Exploitation
Blessing Olamide Taiwo; Oluwaseun Victor Famobuwa; Melodi Mbuyi Mata; Mohammed Sazid; Yewuhalashet Fissha; Victor Afolabi Jebutu; Adams Abiodun Akinlabi; Olaoluwa Bidemi Ogunyemi; Ozigi Abubakar
Abstract
The purpose of this research work is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo State, aggregate quarries. In addition, an Artificial Neural Network (ANN) model for granite profitability was developed. A structured survey questionnaire was ...
Read More
The purpose of this research work is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo State, aggregate quarries. In addition, an Artificial Neural Network (ANN) model for granite profitability was developed. A structured survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. In this study, the efficacy of granite fragmentation was assessed using the WipFrag software. The relationship between particle size distribution, blast design, blast efficiency, and uniformity index were analyzed using the WipFrag result. The optimum blast design was also identified and recommended for mine production. The result revealed that large burden distances result in bigger X50, X80, and Xmax fragmentation sizes. A burden distance of 2 m and a 2 m spacing were identified as the optimum burden and spacing. The finding revealed that blast mean size and 80% passing mesh size have a positive correlation. The result from this study indicated that the uniformity index has a positive correlation with blast efficiency and a negative correlation with maximum blast fragmentation size. The prediction accuracy of the developed models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), and mean square error (MSE). The error analysis revealed that the ANN model is suitable for predicting quarry-generated profit.
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 ...
Read More
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. ...
Read More
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.
