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Facility site selection, (such as Primary or in pit Crusher location) is one of the most important steps in mine design process that has a significant impact on economy of the mining operation. This study covers an attempt to find the optimal location for primary crusher in order to support the expansion of Sungun copper mine, which is the second largest open pit mine in Iran. Recent exploration drilling has led to a considerable increase in the ore reserve. Therefore, Sungun started the studies followed with an expansion plan with the goal to increase the capacity from 14 to 28 Mt/Year within the next 8 years. In this paper, 14 effective parameters for finding the best location for a new crusher in five possible options were considered. The options were evaluated based on weighted linear combination method. The results show that the location in South East part of the open pit is the best option for locating of the new crusher. Finally, based on the economic parameters and copper price forecasts, pit optimization and sensitive analysis are applied. Results show that in the worst case, just 0.17 % of ore reserve is affected by the selected area and the minable reserve can cover 78 years of operation.

The shape of a blasted rock mass, or simply muckpile, affects the efficiency of loading machines. A muckpile is defined with two main parameters known as throw and drop, while several blasting parameters will influence the muckpile shape. This paper studies the prediction of the muckpile shape in open-pit mines by applying an artificial neural network designed by a genetic algorithm. In that regard, a genetic algorithm has been used in preparing the neural network architecture and parameters. Moreover, input variables have been reduced using the principal component analysis. Finally, the best models for predicting throw and drop determined. Analyzing the performance of the proposed models indicates their superiority in predicting the muckpile shape. As a result, the Mean Squared Error of the throw was 0.53 for the training data and 1.24 for the testing data. While for the drop, the errors were 0.45 and 0.58 for the training and testing data, respectively. Furthermore, the sensitivity analysis shows that specific-charge effects drop and throw more. [ABSTRACT FROM AUTHOR]

Long-term production planning in open-pit mines is a precedence-constraint knapsack problem. A spatial representation of the mining region (called the block-model) is the primary input of mine planning models. One should note that as the number of blocks and periods to be planned increases, the number of decision variables increases. This paper presents a fast yet straightforward algorithm to reduce binary variables in open-pit mine production planning models. The algorithm considers mining capacity, processing capacity, and pit deepening rate to estimate the time span within which a block is mineable. This paper applies the algorithm in 12 different cases. The number of blocks varies from 1000 to 240000, and the mining periods range from 6 to 30 years. According to the results, this algorithm is helpful for problem size reduction. [ABSTRACT FROM AUTHOR]

Despite the upward trend in using semi-mobile in-pit crushing and conveying system (SMIPCC), some mining companies still hesitate to use SMIPCC in their operations. Therefore, a framework is needed to facilitate decision-making between SMIPCC and truck and shovel system (TS) and satisfy the location and relocation time of the semi-mobile crusher (SMC). For this purpose, this paper presents a mathematical integer programming (IP) model considering the operational constraints. The model specifies both the optimum location and optimum relocation time of SMC. Also, it compares the production schedule for the TS and SMIPCC utilised for ore and waste handling over the life of mine. Then, the system with maximum net present value (NPV) is selected as the optimal material handling system. Finally, the model is implemented with a case study. The results show that implementing SMIPCC instead of the TS system for ore and waste materials improves the NPV by 69.77%. [ABSTRACT FROM AUTHOR]

Cost efficiency and high reliability of In-Pit Crushing and Conveying (IPCC) system make it more appealing to be used in deeper open-pit mining activities. Determination of the optimum time and location (OT–OL) for applying the IPCC system were always a challenge. Application of mathematical programming approaches suffers from reduced computational efficiency due to a lot of decision variables. The situation will be worse; by adding the IPCC’s time and location variables to these models. Also, the problem involves a lot of variables that each one of them is a function of others. To tackle these, this paper provides a new heuristic approach to find a good quality solution hierarchically (here in two levels). The problem was broken into two parts (i.e. OT and OL). The model works in a discrete time horizon. It has been verified by the data gathered from Sungun copper mine of Iran. Our computational results show that the system is efficient for developed countries with a stable and low interest rate. [ABSTRACT FROM AUTHOR]

Short-term production plans are the basis for operational mine production schedules. They concentrate on making long-term mine plans operationally feasible. It ensures a steady flow of product for meeting blending targets. Due to the quality variation of material, blend optimization is an uncertainty based optimization problem. There are different approaches toward uncertainty management and the current paper investigates a portfolio optimization model in order to minimize the risks in short-term plans. In this paper, a fuzzy linear programming model is formulated to provide a set of options for the mine plan. These blending options are treated as portfolios. Then a model for the optimal selection of a portfolio is introduced. The objective of the model is to maximize the expected return of the portfolio under constraints limiting its variance. The model is applied in a limestone mine complex. [ABSTRACT FROM AUTHOR]