Research progress and trend analysis of comprehensive prevention and control of coal spontaneous combustion throughout all stages
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摘要:
为深刻了解煤自燃全阶段防控领域的现状及未来趋势，从“监测预警、防治技术、应急救援”3个方面梳理了各阶段研究进展，并提出未来研究方向：运用多元统计分析、物联网、机器学习和人工智能等尖端技术，提升煤自燃预测预报准确性，构建智能化、超前预警与态势研判机制，实现智能化预测预报与超前预警系统；通过多学科交叉研究手段，深刻理解自燃机理、创新防灭火材料、发展新型微生物技术，使得煤自燃防治技术呈现深度研究和多元创新的趋势；在高效、协同、智能的救援机制下，煤自燃应急救援正朝更智能、信息化的方向迈进，期望推动煤自燃防控智能先进发展水平。
Abstract:To gain a profound understanding of the current status and future trends in the comprehensive prevention and control of coal spontaneous combustion, this paper comprehensively reviews stages and research progress in the aspects of “monitoring and early warning, prevention technology, and emergency rescue”. The following future research aspects are proposed: utilizing advanced technologies such as comprehensive multivariate statistical analysis, the Internet of Things (IoT), machine learning, and artificial intelligence to enhance the accuracy of coal spontaneous combustion prediction and forecasting. This involves constructing an intelligent, proactive warning, and situational judgment mechanism to achieve intelligent prediction, forecasting, and proactive warning systems. Through the comprehensive application of interdisciplinary research methods, gaining in-depth insights into the spontaneous combustion mechanism, innovative fire-resistant materials, and the development of new microbial technologies, theses make the technology of spontaneous combustion prevention and control of coal present a trend of in-depth research and multiple innovation. Under the framework of an efficient, collaborative, and intelligent rescue mechanism, the emergency rescue for coal spontaneous combustion is advancing towards a more intelligent and information-oriented direction. The goal is to promote the intelligent advancement of the coal spontaneous combustion prevention and control level.
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图 1 煤自燃监测预警分类
Figure 1. Classification of coal spontaneous combustion monitoring and early warning
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图 3 基于特征温度划分风险等级[28]
Figure 3. Division of risk levels based on the characteristic temperature
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图 5 分布式光纤温度监测装置的结构
Figure 5. Structure of distributed optical fiber temperature monitoring device
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图 7 NT试样抑制剂液预注入前后特征温度点变化
Figure 7. Changes in characteristic temperature points of NT sample before and after pre-injection of inhibitor solution
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图 9 抑制剂的抑制机制示意图[64]
Figure 9. Inhibition mechanism diagrammatic sketch of the inhibitor
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