Mine Economic and Management
Mahdi Pouresmaieli; Mohammad Ataei; Ali Nouri Qarahasanlou; Abbas Barabadi
Abstract
The mining industry operates in a complex and dynamic environment and faces many challenges that can negatively affect sustainable development goals. To avoid these effects, mining needs to adopt strategic decisions. Therefore, it requires effective decision-making processes for resource optimization, ...
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The mining industry operates in a complex and dynamic environment and faces many challenges that can negatively affect sustainable development goals. To avoid these effects, mining needs to adopt strategic decisions. Therefore, it requires effective decision-making processes for resource optimization, operational efficiency, and sustainability. Multicriteria decision-making methods (MCDM) have been considered valuable decision-support tools in the mining industry. This article comprehensively examines MCDM methods and their applications in the mining industry. This article discusses the basic principles and concepts of MCDM methods, including the ability to prioritize and weigh conflicting, multiple criteria and support decision-makers in evaluating diverse options. According to the results, 1579 MCDM articles in mining have been published from the beginning to April 15, 2023, and a scientometric analysis was done on these articles. In another part of this article, 19 MCDM methods, among the most important MCDM methods in this field, have been examined. The process of doing work in 17 cases of the reviewed methods is presented visually. Overall, this paper is a valuable resource for researchers, mining industry professionals, policymakers, and decision-makers that can lead to a deeper understanding of the application of MCDM methods in mining. By facilitating informed decision-making processes, MCDM methods can potentially increase operational efficiency, resource optimization, and sustainable development in various mining sectors, ultimately contributing to mining projects' long-term success and sustainability.
M. Kamran
Abstract
The blasting operation is an important rock fragmentation technique employed in several foundation engineering disciplines such as mining, civil, tunneling, and road planning. Back-break (BB) is one of the adverse effects caused by the blasting operations that produces several effects including vulnerability ...
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The blasting operation is an important rock fragmentation technique employed in several foundation engineering disciplines such as mining, civil, tunneling, and road planning. Back-break (BB) is one of the adverse effects caused by the blasting operations that produces several effects including vulnerability of mining machinery, bench slope design, and risks to the next blast-patterns due to the eruption of gases from several discontinuities in jointed rock masses. Several techniques have been executed by the researchers in order to predict BB in the blasting operations. However, this is the first work to implement a-state-of-the-art Catboost-based t-distributed stochastic neighbor embedding (t-SNE) approach to predict BB. A total of 62 datasets having 12 influential BB-generating features are collected from genuine blasting patterns. A novel dimensionality depletion technique t-SNE that operates the Kullback-Leibler divergence interpretation is employed to tailor the pioneer exaggeration of the blasting dataset. Then the t-SNE dataset obtained is split into a 70:30 ratio of the training and testing datasets. Finally, the Catboost method is implemented on a low-dimensionality blasting database. The performance evaluation criterion confirms that the BB predictive model is more stable with a goodness of fit = 99.04 in the training dataset, 97.26 in the testing datasets, and could anticipate a more accurate prediction. Moreover, the model presented in this work performs superior to the existing publicly available execution of BB. In summary, this model can be practiced in order to predict BB in several rock engineering practices and mining industry scenarios.
A. Hasanzadeh_Sablouei; Seyed M. Moosavirad
Abstract
The electrocoagulation/flotation process is a novel approach in mining industry that is implemented to return Cu metal to the production cycle, which improves copper recovery and reduces waste water. In this research work, the response surface methodology was applied to optimize the factors effective ...
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The electrocoagulation/flotation process is a novel approach in mining industry that is implemented to return Cu metal to the production cycle, which improves copper recovery and reduces waste water. In this research work, the response surface methodology was applied to optimize the factors effective in Cu metal recovery and sludge volume produced from thickener overflow. To this end, the D-optimal experimental design was utilized. The influences of four independent parameters including the electrolysis time, initial pH, current density, and electrodes type were studied to investigate the initial Cu grade percentage (28%) and sludge volume produced from thickener overflow. All these parameters were found to have important effects on the Cu metal recovery and the sludge volume produced. The linear and quadratic models were utilized for the Cu grade and sludge volume, respectively. The importance of the independent variables and the interaction between them was assessed by ANOVA. The optimum operating conditions with 27.22% Cu grade were taken to be: electrolysis time: 6.5 min, initial pH: 6.7, current density: 50.2 A/m2, and electrode type: Fe-Al. Similarly, for the produced sludge volume of 861 cm3, the following conditions were found: electrolysis time: 15 min, initial pH: 4.1, current density: 48.7, and electrode type: Fe-Al. The outcomes underscored a practical viewpoint of electrocoagulation, known as an acceptable method for Cu recovery from mine industrials, especially in mineral processing plants.
