Pouya Nobahar

As fuel consumption is a major operating cost in open-pit mining, identifying and quantifying controllable inefficiencies requires continuous monitoring under real production conditions. This study presents a comprehensive analysis of haul truck fuel consumption using an integrated onboard monitoring system installed on a Komatsu 785 truck at a copper mine. The system continuously measured fuel flow, payload, position and speed over 90 days and captured 1780 complete haul cycles over 150 shifts under varying operational, environmental and behavioural conditions. Specific fuel consumption (SFC) analysis revealed that road gradient was the dominant factor, with uphill waste-haul routes consuming 75–80 gr/t.km compared to 65–70 gr/t.km on crusher routes. An optimal payload range of 92–97 tonnes minimised the SFC, while both under-loading and over-loading reduced fuel efficiency. Spatial mapping identified specific high-consumption road segments that required maintenance. Driver behaviour significantly affected fuel use, with aggressive driving increasing the SFC by approximately 7–10% (75–77 gr/t.km) compared to normal driver behaviour (70–72 gr/t.km). Rainfall events increased SFC by 2–3 gr/t.km above the baseline due to elevated rolling resistance. Operational delays, particularly loading queues (45% of total idle time), contributed substantially to unproductive fuel consumption. The findings demonstrated that fuel inefficiencies were largely controllable through targeted operational improvements, including payload optimisation, road maintenance, driver training and dispatch co-ordination. The spatial mapping methodology provided a practical diagnostic tool transferable to other mine sites for identifying energy-intensive haulage segments and prioritising fuel-reduction interventions.

The growing global demand for mineral resources is challenging mining operations to maintain productivity while processing lower-grade ores and increasingly complex deposits. This study presents an integrated framework that leverages machine learning (ML) and high-fidelity simulation to model and support scenario-based decision-making for the blasting–crushing–SAG (Semi-Autogenous Grindin) milling chain using a calibrated flowsheet. Using publicly available data from the Barrick Cortez Mine (Nevada, USA), more than three million operational scenarios were generated using the Integrated Extraction Simulator (IES) to capture system variability and sensitivity. Machine learning meta-models, built using Random Forest and XGBoost methods, were trained on the simulated data and achieved coefficients of determination (R²) exceeding 0.90 across all key outputs, including P20, P50, P80, and mass flow rates at different operational stages. The meta-models accurately reproduced plant-scale behaviour while reducing computational requirements by several orders of magnitude compared with full-scale simulations. SHapley Additive exPlanations (SHAP) analysis revealed that blast-hole diameter, explosive energy parameters, screen cut-size, crusher feed characteristics, and SAG mill operating conditions are the dominant factors impacting downstream particle size distributions. The proposed framework enables near-real-time evaluation of “what-if” operational scenarios and provides transparent, quantitative decision-support for integrated mine-to-mill optimisation.

This study presents an integrated, cost-aware artificial intelligence (AI) meta-modelling framework for mine-to-mill optimisation that couples high-fidelity simulation with data-driven predictive modelling. Using over three million scenarios generated in the Integrated Extraction Simulator (IES), the framework quantifies how upstream design parameters such as burden, spacing, hole diameter, and explosive density propagate through screening, crushing, stockpiling, and grinding to affect downstream costs and throughput. Random Forest-based meta-models achieved predictive accuracies above 90%, enabling the rapid evaluation of technical and financial trade-offs across the mining value chain. Stage-wise cost models were formulated for drilling, blasting, comminution, and material handling to link process variables to costs per tonne. The results reveal clear non-linear cost responses: finer fragmentation reduces the total comminution cost despite higher explosive expenditure, while SAG mill load and speed exhibit U-shaped cost relationships with distinct optimal operating windows. By combining physics-based simulations, machine learning, and cost integration, the framework transforms traditional stage-wise optimisation into a holistic, financially informed decision-support system. The proposed methodology supports real-time, AI-enabled digital twins capable of adaptive mine-to-mill optimisation, paving the way for more efficient and sustainable resource extraction.

The growing global demand for mineral resources is challenging mining operations to maintain productivity while addressing lower-grade ore and increased extraction complexity. Despite the availability of vast datasets across mining stages, much of this information remains underused in decision-making. This study presents an integrated, knowledge-based framework that leverages artificial intelligence (AI) and high-fidelity simulation to model and optimise the full mine to mill process. Using publicly available data from the Barrick Cortez Mine in Nevada, USA, the mining chain from blasting to semi-autogenous grinding (SAG) was modelled using the Integrated Extraction Simulator (IES) from Orica. To mitigate the computational burden of full factorial simulations, three million scenarios were generated to evaluate performance sensitivity. Machine learning models, including linear regression, decision trees, random forests, and XGBoost, were trained and validated. The models achieved an accuracy of more than 90%, underscoring their reliability for predicting process outcomes. SHapley Additive exPlanations (SHAP) were applied to interpret model predictions and quantify feature importance. The findings confirm a strong alignment between simulation and real-world data and highlight key operational parameters that affect downstream process performances. This meta-model approach offers a powerful tool for real-time decision-making, enabling mining operations to improve efficiency, reduce costs, and support sustainable resource management.

This paper presents the machine learning (ML) method, a novel approach that could be a profitable idea to optimize fleet management and achieve a sufficient output to reduce operational costs, by diminishing trucks’ queuing time and excavators’ idle time, based on the best selection of the fleet. The performance of this method was studied at the Zenouz kaolin mine to optimize the type of loader and the number of trucks used to supply the processing plant’s ore demands. Accordingly, five years’ data, such as dates, weather conditions, number of trucks, routes, loader types, and daily hauled ore, were collected, adapted, and processed to train the following five practical algorithms: linear regression, decision tree, K-nearest neighbour, random forest, and gradient boosting algorithm. By comparing the results of the algorithms, the gradient boosting decision tree algorithm was determined to be the best fit and predicted test data values with 85% accuracy. Subsequently, 11,322 data were imported into the machine as various scenarios and daily hauled minerals as output results were predicted for each working zone individually. Finally, the data which had the minimum variation from the selected required scheduled value, and its related data concerning loader type and the number of demanded trucks, were indicated for each day of the working year.