Search

Productivity in opencast mining, particularly in drill-blast (DB) and surface miner (SM) operations, is crucial for optimizing efficiency and reducing costs. These operations are directly affected by fragmentation, which in turn impacts equipment utilization, loading cycle times, and downstream operations. This study analyzed field data such as rock properties, machine parameters, blast design results, and post-blast fragmentation size (0.15–0.82 m), with 0.45 m identified as the optimal fragmentation size for a 12 m3 shovel bucket. Traditional productivity assessments often use simplistic models that fail to capture the complexities of mining operations. To address this, an explainable machine learning (ML) model was developed, integrating fragmentation size, rock and machine parameters, and geometric factors to evaluate DB and SM operations in opencast coal mines. Various ML techniques, such as artificial neural network (ANN), random forest regression (RFR), gradient boosting regressor (GBT), and support vector regression (SVR), were employed to analyze these parameters. Among these, the RFR model demonstrated the highest accuracy, with a coefficients of determination (R2) of 99.5% for training and 99.2% for testing in DB datasets, and 99.9% for training and 99.5% for testing in SM datasets. Furthermore, the RFR model had the lowest root mean square error, mean absolute error, and mean absolute percentage error of 10.35, 4.788, and 2.1% for DB training datasets, and 5.53, 1.75, and 1.5% for SM training datasets, respectively, underscoring its superior performance. Using SHAP (Shapley Additive exPlanations), the study identified key productivity drivers: SM cycle time, diesel consumption, and coal face length. Fragmentation size, resulting from blasting, was also found to influence shovel efficiency and overall productivity significantly. This paper highlights the effectiveness of ensemble ML models in predicting and analyzing complex productivity dynamics in opencast mining. [ABSTRACT FROM AUTHOR]

To achieve a rational allocation of real-time operational equipment, such as excavators and dump trucks, in open-pit mines, and thereby enhance truck–shovel coordination, this paper addresses the challenges posed by unreasonable on-site scheduling, which includes excessive truck waiting times and prolonged excavator boom-and-dipper operations. Ultimately, the paper aims to attain optimal truck–shovel coordination efficiency. To this end, we construct a scheduling optimization model, with the production capacities of trucks and shovels serving as constraints. The objective functions of this model focus on minimizing transportation costs, reducing truck waiting times, and shortening excavator boom-and-dipper operation durations. To solve this model, we have developed an improved genetic algorithm that integrates roulette wheel selection and elite preservation strategies. The experimental results of our algorithm demonstrate that it can provide a more refined operational equipment scheduling scheme, effectively decreasing truck transportation costs and enhancing equipment utilization efficiency in open-pit mines. [ABSTRACT FROM AUTHOR]