Improve Energy Efficiency in Surface Mines Using Artificial Intelligence

A. Soofastaei, Milad Fouladgar
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Abstract

This chapter demonstrates the practical application of artificial intelligence (AI) to improve energy efficiency in surface mines. The suggested AI approach has been applied in two different mine sites in Australia and Iran, and the achieved results have been promising. Mobile equipment in mine sites consumes a massive amount of energy, and the main part of this energy is provided by diesel. The critical diesel consumers in surface mines are haul trucks, the huge machines that move mine materials in the mine sites. There are many effective parameters on haul trucks’ fuel consumption. AI models can help mine managers to predict and minimize haul truck energy consumption and consequently reduce the greenhouse gas emission generated by these trucks. This chapter presents a practical and validated AI approach to optimize three key parameters, including truck speed and payload and the total haul road resistance to minimize haul truck fuel consumption in surface mines. The results of the developed AI model for two mine sites have been presented in this chapter. The model increased the energy efficiency of mostly used trucks in surface mining, Caterpillar 793D and Komatsu HD785. The results show the trucks’ fuel consumption reduction between 9 and 12%.
利用人工智能提高露天矿的能源效率
本章展示了人工智能(AI)在提高露天矿能效方面的实际应用。建议的人工智能方法已在澳大利亚和伊朗的两个不同的矿区应用,取得了令人鼓舞的结果。矿山现场移动设备消耗大量的能源,而这些能源的主要部分是由柴油提供的。在露天矿山中,主要的柴油消费者是运输卡车,这种大型机器在矿区运输矿山材料。影响卡车燃油消耗的有效参数有很多。人工智能模型可以帮助矿山管理者预测和最大限度地减少运输卡车的能耗,从而减少这些卡车产生的温室气体排放。本章提出了一种实用且经过验证的人工智能方法,用于优化三个关键参数,包括卡车速度和有效载荷以及总运输道路阻力,以最大限度地减少露天矿运输卡车的燃油消耗。本章给出了两个矿区人工智能模型的开发结果。该模型提高了露天采矿中常用卡车、卡特彼勒793D和小松HD785的能源效率。结果表明,卡车的燃油消耗减少了9%至12%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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