Fuzzy inference system using genetic algorithm and pattern search for predicting roof fall rate in underground coal mines

IF 6.9 1区 工程技术 Q2 ENERGY & FUELS International Journal of Coal Science & Technology Pub Date : 2024-01-03 DOI:10.1007/s40789-023-00630-4
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Abstract

One of the most dangerous safety hazard in underground coal mines is roof falls during retreat mining. Roof falls may cause life-threatening and non-fatal injuries to miners and impede mining and transportation operations. As a result, a reliable roof fall prediction model is essential to tackle such challenges. Different parameters that substantially impact roof falls are ill-defined and intangible, making this an uncertain and challenging research issue. The National Institute for Occupational Safety and Health assembled a national database of roof performance from 37 coal mines to explore the factors contributing to roof falls. Data acquired for 37 mines is limited due to several restrictions, which increased the likelihood of incompleteness. Fuzzy logic is a technique for coping with ambiguity, incompleteness, and uncertainty. Therefore, In this paper, the fuzzy inference method is presented, which employs a genetic algorithm to create fuzzy rules based on 109 records of roof fall data and pattern search to refine the membership functions of parameters. The performance of the deployed model is evaluated using statistical measures such as the Root-Mean-Square Error , Mean-Absolute-Error, and coefficient of determination ( \(R_2\) ). Based on these criteria, the suggested model outperforms the existing models to precisely predict roof fall rates using fewer fuzzy rules.

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利用遗传算法和模式搜索的模糊推理系统预测煤矿井下顶板冒落率
摘要 煤矿井下最危险的安全隐患之一是回撤开采过程中的顶板冒落。顶板冒落可能对矿工造成致命或非致命伤害,并阻碍采矿和运输作业。因此,一个可靠的顶板冒落预测模型对于应对这些挑战至关重要。对顶板坠落产生重大影响的不同参数定义不清且无形,这使其成为一个不确定且具有挑战性的研究课题。美国国家职业安全与健康研究所从 37 个煤矿中收集了有关顶板性能的国家数据库,以探索导致顶板坠落的因素。由于受到多种限制,37 个煤矿获得的数据有限,这增加了数据不完整的可能性。模糊逻辑是一种应对模糊性、不完整性和不确定性的技术。因此,本文介绍了模糊推理方法,该方法采用遗传算法,根据 109 条屋顶坠落数据记录创建模糊规则,并通过模式搜索来完善参数的成员函数。所部署模型的性能使用统计量进行评估,如均方根误差、均值-绝对误差和判定系数(R_2)。根据这些标准,建议的模型在使用较少的模糊规则精确预测屋顶倒塌率方面优于现有模型。
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来源期刊
CiteScore
11.40
自引率
8.40%
发文量
678
审稿时长
12 weeks
期刊介绍: The International Journal of Coal Science & Technology is a peer-reviewed open access journal that focuses on key topics of coal scientific research and mining development. It serves as a forum for scientists to present research findings and discuss challenging issues in the field. The journal covers a range of topics including coal geology, geochemistry, geophysics, mineralogy, and petrology. It also covers coal mining theory, technology, and engineering, as well as coal processing, utilization, and conversion. Additionally, the journal explores coal mining environment and reclamation, along with related aspects. The International Journal of Coal Science & Technology is published with China Coal Society, who also cover the publication costs. This means that authors do not need to pay an article-processing charge.
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