جستجوی مقالات مرتبط با کلیدواژه "معادن روباز" در نشریات گروه "مهندسی معدن"

Loading and hauling operations have the highest share of the production costs of open-pit mines. Therefore, in choosing the loading and hauling machines, paying attention to the economic aspects is necessary. For a better understanding of the financial performance of mining machinery, various performance indicators have been proposed by researchers. This research presents a special form of economic performance index, which indicates the ratio of each machine's revenue to its total cost. For this purpose, the cost and revenue of the transport fleet, including dump trucks, mining shovels, and backhoe excavators, were collected for two years (From September 2017 to September 2019) and determined. As the first step, the total cost and the amount of revenue from the operation of each machine then, the Economic Performance Index (EPI) for each of the loading and hauling machines is calculated and compared with each other. The results show that Backhoe excavators perform better than mining shovels. In addition, by examining different trucks from different manufacturers, it was found that C100 series trucks have the best performance among all trucks. Its average economic performance index value for each of those trucks is 2.82.

Optimization of haulage system in open pit mines is very important due to its high operating costs. Till now a large no. of researches have been conducted to optimize the truck fleet size in an open pit mine in dispatching and non-dispatching modes. These researches have gained many achievements in this regard and still they are going to continue. On the other hand, nowadays, application of artificial intelligence is spreading vastly to optimize systems in the field of (mining) engineering, especially in np-hard problems. One of the new meta-heuristic algorithms in engineering field is Imperialistic Competition (IC), which is developed primarily for electrical systems. Recently, it has found its application in the field of mining systems as well. In this paper, application of IC algorithm for optimization of haulage system in open pit mines is introduced. Implementation of the IC algorithm for Songon Copper mine was validated with comparison of its results with those of Genetic algorithm. Comparison showed promising results. Optimization of the system with IC algorithm improves the mine production from 32 thousand tons as the current situation to 37 thousand tons, while optimization of the system with Genetic algorithm improves the mine production to 35 thousand tones.

Long-term production scheduling in open-pit mines is a crucial issue in mining planning and determines the distribution of cash flow throughout the life of the mine. The purpose of the planning is to maximize the net present value by taking into account all operational constraints such as slope, mixing of different grades, mineral production, and extraction capacity. The uncertainties associated with model data play an important role in optimizing long-term production plans. Among the uncertainties, grade uncertainty plays a major role. In this paper, hybrid models are presented by the Lagrangian relaxation (LR) method, augmented Lagrangian relaxation (ALR) method, and firefly algorithm (FA) to solve the long-term production scheduling problem of open-pit mines with the assumption of deterministic and also considering the grade uncertainty. The firefly algorithm is used to update the Lagrange multipliers. The newly proposed approaches are based on optimizing Lagrangian multipliers and comparing them with the results of combined Lagrangian relaxation method and augmented Lagrangian relaxation with the Genetic Algorithm (GA), and the traditional sub-gradient (SG) method. For solving and validating the obtained model, Chadarmelo iron ore mine is considered as a suitable case study. The results of the case study show that the combined strategy (ALR-FA) can provide a near-optimal solution over other methods such that, over a given period, the net present value using the proposed hybrid approach is 20.11% higher than the traditional method is available. Also, the CPU speed of the proposed model is 4.7% more than the other methods.

Summary:

One of the main problems of mine planning is long-term production scheduling, which is very important in the theoretical research of open-pit mining and determines the distribution of cash flow throughout the life of the mine. In fact, the purpose is to maximize the net present value of the future profits generated. Among the uncertainties, grade uncertainty will play a major role in the accuracy of long-term production scheduling. In this paper, a hybrid model is presented by the Lagrangian relaxation method and bat algorithm to solve the problem of long-term production of open-pit mines, in which the uncertainty of the grade is also considered. The new approaches proposed are based on optimizing Lagrange coefficients and comparing them with the traditional method. The bat algorithm is used to update the Lagrange coefficients. The results of the case study show that the hybrid strategy can provide an acceptable solution compared to the traditional approximation method so that over a given period the net present value using the proposed hybrid method is 6.69% higher than the traditional one.

In recent years, a new approach to cheaper computational algorithms, such as meta-heuristic techniques, has attracted more attention from researchers to solve production scheduling problems. Although these techniques do not guarantee optimization as a final solution for production, they can provide suitable solutions for production at a lower computational cost.

Methodology and Approaches:

In this paper, an optimal hybrid model by the Lagrangian relaxation method and bat algorithm is presented to solve the problem of long-term production of open-pit mines, where the uncertainty of the grade is also considered. The newly proposed approach is based on optimizing Lagrange coefficients and comparing it with the traditional method. The results of the proposed approach are also compared with the combined approach based on the Lagrangian relaxation method and genetic algorithm. The bat algorithm is used to update the Lagrange coefficients.

The results of a case study show that the Lagrangian relaxation method can provide a suitable solution to the main problem and the combined strategy can produce a more effective solution than the traditional approximation method. It was also found that the proposed method has advantages, such as stable convergence property and prevention of early convergence. Over a given period, the net present value using the LR-BA hybrid method is 6.69% higher than the traditional method and also 5.58% higher than the LR-GA method.

Summary: Based on statistics, flyrock is the main reason of 20 to 40% of blasting induced accidents in mines. Therefore, the accurate prediction of flyrock has a remarkable role on reducing its detrimental effects. In this paper, a model tree was developed for flyrock prediction using M5P technique. This model was trained and tested by the blasting database of Sungun copper mine. The results showed that the proposed model can estimate the flyrock with an acceptable error. Flyrock is one of the most challenging safety issues of blasting operation in open pit mines, which always threatens the safety of personnel and equipment. Thus, the accurate prediction of flyrock seems necessary for determination of safe blasting zone. During past years, many empirical models have been developed using artificial intelligence techniques for flyrock prediction. Most of these models do not indicate the relationship between input and output parameters, so they are opaque and vague. The main purpose of this paper is to construct a transparent and understandable model for flyrock prediction. Methodology and Approache: In this paper, a model is developed for flyrock prediction using M5P technique. This technique presents a tree structure which contains linear equations in its leaves and using these equations the flyrock can be predicted easily. In this model, the flyrock distance is estimated using the most important controllable blasting parameters (i.e. burden, spacing, stemming, blasthole length, blasthole diameter, powder factor and mean charge per blasthole). Results and Conclusions: The accuracy and efficiency of proposed model was evaluated using various statistical indices such as coefficient and determination (R2), variance account for (VAF) and root mean square error (RMSE). These indices were obtained 92.1%, 92%, and 3.9, respectively. Therefore, it can be concluded that M5P technique is a suitable and efficient means that provides an acceptable prediction for flyrock. Furthermore, the results indicated that the burden and blasthole diameter are the most and the least effective parameters on flyrock prediction, respectively. The output of this model can be considered as a preliminary estimation of flyrock, based on which the risk of hazards and potential accidents can be reduced to a desirable extent.

Mining due to its specific characteristic is considered as a hazardous industry. Risk assessment is one of the most important project management procedures. The aim of this research is to present an approach for identification and ranking risks in the open pit mines. Ranking is a multi-attribute decision making. In order to identify the risks، risk breakdown structure method and ranking risks by Fuzzy TOPSIS method is implemented. First a comprehensive structure of risks in open pit mines in the frame work of 20 main levels and 117 sublevels is designed. Then different indices such as “probability”، “impact on project objectives (time، cost، quality and performance) ’’، “manageability”، “continually repeating”، “exposure”، “proximity”، “confidence level” for ranking of risk issues are determined. This approach is applied for risk assessment of Zarshuran Gold Mine as one of the biggest goldmines in Iran. In this mine، marketing، geotechnical، economic and political risks have been identified as the most significant risks، respectively.

Open pit mining is one of the most important methods of surface mining in which extraction of mineral deposit is carried out in benches. Ultimate limits of an open pit which define its size and shape at the end of the mine’s life must be designed before to start the operation. Manual and computer methods can be used to determine optimum ultimate pit limits. Manual methods of designing pit limits are based on stripping ratios and involve determining break even pit limits. The objective of computer methods is to determine the optimum ultimate pit outline for which the net profit is maximized. A number of algorithms such as floating or moving cone method, the Korobov algorithm and the corrected form of this method and the Lerchs and Grossmann algorithm based on graph theory have been developed to find out the optimum final pit limits. Each of these methods has special advantages and disadvantages. The designers of the corrected form of Korobov algorithm claim that this method is able to yield the true optimum pit in all the cases. The aim of this paper is to examine this method for being as a true optimum pit algorithm. This method is compared with the Lerchs-Grossmann algorithm which is the only method that can be proved, rigorously, always to yield the true optimum pit. The results show that the corrected form of Korobov algorithm is not always able to find out a true optimum pit outline.

A large number of algorithms have already been developed to optimize open pit limits based on some assumptions and constraints. Therefore، it is normal to develop new algorithms to decrease existing limitations and increase their capabilities. The key group concept، mainly used for stability slope analysis is، in this paper، used to develop a new algorithm named “KG-PLO” to optimize open pit limits. The KG-PLO is implemented on an especial type of the economic block model of the mining area. The algorithm can present the true optimal limit because it is rigorous and have a mathematical proof. Since the KG-PLO enjoys a sequential procedure، the logic iterations are not too much. The KG-PLO algorithm has been developed based on the existing 2D key group approach. It is possible to improve the proposed algorithm concerning the further improvement of the key group concept، proportionally. A computer program in Mathematica media is prepared to implement the KG-PLO algorithm.