In a recent article published in Fire, researchers presented a comprehensive analytical framework, termed INK-FBSD, to holistically understand and manage coal mine fire risks.
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The framework integrates Interpretive Structural Modeling (ISM), the NK model, fuzzy Bayesian network analysis, and System Dynamics (SD) simulation.
Background
Coal mine fire research has historically focused on static analyses of known risk factors and accident patterns primarily through data-driven statistical methods. While these approaches have contributed valuable insights, they often lack a comprehensive understanding of the underlying mechanisms driving fire occurrences.
The complexity of coal mine fires, shaped by interactions among geological, equipment, environmental, and management factors, demands integrated and dynamic models capable of capturing complex couplings and feedback loops.
Previous studies have relied on isolated models, limiting the depth of analysis and overlooked multifactorial dependencies integral to fire risks in mining systems.
This research identifies a gap in adopting dynamic, multidimensional frameworks that combine qualitative and quantitative analysis, probabilistic reasoning, and system simulation to better anticipate and control mine fire hazards.
The Current Study
The technical approach of the study unfolds in four sequentially integrated stages within the INK-FBSD framework.
First, Interpretive Structural Modeling (ISM) is employed to identify and hierarchically structure 31 mining-related fire risk factors across categories, including human behavior, equipment condition, environmental parameters, management practices, and fire protection systems. ISM captures the relationships and influence pathways among these disparate elements, revealing that a well-institutionalized mine safety accountability system is a critical foundational factor.
Second, the NK model - originating from complex systems theory - is applied to quantify how the likelihood of fire accidents escalates when multiple risk factors interact. The model demonstrates that risk increases significantly as risk factors couple, especially in four-factor combinations involving equipment, environment, management, and fire protection.
Third, a fuzzy Bayesian network analysis provides probabilistic risk assessment by estimating overall fire occurrence likelihood and diagnosing key contributory factors based on posterior probabilities. This method identifies excessive coal dust accumulation and deficiencies in fire prevention measures as the primary drivers when a fire event occurs.
Finally, dynamic risk evolution is simulated through System Dynamics modeling, which captures temporal changes and feedback in key risk factors such as equipment degradation and maintenance delays. The SD simulation outputs indicate that without timely intervention, risks can escalate quickly to critical levels within 9 to 15 months, signaling the urgency of continuous monitoring and proactive maintenance in mining operations.
Results and Discussion
The findings of this integrative research reveal several important insights pertinent to mining safety management.
ISM confirms the pivotal role of the mine safety accountability system as a fundamental driver influencing many other risk factors. The coupling risk analysis using the NK model indicates that coal mine fires mostly arise from complex interactions involving equipment failure, environmental hazards, managerial shortcomings, and fire protection lapses rather than from isolated causes.
This multi-factor coupling elevates the probability and severity of fire incidents. The fuzzy Bayesian analysis estimates an overall fire risk probability of approximately 46 %, which places the current mining operational environment in a moderate-to-high risk category. Moreover, it highlights excessive coal dust and inadequate fire prevention as key contributors, underscoring the need for improved dust control and enhanced fire mitigation protocols.
The dynamic simulations paint a serious picture regarding equipment-related risks, showing that degradation and deferred maintenance quickly escalate fire hazards.
Critical infrastructure components like conveyor belts and ventilation systems are identified as particularly vulnerable. The temporal analysis also underscores that risks related to poor equipment contact, pipeline aging, and delayed repairs reach severe levels as early as the 8th to 15th month if left unaddressed. Furthermore, the study highlights management risks linked to insufficient technical personnel and failures in enforcing safety responsibility systems, which can ramp to high-risk conditions within months. The practical implication is that mining enterprises must institutionalize robust safety accountability frameworks, ensure adequate staffing, and rigorously implement safety audits.
Conclusion
This study presents a novel, multidimensional analytical framework for understanding and managing coal mine fire risks by integrating structural modeling, probabilistic reasoning, risk coupling quantification, and system dynamics simulation.
The INK-FBSD framework allows mining safety professionals to systematically identify root causes, evaluate multifactor interactions, and anticipate the dynamic progression of fire hazards.
Key contributions include establishing the mine safety accountability system as the central institutional factor, revealing the significant amplification of risk due to factor coupling, and emphasizing principal hazards such as coal dust accumulation and equipment maintenance lapses.
The framework’s dynamic perspective highlights the critical time windows for interventions to prevent risk escalation. Consequently, the research provides actionable guidance tailored to mining operations, recommending strengthened institutional controls, technical staffing, comprehensive inspection regimes, dust and fire hazard mitigation, and adaptive safety scheduling.
Adoption of this integrated approach promises improved fire risk management and enhanced occupational safety in underground coal mining environments.
Journal Reference
Tan S., Shi J., et al. (2025). Dynamic and Multidimensional Risk Assessment Methodology for Coal Mine Fire Prevention: An INK-FBSD Approach. Fire 8(12):456. DOI: 10.3390/fire8120456, https://www.mdpi.com/2571-6255/8/12/456