关于加快推动“黑灯”选煤厂建设的探讨

    Discussion on accelerating the construction of “Lights-out” coal preparation plants

    • 摘要: 为推动选煤厂从碎片式智能化向全流程协同智能转型,助力煤炭工业高质量发展与能源清洁高效利用,系统探讨了“黑灯”选煤厂作为选煤智能化高级形态的现实意义与建设路径。在梳理了我国选煤厂智能化建设发展现状的基础上,指出当前建设面临着关键技术尚未成熟、智能化深度不足,管理体系与智能化需求脱节,投资压力大、运维可持续性差,标准体系不健全、复制推广难度大等核心挑战,提出从空间、业务、技术三个维度,采取分阶段、渐进式、务实化的思路,按照“单环节—多系统—全厂智能化”(L1—L2—L3)三级梯度路径逐步推进,并从技术攻关与标准建设、政策引导与激励机制、管理模式变革与人才培育、示范引领与分类推进四个维度给出了针对性对策建议,旨在推动形成可复制、可推广的“黑灯”选煤厂建设模式,为实现选煤全流程无人化、自适应优化与本质安全提供理论支撑与实践参考。

       

      Abstract: To promote the transformation of coal preparation plants from fragmented intelligence to full-process collaborative intelligence, and to support the high-quality development of the coal industry alongside the clean and efficient utilization of energy, this paper systematically explores the practical significance and construction paths of the “Lights-out” coal preparation plant as an advanced stage of intelligent coal preparation. Based on a review of the current status of intelligent construction in China′s coal preparation plants, the study identifies core challenges, including immature key technologies, insufficient depth of intelligence, misalignment between management systems and intelligent requirements, high investment pressure with poor operational sustainability, and an incomplete standard system that hinders replication and promotion. The paper proposes a phased, progressive, and pragmatic approach across three dimensions: space, business, and technology. It outlines a three-level gradient path—“Single Link — Multi-system — Plant-wide Intelligentization” (L1—L2—L3)—to advance construction. Furthermore, targeted countermeasures and suggestions are provided from four perspectives: technical breakthroughs and standardization, policy guidance and incentive mechanisms, management model innovation and talent cultivation, and demonstration leadership and classified promotion. This study aims to foster a replicable and scalable construction model for “Lights-out” coal preparation plants, providing theoretical support and practical references for achieving unmanned full-process operations, self-adaptive optimization, and intrinsic safety in coal preparation.

       

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