Research progress on early warning mechanisms and technologies in the physicochemical process of coal spontaneous combustion

This is the authors' abstract. We don't add key points for this paper.

Coal spontaneous combustion (CSC) is a major safety risk in deep coal mining, and its evolutionary process has strong concealment characteristics. Traditional single-indicator warning methods have difficulty meeting the requirements for early and accurate CSC identification. This study aims to systematically review the warning mechanisms and technological progress in the physical and chemical processes of CSC, clarify the limitations of existing methods, and explore the development path of intelligent warnings. First, the evolution model of CSC and its nonlinear threshold characteristics were explained from the perspective of the CSC mechanisms, laying a theoretical foundation for phased warning. Subsequently, experimental acquisition methods and on-site perception techniques for CSC warning information were summarized, and the applicability, advantages, and disadvantages of various monitoring methods were compared. The inherent shortcomings of a single information source in terms of spatiotemporal coverage and uncertainty were highlighted. On this basis, the research status of multisource information fusion warning is summarized, and a multisource information fusion technology for CSC based on sound, electricity, temperature, and gas data is proposed. The main findings indicate that there are clear mutation thresholds between CSC stages, and dominant signals vary in different stages. Multisource fusion can effectively compensate for the shortcomings of a single indicator. The potential advantages and development directions of large-model technology in multisource information fusion, mechanism embedding, and intelligent decision-making are further explored. This review provides a systematic reference for the theoretical research and engineering application of CSC warnings, which has positive significance for promoting the intelligent prevention and control of coal mine safety.