基于清洁生产原则的开采采矿设计中的废岩减少Q学习方法
Naser Badakhshan1, Ezzeddin Bakhtavar2, Kourosh Shahriar3
1Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran.
Scientific reports
|January 28, 2026
概括
本研究介绍了一种Q学习框架,用于优化矿坑设计,显著降低废石开采和环境成本. 该方法优先考虑可持续性而不是最大利,提高采矿业务的资源效率.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 大规模采矿产生大量的废石,给环境和经济带来挑战.
- 目前用于最终坑界设计的方法经常忽视环境成本,影响可持续性.
- 减少废物产生对于使采矿与可持续发展目标保持一致至关重要.
研究的目的:
- 开发和验证一个新的框架,以优化最终的坑边界设计.
- 将环境成本纳入集体经济价值计算中.
- 为了最大限度地提高矿石的回收和利能力,尽量减少废石的开采.
主要方法:
- 整合数学建模与Q-learning,一个强化学习算法.
- 将环境成本 (预防,减轻,补偿) 明确纳入集体经济价值.
- 使用大规模铜矿矿藏的验证和与Lerchs-Grossmann算法进行比较.
主要成果:
- 在Q-learning框架下,废岩采矿减少了270万.
- 矿石回收量略有下降,减少了50万.
- 计算时间从7.2小时减少到5.8小时,框架优先考虑现实的坑道设计,而不是最大利.
结论:
- 通过纳入环境成本,Q-learning框架提供了一种更可持续的方法来设计最终的坑道极限设计.
- 这种方法提高了资源效率,最大限度地减少了生态影响,并促进了采矿中的清洁生产.
- 基于强化学习的优化促进了采矿行业的可持续性.
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