فهرست مطالب امیرحسین بانگیان تبریزی

The land that was influenced by mining operations especially waste dump, was increased continuously. Therefore the necessity of mine reclamation is more important than the past. Plant species selection is one of the main topics in open pit mine reclamation regarding mined land use and environmental protection.

In this research, the selection of suitable plant species regarding the environmental rehabilitation of Songun copper mine was performed. At first, suitable plant species were selected by main criteria such as post-mining land use, climatic condition, and ecological parameters of soil. Then selected species were ranked based on a completed questionnaire with experts by the combination of Analytic hierarchy process (AHP), Simple Multi-Attribute Rating Technique (SMARTER), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods.

Suitable tree species in acidic and metallic pollutant land are Ulmus minor, Juniperus communis, Betula pendula, Robiniapseudoacacia, Acer compestre, and Rhus coriria, respectively. Native tree species such as Ficus carica, Fraxinus excelsior, and Juglans regia were selected for the alkaline region, respectively.

From the selected tree species for this region, the more successful species in reducing pollution and preventing soil erosion are preferred. Robiniapseudoacacia is not considered one of the native trees of the region. Still, because it is more successful than other trees in environmental protection, it was selected as the superior plant species for this region. Among the native species of the region, Acer campestre takes priority due to the purpose of preserving the environment.

One of the problems with NATM tunneling in urban areas is the risk of excessive surface settlement during excavation operations. For real analysis and detailed study of surface settlement, it is necessary to pay attention to the real soil conditions. However, the conventional methods are always deterministic, rather than taking the natural spatial variability of soil properties into account. Therefore, in this study, an attempt has been made to model the real soil conditions by spatial variability of the soil young modulus based on a three-dimensional random field. By combining finite difference analysis with random field theory, a preliminary investigation has been performed into the surface settlement with spatially random Young modules. For this purpose, a combination of finite difference numerical method, random field, and Monte Carlo simulation is used which is known as the random finite difference method (RFDM). The procedure used is re-implemented by the authors in a MATLAB environment to combine it with The FLAC3D program and a series of parametric analyses were conducted to study the effects of uncertainty due to the variability of soil Young’s modulus on ground movements.

Excessive surface settlement is one of the major problems we encounter when constructing shallow tunnels in soft grounds. For the analytical study of surface settlement, it is necessary to consider soil properties in design calculations with high accuracy. In this research, the complex RFDM method is used to express the spatial variability of soil properties so that we can show its effects on surface settlement. The results demonstrate that soil variability exerts an influence both on the magnitude and distribution of surface settlement. In addition, it is concluded that negligence of the spatial variability of soil properties in surface settlement probability analysis can lead to underestimation of tunnel design parameters.

To create a random field, the values of SOF are determined first. Then, a three-dimensional random field is created by the random field generation functions. The random field created is assigned to the finite difference mesh by the embedded FISH language in FlAC3D.Finally, 1000 Monte Carlo simulations are performed and 1000 surface settlement curves for each SOF are generated. 

The mean values ​​of the Smax in numerical stochastic analysis when the SOF is 60 m is approximately equal to the obtained Smax from the numerical model because with increasing SOF the spatial correlation of the Young modulus parameter increases and is closer to the soil characteristics of the tunnel. In addition, the COV of the Smax tends to be 0.3 with increasing the SOF, but in general, it increases significantly (from 0.01 to 0.3), which causes changes in the magnitude of the Smax (between 5 and 80). Mm) becomes.The spatial variability of the Young modules causes the change in the magnitude of the surface settlement as well as a change in its location, so three-dimensional numerical analyzes can accurately display the maximum displacement of the Smax in both a vertical and longitudinal section of the tunnel.

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.

It is significant to discover a global optimization in the problems dealing with large dimensional scales to increase the quality of decision-making in the mining operation. It has been broadly confirmed that the long-term production scheduling (LTPS) problem performs a main role in mining projects to develop the performance regarding the obtainability of constraints, while maximizing the whole profits of the project in a specific period. There is a requirement for improving the scheduling methodologies to get a good solution since the production scheduling problems are non-deterministic polynomial-time hard. The current paper introduces the hybrid models so as to solve the LTPS problem under the condition of grade uncertainty with the contribution of Lagrangian relaxation (LR), particle swarm optimization (PSO), firefly algorithm (FA), and bat algorithm (BA). In fact, the LTPS problem is solved under the condition of grade uncertainty. It is proposed to use the LR technique on the LTPS problem and develop its performance, speeding up the convergence. Furthermore, PSO, FA, and BA are projected to bring up-to-date the Lagrangian multipliers. The consequences of the case study specifies that the LR method is more influential than the traditional linearization method to clarify the large-scale problem and make an acceptable solution. The results obtained point out that a better presentation is gained by LR–FA in comparison with LR-PSO, LR-BA, LR-Genetic Algorithm (GA), and traditional methods in terms of the summation net present value. Moreover, the CPU time by the LR-FA method is approximately 16.2% upper than the other methods.

In this research work, a 3D numerical modeling technique is proposed based on the 3D particle flow code in order to investigate the failure mechanism of rock foundations. Two series of footings with different geometries and areas are considered in this work. The failure mechanism obtained is similar to that of the Terzaghi’s but there is a negligible difference in between. Lastly, one equation is presented to calculate the bearing capacity based on the results achieved from the numerical model and the Mohr-Coulomb theory. The sensitivity analyses are performed on the friction angle, cohesion, and footing width. The results obtained are compared with the corresponding results given by the equations given by Terzaghi and Meyerhof. This comparison demonstrates a good agreement between them. In the friction angle sensitive analysis, the amounts of the bearing capacity diagram are very close to Meyerhof’s, which overlap with each other.

In this paper, we present an integrated model to find the optimum size of blast block that uses (i) a multi-criteria decision-making method to specify the applicable size of the mineable block; (ii) a linear programming method for the selection of the blasted areas to be excavated and in deciding the quantity of ores and wastes to be mined from each one of the selected blocks. These two methods use improved estimates of the orebody characteristics utilizing the blast hole data in addition to the usual borehole statistics to improve the prediction accuracy of the block level ore body characteristics. This work aims to make a mathematical model to figure out the ideal width and length of the blast block in order to curtail drilling and blasting expenses in open-pit mines. As a consequence, the effective blast block size is heeded so as to decrease the expenses of drilling and blasting. Furthermore, a complete set of actual principles is presented to specify the applicable size of the mineable block by means of the multi-criteria decision-making method of fuzzy logic. The aforementioned model is practiced to forecast the block size necessary for the purpose of production planning. Next, a mixed integer programming model is developed to blast planning in order to select the optimal size of the blast block by considering the mineable block. The proposed model is applied in the Chadormalu iron ore mine and the rationality of the model is demonstrated by the outcomes of dissimilar circumstances.