with the particle size, i.e., 44µm and 37 µm had a higher burning velocity than other dimensions [35]. Another study was conducted on coal dust to measure the explosion severity and the ignition sensitivity of different ranks of coal. Lower ranks of coal are reported to be easily ignited with severe explosion due to the highly volatile content and pyrolytic property of coal [35]. Cao et al. [33] used experimental and numerical analysis to understand the explosion severity of coal dust. The simulations showed the behaviour of coal dust particles after the explosion. These results were consistent with the experimental observations; hence, the simulations can be reliably used to model coal dust explosion. Cashdollar [36] used a US Bureau of Mines (USBM) 20-L laboratory chamber to measure the effect of coal dust explosibility. The 20-litre chamber data agree relatively well with those from full-scale experimental mine tests.
Particle size, volatile and oxygen [37] contents are almost equally important in governing the strength of coal dust explosibility. Tan et al. [31] used a pipe apparatus to analyse the effect of change in dust particle size, concentration and a mixture of methane-coal dust on the explosion pressure. Both the particle size and the concentrations varied at five different levels. A high explosibility index Kst and the maximum pressure Pmax were recorded for nano-sized particles compared to micro-particles. Similarly, a 38 L explosive chamber was used for testing coal dust explosibility ignition at different concentrations [32]. A 5 KJ Sobbe igniter ignited coal dust to test if the coal was under or over-fueled at different concentrations. It was observed that coal dust concentrations below 100 g/m3 and 200 g/m3 failed to deflagrate because of insufficient fuel.
Furthermore, higher dust concentrations above 1200 g/m3 and 1400 g/m3 significantly affected the maximum pressure as less oxygen was available to detonate the coal dust sample. When conducting a coal dust explosibility experiment, the range of each parameter plays a vital role in governing the response to the dust explosion. 

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Subjects: Engineering, Chemical
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Muddasar Habib
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Khan Muhammad
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Saad Salman
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Revisions: 2 times (View History)
Update Date: 31 May 2022
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