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<p>In this second part of a two-part series, this study investigates whether operator practices significantly affect the energy efficiency of hydraulic shovels, using detailed telemetry data. Rather than assuming this relationship, the research systematically tests it through a combination of statistical and regression analyses. First, Welch’s and Kruskal–Wallis ANOVA confirm that operators differ significantly in their energy efficiency (<i>p</i> = 0.0000). Next, correlation analysis links parameters identified in Part I to energy per unit loading rate, and difference regression analysis determines which parameters most strongly influence efficiency variations. The results show that differences in payloads are the primary driver of performance differences between operators, while other factors, such as differences in boom angle, swing-out time, and digging time, show limited or no statistical significance. Importantly, the findings suggest that aligning the least efficient operator’s payload per cycle with that of the most proficient one could improve energy efficiency by 11% and reduce energy costs associated with a 1 tonne/sec loading rate by 14%. By empirically demonstrating the central role of operator practices, this research offers a novel, data-driven pathway for improving shovel performance, reducing operational costs, and advancing sustainability in mining operations.</p>

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Using Telemetry to Assess Operator Effects on Hydraulic Shovel Energy Efficiency. Part II: Assessing Operator Effects

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月19日

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  • Noah Adekunle Aluko,
  • Kwame Awuah-Offei

摘要

In this second part of a two-part series, this study investigates whether operator practices significantly affect the energy efficiency of hydraulic shovels, using detailed telemetry data. Rather than assuming this relationship, the research systematically tests it through a combination of statistical and regression analyses. First, Welch’s and Kruskal–Wallis ANOVA confirm that operators differ significantly in their energy efficiency (p = 0.0000). Next, correlation analysis links parameters identified in Part I to energy per unit loading rate, and difference regression analysis determines which parameters most strongly influence efficiency variations. The results show that differences in payloads are the primary driver of performance differences between operators, while other factors, such as differences in boom angle, swing-out time, and digging time, show limited or no statistical significance. Importantly, the findings suggest that aligning the least efficient operator’s payload per cycle with that of the most proficient one could improve energy efficiency by 11% and reduce energy costs associated with a 1 tonne/sec loading rate by 14%. By empirically demonstrating the central role of operator practices, this research offers a novel, data-driven pathway for improving shovel performance, reducing operational costs, and advancing sustainability in mining operations.