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金属矿山 ›› 2023, Vol. 52 ›› Issue (03): 193-198.

• 机电与自动化 • 上一篇    下一篇

露天矿新能源纯电动卡车的智能调度优化及应用

程 平1 李晓光2 顾清华1,2 江 松1
  

  1. 1. 西安建筑科技大学资源工程学院,陕西 西安 710055;2. 西安建筑科技大学管理学院,陕西 西安 710055
  • 出版日期:2023-03-15 发布日期:2023-04-12
  • 基金资助:
    国家自然科学基金面上项目(编号:51774228);陕西省自然科学基金项目(编号:2017JM5043)。

Intelligent Scheduling Optimization and Application of New Energy Electric Truck in Open-pit Mine

CHENG Ping1 LI Xiaoguang2 GU Qinghua1,2 JIANG Song1   

  1. 1. School of Resources Engineering,Xi′an University of Architecture and Technology,Xi′an 710055,China;2. School of Management,Xi′an University of Architecture and Technology,Xi′an 710055,China
  • Online:2023-03-15 Published:2023-04-12

摘要: 随着我国“双碳”战略的提出,在绿色矿山建设背景下,新能源纯电动卡车在露天矿生产中的应用越来 越广泛。 为了提高新能源纯电动卡车的运输效率,降低矿山生产成本,从卡车的碳排放成本和矿山生产指标角度出 发,综合考虑电池电量、车铲生产能力以及矿石品位等多种影响因素,构建了基于碳排放成本的露天矿新能源纯电动 卡车多目标调度优化模型,并采用非支配邻域免疫算法(NNIA)对模型进行了求解。 通过国内某露天矿的生产数据进 行仿真分析发现,所建立的多目标优化模型求得的调度方案,提高了设备利用效率 9. 5%,增加了 11. 29%的矿石运输 量,同时有效减少了电能消耗所带来的碳排放成本。

关键词: 露天矿, “双碳”目标, 绿色矿山, 电动卡车, 充电需求, 调度模型

Abstract: With the implementation of the " dual-carbon" strategy,the application of new energy electric trucks in openpit mines has become more and more extensive under the background of green mine construction. In order to improve truck transportation efficiency and reduce mine production costs,a multi-objective scheduling optimization model for open-pit mine new energy electric trucks based on carbon emission costs is constructed by comprehensively considering various influencing factors such as battery power,truck shovel production capacity and ore grade,from the perspective of truck carbon emission costs and mine production indicators. The Non-Dominated Neighborhood Immune Algorithm ( NNIA) is adopted to solve the model. Through the simulation analysis of the production data of a domestic open-pit mine,it is found that the scheduling plan obtained by the established multi-objective optimization model increased the equipment utilization efficiency by 9. 5% and increased the ore transportation volume by 11. 29%. Besides that,it effectively reduces the cost of carbon emissions caused by electricity consumption.