Artificial Neural Network Modeling as an Approach to Limestone Blast Production Rate Prediction: a Comparison of PI-BANN and MVR Models | ||
| Journal of Mining and Environment | ||
| مقاله 1، دوره 14، شماره 2، تیر 2023، صفحه 375-388 اصل مقاله (5.9 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2023.12489.2266 | ||
| نویسندگان | ||
| Blessing Olamide Taiwo* 1؛ Gebretsadik Angesom2؛ Yewuhalashet Fissha2، 3؛ Yemane Kide2؛ Enming Li4، 5؛ Kiross Haile6؛ Oluwaseun Augustine Oni1 | ||
| 1Department of Mining Engineering, Federal University of Technology, Akure, Nigeria | ||
| 2Department of Mining Engineering, Aksum University, Aksum, Tigray, Ethiopia | ||
| 3Department of Geosciences, Geo-technology and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita, Japan | ||
| 4School of Resources and Safety Engineering, Central South University, Changsha, China | ||
| 5Universidad Politécnica de Madrid – ETSI Minas y Energía, Ríos Rosas Madrid, Spain. | ||
| 6Ethiopian Ministry of Mines, Mineral Industry Development Institute, Addis Ababa, Ethiopia | ||
| چکیده | ||
| Rock blast production rate (BPR) is one of the most crucial factors in the evaluation of mine project's performance. In order to improve the production of a limestone mine, the blast design parameters and image analysis results are used in this work to evaluate the BPR. Additionally, the effect of rock strength on BPR is determined using the blast result collected. In order to model BPR prediction using artificial neural networks (ANNs) and multivariate prediction techniques, a total of 219 datasets with 8 blasting influential parameters from limestone mine blasting in India are collected. To obtain a high-accuracy model, a new training process called the permutation important-based Bayesian (PI-BANN) training approach is proposed in this work. The developed models are validated with new 20 blast rounds, and evaluated with two model performance indices. The validation result shows that the two model results agree well with the BPR practical records. Additionally, compared to the MVR model, the proposed PI-BANN model in this work provides a more accurate result. Based on the controllable parameters, the two models can be used to predict BPR in a variety of rock excavation techniques. The study result reveals that rock strength variation affects both the blast outcome (BPR) and the quantity of explosives used in each blast round. | ||
| کلیدواژهها | ||
| rock fragmentation؛ blasting improvement؛ soft computing models؛ model prediction evaluation؛ Machine learning | ||
| مراجع | ||
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