An Integrated Internet of Things and Prediction-based Optimization approach for real-time surface waste mining in Indian coal mining industry
DOI:
https://doi.org/10.4108/dtip.13143Keywords:
Surface waste mining, Internet of Things, Machine learning, Dynamic dispatching, Fleet management, Discrete-event simulationAbstract
INTRODUCTION: Surface waste mining (SWM) involves non-value-adding activities that affect operational efficiency in the mining industry. Improving fleet utilisation through real-time monitoring and coordinated dispatching can help address these challenges.
OBJECTIVES: This study develops an integrated framework combining Internet of Things (IoT)-based activity recognition with dynamic fleet dispatching to improve productivity and balance excavator utilisation and tipper waiting time.
METHODS: The framework comprises two phases. Phase I processes GPS and RFID telemetry to classify tipper activity states using machine learning. Phase II uses the recognised states to trigger a condition-based dispatching heuristic. A multi-objective model incorporating first-come, first-served queue recursion is evaluated through telemetry classification experiments and discrete-event simulations calibrated using operational data from an opencast coal mine in central India.
RESULTS: The gradient-boosting classifier achieves a macro-F1 score of 0.986. Increasing the fleet from 20 to 36 tippers reduces average excavator idle time from 2.78 to 0.70 hours per 8-hour shift. For the existing 30-tipper fleet, the proposed framework projects a 13.6% increase in productivity, from 4,100 to 4,659 tonnes per shift. Compared with fixed assignment, the dispatching heuristic increases production by 2.5% and reduces tipper waiting time by 66%. Under heterogeneous operating conditions, the heuristic maintains excavator output imbalance at a coefficient of variation of no more than 2.4%.
CONCLUSION: Integrating IoT-based activity recognition with condition-based dispatching provides a data-driven approach to improving fleet productivity and equipment utilisation in surface mining. The framework supports fleet-sizing and dispatching decisions while offering scope for extension to heterogeneous fleets and changing mine layouts.
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