[1]. Eloranta, J. W. (1997). Efficiency of blasting vs. crushing and grinding. In Proceedings of the 23rd Conference on Explosives and Blasting Technique, Las Vegas, USA.
[2]. Kahriman, A. (2004). Influence of blasting on the energy consumption of crushing and grinding operations. International Journal of Rock Mechanics and Mining Sciences, 41, 787–796.
[3]. Oluokun, O., & Pub, A. (2025). Energy efficiency in mining operations: Policy and technological innovations. International Journal of Multidisciplinary Research and Growth Evaluation, 5, 994–1008.
[4]. Bilgin, N., Tuncdemir, H., & Demircin, M. A. (2016). Effects of blasting on crushing and grinding efficiency in a limestone quarry. Tunnelling and Underground Space Technology, 51, 295–305.
[5]. Kanchibotla, S. S., Valery, W., & Morrell, S. (1999). Modelling fines in blast fragmentation and its impact on crushing and grinding. In Proceedings of Explo '99 Conference, Kalgoorlie, Australia.
[6]. Zaman, U., Qamar, Zhao, Y., Zaman, S., Batool, K., & Nasir, R. (2024). Energy efficiency and environmental consciousness in the minerals industry amidst digital transition. Resources Policy, 91, 104851.
[7]. Drechsler, M., & Skinner, W. (2023). Commercialisation pathway for low-energy wet/dry gyratory rolls crusher comminution technology. Minerals Engineering, 204, 108419.
[8]. Workman, L., & Eloranta, J. (2003). The effects of blasting on crushing and grinding efficiency and energy consumption. In Proceedings of the Annual Conference on Explosives and Blasting Technique.
[9]. Eloranta, J. W. (1995). The efficiency of blasting versus crushing and grinding. In Proceedings of the International Conference on Mining.
[10]. Zaman, U., Qamar, Zhao, Y., Zaman, S., Batool, K., & Nasir, R. (2024). Energy efficiency and environmental consciousness in the minerals industry amidst digital transition. Resources Policy, 91, 104851.
[11]. Drechsler, M., & Skinner, W. (2023). Commercialisation pathway for low-energy wet/dry gyratory rolls crusher comminution technology. Minerals Engineering, 204, 108419.
[12]. Monjezi, M., Dehghani, H., & Bahrami, A. (2008). Prediction of rock fragmentation due to blasting using multiple regression analysis. Journal of Mining and Environment, 4, 23–30.
[13]. Eloranta, J. W. (1995). The efficiency of blasting versus crushing and grinding. In Proceedings of the International Conference on Mining.
[14]. Monjezi, M., Dehghani, H., & Bahrami, A. (2008). Prediction of rock fragmentation due to blasting using multiple regression analysis. Journal of Mining and Environment, 4, 23–30.
[15]. Bahrami, A., Monjezi, M., Goshtasbi, K., & Ghazvinian, A. (2011). Prediction of rock fragmentation due to blasting using artificial neural networks. Engineering with Computers, 27, 177–181.
[16]. Al-Bakri, A. Y., & Sazid, M. (2021). Application of artificial neural network (ANN) for prediction and optimization of blast-induced impacts. Mining, 1(3), 315-334.
[17]. Basu, J. K., Bhattacharyya, D., & Kim, T. H. (2010). Use of artificial neural network in pattern recognition. International journal of software engineering and its applications, 4(2).
[18]. Guo, H., Nguyen, H., Vu, D. A., & Bui, X. N. (2021). Forecasting mining capital cost for open-pit mining projects based on artificial neural network approach. Resources Policy, 74, 101474.
[19]. Abd Elwahab, A., Topal, E., & Jang, H. (2023). Review of machine learning application in mine blasting. Arabian Journal of Geosciences, 16, 133.
[20]. Charbonneau, P. (2002). An introduction to genetic algorithms for numerical optimization. NCAR Technical Note, 74, 4-13.
[21]. Xing, L. N., Chen, Y. W., & Cai, H. P. (2006). An intelligent genetic algorithm designed for global optimization of multi-minima functions. Applied Mathematics and Computation, 178(2), 355-371.
[22]. Zhang, C., Shao, H., & Li, Y. (2000). Particle swarm optimisation for evolving artificial neural network. In Proceedings of the 2000 IEEE International Conference on Systems, Man, and Cybernetics, Vol. 4, pp. 2487-2490.
[23]. Saubi, O., Jamisola Jr., R.S., Suglo, R.S., & Matsebe, O. (2025). Optimisation of blast-induced rock fragmentation using hybrid artificial intelligence methods at Orapa Diamond Mine (Botswana). Journal of Mining Institute, 275, 179–195.
[24]. Gholami, A. R., & Khoshdast, H. (2025). Intelligent estimation of selectivity index and metallurgical responses of coal bioflotation using artificial neural networks. Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 47, 3713–3731.
[25]. Gebretsadik, A., Kumar, R., Fissha, Y., Kide, Y., Okada, N., Ikeda, H., Mishra, A.K., Armaghani, D.J., Ohtomo, Y., & Kawamura, Y. (2024). Enhancing rock fragmentation assessment in mine blasting through machine learning algorithms: a practical approach. Discover Applied Sciences, 6(5), 1-12.
[26]. Bai, R., Zhang, P., Zhang, Z., Sun, X., Fei, H., Bao, S., Hu, G., & Li, W. (2023). Optimization of blasting parameters and prediction of vibration effects in open pit mines based on deep neural networks. Alexandria Engineering Journal, 70, 261-271.
[27]. Shields, L., Silva, J., Calnan, J., Maldonado, E., & Agioutantis, Z. (2025). Integrating underground blast fragmentation modeling for sustainable mine-to-mill optimization: a focus on blast fragmentation and energy efficiency in comminution circuits. Rock Mechanics and Rock Engineering, 58, 4497–4508.
[28]. Guo, J., Zhao, Z., Zhao, P., & Chen, J. (2024). Prediction and optimization of open-pit mine blasting based on intelligent algorithms. Applied Sciences, 14(13), 5609.
[29]. Bisoyi, S. K., & Pal, B. K. (2022). Optimization of blasting parameters in opencast mines using firefly algorithm and deep neural networks. Sādhanā, 47, 172.
[30]. Ronkin, M. V., Akimova, E. N., & Misilov, V. E. (2023). Review of deep learning approaches in solving rock fragmentation problems. AIMS Mathematics, 8(10), 23900–23940.
[31]. Huan, B., Li, X., Wang, J., & Hu, T. (2025). An interpretable deep learning model for the accurate prediction of mean fragmentation size in blasting operations. Scientific Reports, 15(1), 11234.
[32]. Nielsen, K., & Kristiansen, J. (2020). Blasting–crushing–grinding: Optimisation of an integrated comminution system. In Mine-to-Mill Optimization, CRC Press.
[33]. Valery, W., Duffy, K. A., Jankovic, A., Valle, R., Tabosa, E., & Yelkin, I. (2016). Complete optimisation from mine-to-mill to maximise profitability. In Proceedings of AusIMM Conference.
[34]. Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2021). Explainable AI: A review of machine learning interpretability methods. Entropy, 23, 18.
[35]. Min, J., Song, X., Zheng, S., King, C. B., Deng, X., & Hong, Y. (2024). Applied statistics in the era of artificial intelligence: A review and vision. arXiv preprint, arXiv:2412.10331.
[36]. Zhu, J., Jiang, Z., & Feng, L. (2022). Improved neural network for wastewater treatment processes. Chemosphere, 308, 136116.
[37]. Assis, A., Dantas, J., & Andrade, E. (2025). The performance–interpretability trade-off: A comparative study of machine learning models. Journal of Reliable Intelligent Environments, 11, 1.
[38]. Khoshdast, H. (2019). Practical problems in froth flotation. Hormozgan University Press, Tehran, Iran.
[39]. Gharaei, R. S., Gholami, A., Khoshdast, H., & Biglari, H. (2024). A multi-method machine learning framework for simulating power dynamics in an iron ore cone crusher plant. Mineral Processing and Extractive Metallurgy Review, 1–16.
[40]. Hall, M. A. (1999). Correlation-based feature selection for machine learning. PhD Thesis, University of Waikato, New Zealand.
[41]. Gholami, A., Khoshdast, H., & Hassanzadeh, A. (2021). Hybrid genetic and artificial bee colony algorithms for modeling wastewater bio-treatment processes. Journal of Environmental Management, 299, 113666.
[42]. Khoshdast, H., Gholami, A. R., Hassanzadeh, A., Niedoba, T., & Surowiak, A. (2021). Advanced simulation of chromium removal from wastewater using hybrid neural networks. Materials, 14, 2880.
[43]. Eskandar, H., Heydari, E., Hasanipanah, M., Jalil Masir, M., & Mahmodi Derakhsh, A. (2018). Feasibility of particle swarm optimization and multiple regression for the prediction of an environmental issue of mine blasting. Engineering Computations, 35(1), 363-376.
[44]. Nazari, S., Gholami, A., Khoshdast, H., Li, J., He, Y., & Hassanzadeh, A. (2023). Advanced simulation of quartz flotation using historical data and deep learning methods. Minerals, 13, 128.
[45]. Jarkani, S. A., Khoshdast, H., Shariat, E., & Sam, A. (2014). Modeling the effects of mechanical parameters on hydrodynamic behavior of vertical current classifiers. International Journal of Mining Science and Technology, 24, 123–127.
[46]. Khoshdast, H. (2019). Practical problems in froth flotation. Hormozgan University Press, Tehran, Iran.
)