Abstract

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.

Department(s)

Mining Engineering

Publication Status

Open Access

Keywords and Phrases

Energy efficiency; Hydraulic shovels; Operator effects; Operator practices; Telemetry

International Standard Serial Number (ISSN)

2524-3470; 2524-3462

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 Springer, All rights reserved.

Publication Date

01 Jan 2025

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