CFD Study of Indian Coal to Assess the Severity of Coal Dust Explosion and Its Classification as Per Explosibility

Abstract

This paper presents a comprehensive study using computational fluid dynamics (CFD) to evaluate the explosibility of Indian coals and classify their explosion severity. A Siwek 20 L explosion chamber was simulated by ASTM standard 1226-19 to analyze coal samples collected from 24 coal mines across various coalfields of India. The explosibility parameters that is, maximum explosion pressure (P_max), maximum rate of pressure rise ((dP/dt)_max), explosion delay time (T_ed), time to reach P_max (T_ep), and deflagration index (K_st) were estimated for each coal sample to evaluate the deflagration index, which measures the severity of explosions. The deflagration index (K_st) of all coal samples varied significantly between 47.90 bar·ms⁻¹ and 109.43 bar·ms⁻¹ indicating weak explosion potentials (0 < K_st < 200) as per OSHA 2009 standards. Based on this result, a classification system can be proposed for Indian coals depending on shared characteristics, which may be helpful in identifying coal according to their deflagration index (degree of severity). Presently, no formal classification system exists for Indian coal, and current assessments rely on USA OSHA regulations. Hence, multivariate statistical techniques, including feature selection, correlation analysis, multiple regression, and hierarchical clustering, were employed to identify the factors influencing explosion severity and to categorize the coal samples. Volatile matter dry (VM_d) and crossing point temperature (CPT) were the most influential factors impacting K_st. A non-linear regression model yielded a polynomial equation with a strong fit (R² = 0.909, std. error of estimate = 5.19%) for predicting the deflagration index and validated with test results. Hierarchical clustering further classified the coal samples into three distinct groups based on their explosion susceptibility: highly susceptible, moderately susceptible, and potentially susceptible. The proposed classification and prediction model can guide industry stakeholders to implement more effective explosion mitigation strategies and safety protocols.