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Artificial Neural Network Modeling as an Approach to Limestone Blast Production Rate Prediction: a Comparison of PI-BANN and MVR Models

محل انتشار: مجله معدن و محیط زیست، دوره: 14، شماره: 2
سال انتشار: 1402
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 116

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لینک ثابت به این مقاله:
https://civilica.com/doc/1676816

شناسه ملی سند علمی:

JR_JMAE-14-2_001

تاریخ نمایه سازی: 28 خرداد 1402

چکیده مقاله:

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 ۲۱۹ datasets with ۸ 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 ۲۰ 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.

کلیدواژه ها:

rock fragmentation ، blasting improvement ، soft computing models ، model prediction evaluation ، Machine learning

نویسندگان

Blessing Taiwo

Department of Mining Engineering, Federal University of Technology, Akure, Nigeria

Gebretsadik Angesom

Department of Mining Engineering, Aksum University, Aksum, Tigray, Ethiopia

Yewuhalashet Fissha

Department of Mining Engineering, Aksum University, Aksum, Tigray, Ethiopia

Yemane Kide

Department of Mining Engineering, Aksum University, Aksum, Tigray, Ethiopia

Enming Li

School of Resources and Safety Engineering, Central South University, Changsha, China

Kiross Haile

Ethiopian Ministry of Mines, Mineral Industry Development Institute, Addis Ababa, Ethiopia

Oluwaseun Oni

Department of Mining Engineering, Federal University of Technology, Akure, Nigeria

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