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Data-driven modeling and simulation framework for material handling systems in coal mines

机译:煤矿物料处理系统的数据驱动建模和仿真框架

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摘要

In coal mining industry, discrete-event simulation has been widely used to support decisions in material handling system (MHS) to achieve premiums on revenues. However, the conventional simulation modeling approach requires extensive expertise of simulation during the modeling phase and lacks flexibility when the MHS structure changes. In this paper, a data-driven modeling and simulation framework is developed for MHS of coal mines to automatically generate a discrete-event simulation model based on current MHS structural and operational data. To this end, a formal information model based on Unified Modeling Language (UML) is first developed to provide MHS structural information for simulation model generation, production information for simulation execution, and output requirement information for defining simulation outputs. Then, Petri net-based model generation procedures are designed and used to automatically generate a simulation model in Arena® based on the simulation inputs conforming to the constructed information model. The proposed framework is demonstrated for one of the largest open-pit coal mines in the USA, and it has been demonstrated that the framework can be used to effectively generate the simulation models that precisely represent MHS of coal mines, and then be used to support various decisions in coal mining such as equipment scheduling.
机译:在煤矿行业中,离散事件模拟已广泛用于支持物料搬运系统(MHS)中的决策,以实现收入溢价。但是,传统的仿真建模方法在建模阶段需要大量的仿真知识,并且在MHS结构发生变化时缺乏灵活性。本文为煤矿的MHS开发了一个数据驱动的建模和仿真框架,以便根据当前的MHS结构和运营数据自动生成离散事件仿真模型。为此,首先开发了基于统一建模语言(UML)的正式信息模型,以提供用于生成仿真模型的MHS结构信息,用于执行仿真的生产信息以及用于定义仿真输出的输出需求信息。然后,基于Petri网的模型生成过程被设计并用于基于符合所构建信息模型的模拟输入在Arena®中自动生成模拟模型。为美国最大的露天煤矿之一演示了所建议的框架,并且已证明该框架可用于有效生成精确代表煤矿MHS的仿真模型,然后用于支持该模型。煤矿开采中的各种决策,例如设备调度。

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