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考虑煤价波动的露天煤矿中-长期境界动态优化。

Mid-long term boundary dynamic optimization of open-pit coal mine considering coal price fluctuation.

机构信息

College of Mining, Liaoning Technical University, Fuxin, Liaoning, China.

出版信息

PLoS One. 2024 Feb 23;19(2):e0296932. doi: 10.1371/journal.pone.0296932. eCollection 2024.

Abstract

The delineation of the open-pit mining boundary, particularly in the context of medium to long-term planning, forms the foundation of mining design. However, due to the non-linear and dynamic nature of the economic and technical parameters influencing boundary delineation, determining the optimal mining boundary can be exceedingly challenging. Currently, most boundary optimization methods assume that block parameters remain fixed, which results in enterprises assuming a certain level of risk when facing changes in internal and external conditions. In this regard, this paper introduces the concept of "achievement degree" to reflect the risk associated with the results of boundary design. Using coal prices as an example, this article applies the predicted coal price curve to boundary optimization adjustments by specifying the "achievement degree" requirements for various time periods, thereby facilitating risk-controlled and economically optimal boundary decisions. Taking the illustrative case of an idealized small-scale inclined coal seam open-pit mine, adjustments to the boundary closely track variations in coal prices, further enhancing returns. The results demonstrate that the method proposed in this paper can increase overall revenue by approximately 51.15% within the forecast period, while effectively managing risks.

摘要

露天采矿边界的划定,特别是在中长期规划的背景下,是采矿设计的基础。然而,由于影响边界划定的经济和技术参数具有非线性和动态性质,确定最佳采矿边界可能极具挑战性。目前,大多数边界优化方法都假设块体参数保持不变,这导致企业在面临内部和外部条件变化时承担一定的风险。在这方面,本文引入了“实现度”的概念来反映与边界设计结果相关的风险。本文以煤炭价格为例,通过指定各个时间段的“实现度”要求,将预测的煤炭价格曲线应用于边界优化调整,从而实现风险控制和经济最优的边界决策。以理想化的小型倾斜煤层露天矿为例,边界调整紧密跟踪煤炭价格的变化,进一步提高了收益。结果表明,本文提出的方法可以在预测期内使总收入增加约 51.15%,同时有效管理风险。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58b4/10890747/16de0a775e95/pone.0296932.g001.jpg

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