فهرست مطالب مجید عطایی پور

The need to use minerals in recent years has led to the development of the volume of mining operations and, as a result, increased mining depth. With the increase in the depth of mines and as a result the number of geotechnical/geomechanical parameters/variables with uncertainty, underground mining activities can be associated with hazards and risks with catastrophic consequences. Choosing an appropriate and safe underground mining method, studying, and ranking them from the risk assessment viewpoint along with mine design from the early stages of mining can lead to the reduction of life and financial risks. It can also be effective in preventing accidents. Considering the importance of the subject, in this research, using the fuzzy WASPAS technique, we studied, ranked, and determined the importance of some of the most important underground mining methods from the perspective of risk assessment and taking into account the uncertainty and risk of geomechanical parameters. The research started with the design of a questionnaire and a survey of experts. Then the fuzzy WASPAS method was implemented. Finally, by performing relevant calculations, the final weights of underground mining methods were determined and they were ranked. The results of the research show that the methods of Room & Pillar, Sublevel Stoping, Cut & Fill, and Sublevel Caving have the highest weight, respectively. Therefore, compared to the other methods, the method of Room & Pillar has the highest and the method of Sublevel Caving has the lowest potential in creating risk and instability.

To design an open pit mine, geological operations must be conducted, followed by the preparation of a three-dimensional model and mineral block model. The ultimate pit limit can be determined through accurate methods and artificial intelligence techniques. The problem of determining the ultimate pit limit is considered to be NP-hard, making it challenging to solve. While exact methods provide better and optimal results, they may require significant time to answer the problem due to the large number of blocks involved. In such cases, it is more suitable to use collective algorithms or a planned approach to determine the final range. Optimizing the determination of the ultimate pit limit is similar to other optimization problems that can be addressed using logical algorithms in MATLAB software. In this study, Keshtel algorithm, implemented in MATLAB, is utilized to optimize the final range. Initially, Keshtel algorithm is employed to solve the problem. Subsequently, the Songun copper mine is chosen as a case study for the two-dimensional and three-dimensional implementation, and the results of determining the ultimate pit limit are compared with both Keshtel algorithm and NPV Scheduler software. The findings reveal that Keshtel algorithm, used to determine the final limits of the Songun copper mine, differs by only 0.47% compared to the NPV Scheduler software. Moreover, the comparison of Keshtel algorithm with the results of Lerch Grossman in determining the two-dimensional final range, as well as the comparison with NPV Scheduler software in three-dimensional problems, demonstrates its efficiency in solving these issues effectively.

In the occurrence of geomechanical risks in underground mines, not only one factor, but a set of factors are closely related to each other. Therefore, choosing a suitable underground mining method and studying the relationship and interaction between the geomechanical and geological factors affecting it, before starting the mining process, can maximize profit and recovery, increase productivity, reduce production costs and ore losses, and finally create a safe environment for underground miners. Therefore, according to the importance of the subject, in this research, using the fuzzy DEMATEL method, the structure governing the factors was analyzed from the point of view of their influence and how they relate to each other, and also the importance of them. To implement this technique, first questionnaires were designed and distributed among experts, then 18 questionnaires were received to evaluate the factors. Finally, by implementing this method, the vectors of R, C, R+C, and R-C were calculated for each factor. The results show that the factors of discontinuity spacing and geological structures of the deposit have the highest (cause factors), and the criteria of Rock Mass Rating (RMR) and rock mass deformation modulus have the lowest impact (effect factors), respectively. The causal diagram drawn for the geomechanical factors influencing the underground mining method selection and the occurrence of geomechanical risks in underground mines, also shows that the Rock Mass Rating (RMR) factor has the highest prominence value and therefore has the most importance among all other parameters.

Financing the mining projects is one of the issues against investors at different stages of the project. There are several options for financing mining projects and each of them has its properties. The rate of using these choices depends on the plan’s circumstance and investors’ Preferences. In the present study, after identifying the criteria affecting the NPV of mining companies, with the help of a questionnaire and using the Delphi technique, five criteria of refund mechanism, the volume of capital, interest rates, access costs, and risks, were selected as the main criteria for studying different financing alternatives (including stock exchange, sukuk, banks and financial and credit institutions, internal capital, and foreign bank). Since these criteria are not independent and the dependency between them affects the choice of financing method and as a result the goal of this research, the method of Analytical Network Process (ANP) was used and the relative weight of each criterion is obtained. In the next step, according to the mentioned criteria and determining the impact of each on the NPV of mining companies, the alternatives should be prioritized in such a way as to achieve the highest NPV. The findings of the research show that foreign banks with a final relative weight of 0.398 is the best option. After that, the use of internal capital with a relative weight of 0.217, sukuk with a final relative weight of 0.161, the stock exchange with a final relative weight of 0.138, and finally, internal banks with a final weight of 0.086 are the most appropriate methods, respectively.

The valuation of a mining project is always under uncertainty due to the very complex nature of the deposit. Therefore, in order to evaluate the project more accurately, sources of uncertainty should always be identified and analyzed to increase the net present value. One of the most important sources of uncertainty in a mining project is the price and operating costs that have a significant impact on the net present value during the implementation of that project. According to many researches related to the evaluation of mining projects under conditions of uncertainty, they showed that the use of the real options approach had a significant impact on increasing the net present value of mining projects. In this paper, using the real option approach, according to the case study that was carried out in the Sangan iron ore mine project, an option under the title of low-grade iron ore stockpile (grade less than 35%) and iron ore stockpile with oxide percentage less than FEO<10% defined. The purpose of defining this option is to check the right time to sell low-grade iron ore and iron ore with low oxide percentage, which as a result, according to the technique of real options, can lead to the profitability of the project and increase the net present value.

Due to its nature, mining operations are associated with many uncertainties. The effective factors in selecting the appropriate mining method in underground mines are also associated with uncertainties. The uncertainty associated with these parameters can cause various life-threatening/mortal and financial risks. Considering the risk and uncertainty related to these parameters, ranking and determining their importance, not only helps to choose the best (the safest and the most profitable) mining method before starting the mining process, but also to design a better and safer mine and reducing subsequent risks. Fuzzy parameters are generally estimated through expert knowledge, but the degree of confidence in the opinion of different experts is different and the uncertainty and difference in the reliability of their opinion cannot be ignored. In this research, Z-numbers Theory was used to solve the mentioned challenge. To conduct the present study, first the influencing factors in the selection of underground mining methods were studied and classified into 4 main groups of criteria, 13 sub-criteria 1 and 85 sub-criteria 2. Then, the Z-numbers theory was used to rank and determine their importance. After calculating the final weight of each parameter, in order to check the validity and accuracy of the findings, the results were compared with the parameters considered for choosing the underground mining method in Angouran mine. The results show that in each group of parameters, the more weighted factors (the results of the present research) correspond to the parameters related to choosing the mining method in Angouran mine.

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.

Methane has been known as a safety risk for the coal mining activities. Accordingly, one can mitigate this risk, and hence, the level of hazard to which the mining workers are exposed, by predicting the possible exceedance of allowable methane dosage should be provided with a reliable information on the distribution of methane across the working face considering the uncertainties associated with the gas content of such deposits. In this work, the gas content uncertainty in a coal seam is first investigated using the geo-statistical simulation. Then a method is proposed in order to predict methane gas emission based on the Monte Carlo random simulation method. Next, the results obtained are introduced into a 3D Computational Fluid Dynamics (CFD) model to estimate the methane distribution considering the uncertainty associated with the gas content. Defined as zones where the methane concentration is so high that an explosion is much likely to occur, the elevated methane zones (EMZs) are delineated across the working faces. The results obtained show that UGC has an impact on the ventilation parameters and EMZs. The proposed method could be carried out in order to guide the ventilation design in improving safety.

Production planning in underground mines is still a manual process and there are no suitable tools for optimal production planning for these mines. It is impossible to achieve a real optimum result through manual programming because of the complexity of underground mines and production planning problems in these mines. Due to the increasing consumption of minerals on one hand and the depth of mineral reserves, on the other hand, efforts to achieve comprehensive production planning optimization methods in underground mines and especially large-scale production methods with high production rates are inevitable. Among the underground mining methods, sublevel caving is a common method for hard rock mining, and there are very few studies of long-term production planning for this method. In this paper, a new mathematical model with the aim of maximizing the net present value (NPV) is presented to optimize the production planning of the sublevel caving method. The technical and operational constraints of the sublevel caving method such as development constraints, production capacity, sublevels geometry, and storage access are included in this model. The proposed model was implemented on an economic block model and the maximum NPV was determined. A comparison of the results obtained with the results of manual planning shows a significant increase in NPV mining operations.

Prediction of gas emission before mining is difficult since it depends on a number of geology, geographical, and operation factor. The gas content is among the most important parameters for the assessment of gas emission through the coal layer both during and after the mining operation. The most important objective followed by studying the gas content is to evaluate the volume of gas that will emit upon extracting the coal from different depths, as it comprises a fundamental basis for the calculation of the required air volume for ventilating the coal mine. The large amounts of gas released during mining present concerns about sufficient airflow for ventilation to ensure worker safety. Hence, the functions of mine ventilation system are vital for an underground mining system. In the present study, the central data from a total of 64 exploratory boreholes was utilized. Once finished with identifying the most important coal layers in terms of gas emission, the variogram modeling as performed to define the distribution of the gas content across the coal layer. Subsequently, simulations were performed to stochastically assess the gas content. So, an approach is provided for the prediction of gas emission based on a random simulation method, Monte-Carlo Simulation (MCS). For this purpose, six factors are selected for predicting methane emissions including main layer gas content, main layer thickness, advanced rate, production rate, adjacent layer gas content, adjacent layer thickness and main layer distance from adjacent layers. The result shows that the prediction model has enough prediction accuracy for application of actual engineering in the coal mine. The predicted value is also essentially consistent with the actual value and the prediction method based on the uncertainty theory is reliable.

The uncertainty-based mine evaluation and optimization have been regarded as a critical issue. However, it has received less attention in the underground mines than in the open-pit mines due to the diversity of the underground mining methods, and the underground mining parameters' complexity. The grade and commodity price uncertainties play essential roles in mining projects. Mine planning by not incorporating these uncertainties is accompanied by risks. The evaluation and risk assessment of the mine plans is possible through evaluating the mineable reserve in the presence of such uncertainties. In the present work, we evaluate the effects of grade and commodity price uncertainties on the underground mining stope optimization and the resultant mineable reserve. In this regard, the stope boundary is studied both deterministically and stochastically in the presence of the grade and price uncertainties. For this purpose, in this work, we implement the conditional simulation in order to generate equally probable ore reserve models. Furthermore, we optimize the stope boundary using the floating-stope algorithm in each realization. Several decision support criteria including the 'mineable reserve,' 'metal-content,' 'profit,' and 'value-at-risk' are defined to assist the decision-maker in uncertain conditions. Finally, a procedure is defined in order to consider two types of uncertainty sources simultaneously in underground mining. It will guide the decision-maker toward the most appropriate stope boundary that best fits the mining company's requirements. The procedure is implemented in a bauxite mine, and the optimal stope boundary is determined concerning the different criteria.

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.

Underground mine ventilation networks are affected by various parameters which have not been seen at the planning phase. The efficiency of ventilation network is related to this parameter. In this research, resistance variation is defined as one of the parameters involved in ventilation performance. Uncertainty of resistance variation in ventilation networks during mine life was modeled by sensitivity analysis method. For this goal, limited differential gradient of flow alteration against variation of resistance was calculated for each branch. The proposed method was implemented for the TAKHT mine ventilation network. Changes of resistance at main tunnel responsible for the transportation of persons and extracted materials were considered as inputs of the sensitivity analysis model. The maximum amounts of limited differential gradient are related to the most sensitive branches against resistant variation in the main tunnel. They are an air outflow tunnel and a ventilation duct. High-sensitivity branches have a higher risk for failure in the ventilation system, so the sensitivity of each branch can be considered as a factor in the reliability analysis model.

The main production operations in most mines are drilling, blasting, loading, and hauling. Therefore, the optimization of each part causes an increase in profits and a decrease in mining costs. About 50 to 60 percent of mining costs are related to machinery, and the major share of them belong to loading and hauling machine. So, the correct utilization of the haulage fleet is very important step to increase productivity and to reduce production costs of the systems. In this study, by using simulation method and utilization of Arena software, the operation of the current production system of Abadeh Fireclay mine have been studied. Results showed that the productivity of loading machines in mine is low and due to the shortage of trucks the unit production costs of haulage system be increased.  By using the high-capacity machines in mining operations, mine production will be increased and also production costs will be reduced significantly. Moreover, when flexible dispatching approach is used with proportionate loading and hauling machines, production will incr ease by 4 to 8 percent with a given number of trucks and also production costs will decrease by 5 to 7 percent.

Job creation is one of the important social impacts of mining, which is considered in many aspects. The number of new jobs created, the duration of employment, the acquisition of skills for employment in other mines and the duration of work in inappropriate conditions (sometimes underground mines) can be attributed to the job creation opportunities created by mining in the region. In addition to the above mentioned, mining has positive social impacts such as creating welfare facilities and improving the livelihoods of the people of the region and negative impacts, such as the creation of social anomalies and the collapse of gender balance. So far, much research has been done on the economic and environmental impacts of mining, and the third criterion of sustainable development, which is a community, has been neglected. In this paper, the "choice experiment" method, a new method of "stated preferences" approach, has been used to estimate the value of social impacts of mining. In this study, the social impacts of mining on two parts of employment and other social impacts of mining have been investigated. For this purpose, a questionnaire was prepared and the results of 780 responses for each section after encoding were entered into the STATA software and its appropriate pattern and respondents preferences according to the coefficients and pattern results. Based on the results of estimating conditional logit in both section, Lagrange's ratio is statistically significant which shows a significant regression for the model. Also, according to the results of this research, it can be said that for respondents, the number of jobs and desirability of the livelihood is the highest value and they are ready to work longer in inappropriate conditions, provided that these conditions do not lead to social anomalies in their place of residence.

Estimating the costs of blasting operations is an important parameter in open-pit mining. Blasting and rock fragmentation depend on two groups of variables. The first group consists of mass properties, which are uncontrollable, and the second one is the drill-and-blast design parameters, which can be controlled and optimized. The design parameters include burden, spacing, hole length, hole diameter, sub-drilling, charge weight, charge length, stemming length, and charge density. Blasting costs vary depending on the size of these parameters. Moreover, blasting brings about some undesirable results such as air overpressure, fly rock, back-break, and ground vibration. This paper proposes a mathematical model for estimating the costs of blasting operations in the Baghak gypsum mine. The cost of blasting operations in the objective function is divided into three parts: drilling costs, costs of blasting system, and costs of blasting labours. The decision variables used to minimize the costs include burden, spacing, hole diameter, stemming length, charge density, and charge weight. Constraints of the model include the boundary and operational limitations. Air overpressure in the mine is also anticipated as one of the model constraints. The non-linear model obtained with consideration of constraints is optimized by simulated annealing (SA). After optimizing the model by SA, the best values for the decision variables are determined. The value obtained for the cost was obtained to be equal to 2259 $ per 7700 tons for the desired block, which is less than the blasting costs in the Baghak gypsum mine.

Appropriate ventilation in underground mines is one of the most important factors in deep mining. Mining engineers always pay attention to the design of the ventilation network during development and extraction of ore in underground mines. The ventilation network’s efficiency depends on a variety of factors. The presence of barriers in mine ventilation networks is one of the important affecting factors. In this research, using the Monte Carlo technique, a probability distribution function is presented for the resistance of obstacles as the input to the model. For each simulated value, the ventilation network was analyzed and the air flow of all branches of the ventilation system of the NAKHLAK mine was determined. Reliability criteria were introduced. Results showed that branches 4, 6.7, and 13 have been failed. Branch 7 will be able to find its initial performance by changing the flow direction. Branch 13 was completely failed during the simulation process.

In the past, various methods have been proposed to determine the ultimate pit limit. Among the methods, can be pointed out artificial intelligence-based methods such heuristic algorithms, genetic, ants and colony competition. Artificial bee colony (ABC) algorithm is one of the most powerful heuristic algorithms inspired by the cumulative life of bees. In this paper, first, a hypothetical example of the life of the bees was described to find the source with the highest number of nectar by this algorithm. Then, a method based on the bee algorithm is proposed to determine the ultimate pit limit, and to examine its performance, a two-dimensional example is described step by step. In this example, it was found where in cases, the moving cone can not find the optimal solution for determining the ultimate pit limit, the ABC is well suited for its solution.Then, this algorithm was used to determine the ultimate limit of Sungun mine with a number of 120*100*45 blocks. To validate the problem solving of determining the ultimate limit using the ABC algorithm, graph theory and moving cones was used. The results show that the difference in income from the determination of the ultimate pit limit using the ABC algorithm from the moving cone and the graph theory is 1.6% and 12.3%, respectively.

Few algorithms are available for economic optimization of underground mining area . This is mainly because there are a variety of underground mining methods with various restrictions and conditions and the underground mining parameters are more complex . Therefore, it does not allow the development of general optimization tools, especially, software tools or computer programs. Based on the Global Optimisation for Underground Mining Area (GOUMA) algorithm, a computer program , GOUMA-CP, was developed to implement the mentioned algorithm . GOUMA enjoys a new system of modelling named "Variable Value Economic Model " (VVEM) and benefits from the mathematical proof and provide the true optimal solution according to the conditions and environment it is applied . This paper includes a description of the GOUMA-CP program in details and the corresponding algorithm briefly . This computer program has successfully been applied to optimise underground mining area of a gold mine in Australia.

Optimization of underground stope is defined as looking for a boundary with the highest profit considering technological and geometrical constraints. The first algorithms were presented about five decades ago for optimization of mining limits. Most of algorithms, presented till now, have been applied for optimization of open pit limits. Few algorithms have been presented for Underground cases, most of which have been tailored for a specific mining method or lack rigorous techniques. There are, however, some rigorous algorithms but they fail to provide 3D solution,. In this paper, a new algorithm is introduced to optimize stope layout using integer programming (IP) technique. The algorithm is implemented on a specific model, derived from conventional block model. It is a rigorous algorithm and provides a realistic optimum solution, compared to its alternatives. For the first time, two more constraints, namely the minimum width of rib pillars and the maximum stope height, are considered in the proposed algorithm.