Exploitation
Babatunde Adebayo; Blessing Olamide Taiwo; BUSUYI THOMAS AFENI; Aderoju Oluwadolapo Raymond; Joshua Oluwaseyi Faluyi
Abstract
The quarry operators and managers are having a running battle in determining with precision the rate of deterioration of the button of the drill bit as well as its consumption. Therefore, this study is set to find the best-performing model for predicting the drill bit button's wear rate during rock drilling. ...
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The quarry operators and managers are having a running battle in determining with precision the rate of deterioration of the button of the drill bit as well as its consumption. Therefore, this study is set to find the best-performing model for predicting the drill bit button's wear rate during rock drilling. Also, the rate at which drill bit buttons wear out during rock drilling in Ile-Ife, Osogbo, Osun State, and Ibadan, Oyo State, Southwest, Nigeria was investigated. Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and adaptive moment Estimation-based Long Short-Term Memory (LSTM) machine learning approaches were used to create models for estimating the bit wear rate based on circularity factor, rock grain size, equivalent quartz content, uniaxial compressive strength, porosity, and abrasive properties of the rock. The performance of the models was measured using a new error estimation index and four other convectional performance estimators. The analysis of performance shows that the adaptive moment estimation algorithm-based LSTM model did better and more accurately than the other models. Thus, the LSTM models presented can be used to improve drilling operations in real-life situations.
Blessing Olamide Taiwo; Gebretsadik Angesom; Yewuhalashet Fissha; Yemane Kide; Enming Li; Kiross Haile; Oluwaseun Augustine Oni
Abstract
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 ...
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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.
Blessing olamide Taiwo; Raymond O Aderoju; Olutosin Mojisola Falade; Yewuhalashet Fissha; O B Ogunyemi; A O Omosebi; S. Omeyoma; Oluwatomisin Victoria Adediran; H A Bamidele; Michael Ogundiran
Abstract
Overburden material is typically removed in surface mining operations to expose the primary ore deposit. Because of the presence of trace minerals, environmental pollution and acid drainage are caused when the overburdened materials are removed from the mine site and transported to another location. ...
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Overburden material is typically removed in surface mining operations to expose the primary ore deposit. Because of the presence of trace minerals, environmental pollution and acid drainage are caused when the overburdened materials are removed from the mine site and transported to another location. In order to promote the economic and environmental sustainability of dolomite mining, the waste materials must therefore be evaluated for their environmental impact and potential industrial application. Akoko Edo Nigeria is known for its large production of dolomite and carbonate rock with large tonnage waste. The hydrogeochemical and geotechnical analysis of selected mine in this area is performed by randomly collecting and analyzing soil and water samples from four exploration drill holes using an atomic absorption spectrophotometer. The geotechnical analysis results show that dolomite waste soil is suitable for constriction material addictive such as road subgrade, dam design, highway, and other construction work. According to the study's findings, the mine water is slightly polluted, as measured by both the Overall Index of Pollution (OIP) and the Pollution Load Index (PLI). The chemical analysis of the mine pit water also reveal that the mean value of electrical conductivity, TDS, iron, manganese, copper, and lead all exceed the WHO and SON standards for a safe drinking water. A new pollution assessment model with suitable prediction correlation accuracy (R2= 0.76, mean average error = 0.27) is also developed in this work.
Blessing Olamide Taiwo
Abstract
Assessment of blast results is a significant approach for the improvement of mining operations. The different procedures for investigating rock fragmentation have their limitations, causing different variation prediction errors. Thus every technique is site-explicit, and applicable for a few explicit ...
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Assessment of blast results is a significant approach for the improvement of mining operations. The different procedures for investigating rock fragmentation have their limitations, causing different variation prediction errors. Thus every technique is site-explicit, and applicable for a few explicit purposes. This work evaluates the existing empirical blast fragmentation model predictions in the case study of small-scale dolomite quarries. An attempt is made to compare the prediction accuracy of the modified Kuz-Ram model, Lawal 2021 model, and Kuznetsov-Cunningham-Ouchterlony (KCO) model with the WipFrag© analysis result and proposed artificial neural network (ANN) models. The prediction error analysis of the current models and that of the new proposed ANN models is evaluated using the three model assessment indices. The assessment indices uncover that the KCO model when compared to the modified Kuz-Ram model has the least error for most blast round percentage passing size predicted. However, the proposed artificial neural network models show high prediction exactness in predicting blast fragment mean size than the existing empirical models. Therefore, the proposed ANN models can be used to improve the productivity of small-scale dolomite blasting operation results for practical purposes.
