جستجوی مقالات مرتبط با کلیدواژه « optimization » در نشریات گروه « مهندسی معدن »

The global growth of aluminum demand with the modernization of our society has led to the interest in developing alternative methods to produce aluminum from non-bauxite and low-grade resources such as shale bauxites. For such reserves, the conventional Bayer process is challenging and is not efficient to extract aluminum, and the sintering process is known to be effective. Thus, this study aimed to scrutinize the technical feasibility of alumina extraction from an Iranian low-grade (shale) bauxite ore containing 36.22% Al2O3, 22.11% SiO2, 20.42% Fe2O3, 3.33% TiO2, and 3.13% CaO. In this regard, the sintering process with lime-soda followed by alkaline leaching was adopted to extract alumina, and response surface modeling was employed to assess the important parameters such as the sintering temperature, Na2O(caustic) concentration, CaO/SiO2 molar ratio, and Na2O/Al2O3 molar ratio. The findings indicated that the extraction rate improved by increasing the sintering temperature and CaO/SiO2 ratio and decreasing the Na2O(caustic) dose and Na2O/Al2O3 ratio. It was also found that the Na2O(caustic) concentration, sintering temperature, and interactive effect of Na2O(caustic) concentration with Na2O/Al2O3 ratio had the greatest influence on the extraction efficiency. The process optimization was conducted applying the desirability function approach, and more than 71% of Al2O3 was extracted at 1150 °C sintering temperature, 2.1 CaO/SiO2 molar ratio, 0.9 Na2O/Al2O3 molar ratio and 30 g/L Na2O(caustic) dose. Ultimately, it was concluded that a lime-soda sintering process at 1150 °C followed by one-step alkaline leaching with Na2O(caustic) at 90 °C could be metallurgically efficient for treating the low-grade (shale) bauxites.

It is well-established that the response surface methodology (RSM) is commonly employed to establish the differences between the predicted values and those observed experimentally. This study mainly goals on the impact of four drilling factors including weight on the bit (WOB), the rotating rapidity of the bit, RPM, cutting angle , and rock resistance on the penetration rate of the drilling tool. In this examination, three kinds of limestone rocks were considered. The planned assessments were carried out at three stages of the considered four input variables. The statistical analysis was realized using both RSM approach and analysis of variance (ANOVA). This analysis allowed us to develop the appropriate penetration model with a higher determination coefficient of 96.19%, which demonstrates the high correlation between the predicted and experimental data, and consequently, it can be concluded that the obtained model is highly suitable for the prediction of the penetration rate. Also from variance analysis, the results obtained show that rotational speed, RPM, and weight on the bit (WOB) parameters, as well as the nature of the rock, which is determined by the rock compressive resistance, having a significant effect on the penetration rate; however, the rake angle has little effect. Finally, the optimal parameters were determined to find the best possible penetration rate of the drilling tool.

Exploratory drilling is one of the most important and costly stages of mineral exploration procedures, so the continuation of mining activities depends on the gathered data during this stage. Due to the importance of cost and time-saving in the performance of mineral exploration projects, the effective parameters for reducing the cost and time of drilling activities should be investigated and optimized. Road construction and the sequence of the drilling boreholes by drilling rigs are among these parameters. The main objectives of this research were to optimize the overall road construction cost and the difference in length drilled by each drilling rig. The problem has been modeled as a Multi-Objective Multiple Traveling Salesman Problem (MOmTSP) and solved by the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Finally; the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method has been used to find the optimal solution among the solutions obtained by the NSGA-II.

Microwave applications in mining and process metallurgy have been the subject of many studies over the past two decades. This paper reviews the microwave-assisted leaching of copper from high-grade sulfide concentrate of the Sarcheshmeh copper complex. Response surface methodology (RSM) is used to optimize the leaching process. In this research, leaching experiments were carried out in a multi-mode cavity, and times in the presence of varying concentrations of H2O2 with microwave assistance after the leaching process parameters including type and concentration of the oxidizing agent, NaCl concentration, and leaching temperature, were optimized using Taguchi orthogonal array design method. Conventional leaching experiments were also performed to evaluate the influence of microwave radiation. It has been recognized that microwave technology has great potential to improve the extraction efficiency of metals in terms of both reductions in required leaching time and the recovery of valuable metals. Under the optimized conditions, the leaching efficiencies of copper were 75.3% and 42.5% in 3 hours by microwave assistance and conventional leaching methods, respectively..

The production cycle in open-pit mines includes the drilling, blasting, loading, and haulage. Since loading and haulage account for a large part of the mining costs, it is very important to optimize the transport fleet from the economic viewpoint. Simulation is one of the most widely used methods in the field of fleet design. However, it is unable to propose an optimized scenario for which the appropriate metaheuristic method should be employed. This paper considers the Sungun copper mine as the case study, and attempts to find the most feasible transportation arrangement. In the first step, in this work, we compare the flexible dispatching with the fixed allocation methods using the Arena software. Accordingly, the use of flexible dispatching reveals the increase in the production rate (20%) and productivity (25%), and the decrease (20%) in the idle time. The firefly metaheuristic algorithm used in the second step shows that the combined scenario of the 35-ton and 100-ton trucks is the most suitable option in terms of productivity and cost. In another attempt, comparing different heterogeneous truck fleets, we have found that the scenarios 35-100 and 35-60-100-144 increase the production rate by 39% and 49%, respectively. Also, in both scenarios, the production cost decreases by 11% and 21%, respectively.

Designing and optimizing the dimensions of drainage systems is very important for keeping the downstream shells dry and preventing the increase of pore water pressure in the body of earth dams. By optimizing the drainage dimensions, the minimum factor of safety, and consequently the construction costs, can be reduced. The purpose of this research was to optimize the size of horizontal drainage that is affected by some important parameters of the dam. In this study, a homogeneous earth dam was modeled using the Geostudio software. The minimum factor of safety was obtained by changing drainage dimensions, materials, and the slope of the dam body. A two-layer neural network was used to predict the least factor of safety resulted from different scenarios created in the software. By training the neural network based on the data obtained from homogeneous dams, the minimum factor of safety for drainage optimization was extracted. For optimal, an Mfile was fitted to the trained neural network function, by which the optimal values of the dam parameters were calculated.The results showed that the optimum values of drainage dimensions obtained for homogeneous dams for three heights of 10, 20, and 30 m could be generalized to other heights between 10 and 30 m with a simple interpolation.

Production scheduling in underground mines is still a manual process, and achieving a truly optimal result through manual scheduling is impossible due to the complexity of the scheduling problems. Among the underground mining methods, sub-level caving is a common mining method with a high production rate for hard rock mining. There are limited studies about long-term production scheduling in the sub-level caving method. In this work, for sub-level caving production scheduling optimization, a new mathematical model with the objective of net present value (NPV) maximization is developed. The general technical and operational constraints of the sub-level caving method such as opening and developments, production capacity, sub-level mining geometry, and ore access are considered in this model. Prior to the application of the scheduling model, the block model is processed to remove the unnecessary blocks. For this purpose, the floating stope algorithm is applied in order to determine the ultimate mine boundary and reduce the number of blocks that consequently reduces the running time of the model. The model is applied to a bauxite mine block model and the maximum NPV is determined, and then the mine development network is designed based on the optimal schedule.

Waste rock dumping is very important in the production planning of open-pit mines. This subject is more crucial when there is a potential of acid-forming (PAF) by waste rocks. In such a type of mines, to protect the environment, the PAF materials should be encapsulated by non-harmful rocks. Therefore, block sequencing of the mined materials should be in such a way that both the environmental and economic considerations are considered. If non-acid forming (NAF) rocks are not mined in a proper time, then a stockpile is required for the NAF materials, which later on would be re-handled for encapsulation of PAF rocks. In the available models, the focus is on either block sequencing or waste dumping strategy. In this work, an attempt has been made to develop an integrated mathematical model for simultaneous optimization of block sequencing and waste rock dumping. The developed model not only maximizes the net present value (NPV) but also decreases the destructive environmental effects of inappropriate waste dumping. The proposed model, which is solved by a CPLEX engine, is applied to two different iron deposits. Also the performance of the proposed model is cross-checked by applying the available (traditional) models in a two-step manner. According to the results obtained, it can be considered that utilizing the developed model, because of extensive re-handling cost reduction, the NPV improvement is significant, especially when the overall stripping ratio is higher (deposit case A).