Envisioning Human–Machine Relationship Towards Mining of the Future: An Overview
Abstract
:1. Introduction
2. Trends of Automation and Implementation of Digital Systems in Mineral Industry
3. Application of Artificial Intelligence in Mining Operations
3.1. Mineral Prospecting and Exploration
3.2. Mineral Excavation
3.3. Mineral Processing
4. Enhancing Human–Machine Collaboration: A Scenario of Roof Bolting Machine Automation
4.1. Factors to Be Considered for an Effective HMI Design
4.2. Designing HMI for an Automated Roof Bolting Machine
5. Contributions and Limitations of Automation in Mineral Extraction Industry
5.1. Contributions of Automation to Mining Operations
5.1.1. Improved Health and Safety in Mining Operations
5.1.2. Reduction in Overall Mining Operating Costs
5.1.3. Increased Productivity
5.1.4. Real-Time Data Acquisition for Sound Decision Making
5.1.5. Remote Monitoring and Control of Equipment
5.2. Challenges and Limitations of Automation in the Mining Industry
5.2.1. Technological and Technical Challenges
5.2.2. Challenging Geology and Harsh Working Conditions
5.2.3. Economical and Cyclical Mining Commodity Price
5.2.4. Regulatory and Legal Issues
6. Conclusions
- (1)
- Human–machine interaction (HMI): developing effective HMIs is critical to fostering trust and ensuring seamless communication between operators and autonomous systems. Alarms and alert mechanisms must be intuitive, enabling swift and efficient responses during system failures or emergencies.
- (2)
- Human factors in automation: successful deployment of autonomous systems requires designs that account for human capabilities and limitations, including the ability to override and manage automated systems when necessary.
- (3)
- Training and trust: operators must be adequately trained to interact with and adapt to automated technologies, ensuring human oversight and operational efficiency.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Autonomous Equipment | Area of Usage in Mining Operations | References |
---|---|---|
Selected Automated Mining Operations | Autonomous drilling | [23,24] |
Explosive charging robots | [19,20] | |
Loading, hauling and Dumping | [9,10,11,24] | |
Conveyor belt inspection and monitoring robots | [12,13] | |
LiDAR and Positioning | [16,17,18,25] | |
Automated Longwall Mining | [21,22,26] | |
GNSS Applications | [14,15] |
Reference | Mineral Exploration and Prospecting | ML Model(s) | Model Performance(s) | |||
---|---|---|---|---|---|---|
Mineral Resource Mapping | Mine Cost Estimation | Mine Planning | Mine Reserve and Grade Estimation | |||
Lee and Oh [29] | ✔ | ANN | Accuracy > 70% | |||
Tessema [33] | ✔ | ANN *, fuzzy-WofE | MSE = 0.0937 SSE = 170.477 | |||
Sun et al. [34] | ✔ | ANN, SVM, RF * | MSE = 0.039, Accuracy = 96.03%, Kappa = 92.06% | |||
Afeni et al. [42] | ✔ | ANN | SE = 0.015, R2 = 0.819, MAE = 2.023 | |||
Atalay [46] | ✔ | XGBoost | RMSE = 0.69, MAE = 0.65 | |||
Jalloh et al. [41] | ✔ | ANN | R2 = 0.8807, MSE = 0.2087 | |||
Nourali and Osanloo [48] | ✔ | RT | RMSE = 219.36, MAE = 178.5 | |||
Zhang et al. [49] | ✔ | ANN, DNN, ACO-DNN * | R2 = 0.991, MAPE = 0.072, VAF = 99.052 | |||
Zheng et al. [51] | ✔ | CFNN, SalpSO-CFNN * | R2 = 0.980, MAE = 179.567, RMSE = 248.401 | |||
Ajak et al. [47] | ✔ | KNN, DT *, SVM, RF, LR, NB | Accuracy = 72%, Probability = 78.6% | |||
Nobahar et al. [52] | ✔ | KNN, DT, LR, RF, GB * | Accuracy = 83% |
Reference | Mineral Excavation | ML Model(s) | Model Performance(s) | |||
---|---|---|---|---|---|---|
Geomechanical Testing | Drilling and Blasting | Slope Stability | Mine Reclamation and Closure | |||
Ogunsola et al. [55] | ✔ | ANN, ANN-GOA *, ANN-SSA, ANN-AOA | R = 0.98498, MSE = 0.0036, VAF = 97.02% | |||
Lawal et al. [54] | ✔ | ANN *, MARS, GA, | RMSEUCS = 0.1248, RMSETS = 0.0332, RMSESS = 0.0639, RMSEYM = 2.91108 | |||
Skentou et al. [58] | ✔ | ANN *, ANN-ICA, ANN-PSO | R = 0.9607 RMSE = 14.8272 | |||
Lawal et al. [56] | ✔ | ANN, MVO-ANN *, SSA-ANN | RMSE = 0.35083 MAE = 0.263396 | |||
Komadja et al. [64] | ✔ | CART, SVR, MARS * | RMSEBIGV = 0.227, R2BIGV = 0.951 | |||
Amiri et al. [67] | ✔ | ANN, ANN-KNN * | R2BIGV = 0.88 R2AOP = 0.95 | |||
Amoako et al. [68] | ✔ | ANN, SVR | MSEFRAG = 0.0031 | |||
Ogunsola et al. [63] | ✔ | ANN | MAEBIGV = 0.1185, MSEBIGV = 0.0316 | |||
Maxwell et al. [70] | ✔ | SVM *, RF, CART, KNN | Accuracy = 86.6% | |||
Li et al. [71] | ✔ | ANN, SVM *, RF | Accuracy = 87.34% | |||
Bui et al. [72] | ✔ | ANN, SVR, M5Rules-GA *, ANN-PSO, ANN-ICA | RMSE = 0.024, R2 = 0.983, VAF = 98.26 |
Reference | Mineral Processing | ML Model(s) | Model Performance(s) |
---|---|---|---|
Hosseini et al. [77] | Final concentrate grade | ANN | R2 = 0.98 |
Bendaouia et al. [76] | Floatation froth | ANN, RF *, GB, SVM, DT, LR | RMSE = 5.40, MAE = 4.58 |
Zarie et al. [74] | Floatation froth | ANN, CNN * | Accuracy = 93.1% |
Jahedsaravani et al. [78] | Batch floatation of copper sulfide | ANN *, ANFIS | RCu = 0.77, GCu = 0.84, Rm = 0.88, Rw = 0.87 |
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Kolapo, P.; Ogunsola, N.O.; Komolafe, K.; Omole, D.D. Envisioning Human–Machine Relationship Towards Mining of the Future: An Overview. Mining 2025, 5, 5. https://doi.org/10.3390/mining5010005
Kolapo P, Ogunsola NO, Komolafe K, Omole DD. Envisioning Human–Machine Relationship Towards Mining of the Future: An Overview. Mining. 2025; 5(1):5. https://doi.org/10.3390/mining5010005
Chicago/Turabian StyleKolapo, Peter, Nafiu Olanrewaju Ogunsola, Kayode Komolafe, and Dare Daniel Omole. 2025. "Envisioning Human–Machine Relationship Towards Mining of the Future: An Overview" Mining 5, no. 1: 5. https://doi.org/10.3390/mining5010005
APA StyleKolapo, P., Ogunsola, N. O., Komolafe, K., & Omole, D. D. (2025). Envisioning Human–Machine Relationship Towards Mining of the Future: An Overview. Mining, 5(1), 5. https://doi.org/10.3390/mining5010005