<![CDATA[High energy intensity in material transportation represents a critical bottleneck in coal mining operational efficiency, with diesel consumption accounting for up to 34% of total expenditures. This study approaches fuel consumption as a controllable process variable, aiming to optimize energy utilization by analyzing operator-driven parameters across a fleet of ten Scania G500 units. Using a process optimization framework, telemetry data from the Scania Driver Evaluation (SDE) system (January–June 2025) were analyzed via Multiple Linear Regression (MLR) to identify significant variances in the energy conversion cycle. The results demonstrate that the optimization of specific process inputs, namely brake application frequency, coasting distance, power mode duty cycles, and cruise control utilization, critically dictates energy throughput, yielding a coefficient of determination (R²) of 66.39%. Implementation of data-driven process interventions and standardized operator protocols successfully mitigated energy waste, reducing average fuel intensity by 3.85% (from 89.87 L/100 km to 86.41 L/100 km) in September 2025. This systematic optimization translates to a significant reduction in operational overhead, totalling Rp 77,850,000.00 per month for a 10-unit fleet. Furthermore, by minimizing thermodynamic losses through improved operator control, this study provides a scalable model for decarbonization, directly supporting Indonesia’s Net Zero Emission (NZE) 2060 objectives through quantifiable energy conservation.]]>
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