Akbar Esmaeilzadeh; Sina Shaffiee Haghshenas; Reza Mikaeil; Giuseppe Guido; Roohollah Shirani Faradonbeh; Roozbeh Abbasi Azghan; Amir Jafarpour; Shadi Taghizadeh
Abstract
Iran is one of the countries with the largest number of quarry mines in the world. Diamond cutting wire is usually used in quarries to cut dimension stone cubes, which is accompanied by hazardous events. Therefore, detecting and investigating the possible quarry risks is crucial to have a safe and sustainable ...
Read More
Iran is one of the countries with the largest number of quarry mines in the world. Diamond cutting wire is usually used in quarries to cut dimension stone cubes, which is accompanied by hazardous events. Therefore, detecting and investigating the possible quarry risks is crucial to have a safe and sustainable mining operation. In mine exploitation, maintaining the safety of vehicles and increasing the knowledge of personnel regarding safety issues can considerably mitigate the number or radius of effect of hazards. Hence, the incidents and risks in the West-Azerbaijan quarries in Iran are investigated in this work. To do so, a list of the hazards and their descriptions are first prepared. Then the hazard risk rating is conducted using the Failure Modes and Effects Analysis (FMEA) method. The number of priorities is calculated for each incident based on probability, intensity, and risk detection probability. Finally, the main causes of risks in the studies quarries are identified. The results obtained show that the most likely dangers in dimensional stone mines in West Azerbaijan are diamond cutting wire breaking, rock-fall, and car accidents, with the priority numbers of 216, 180, and 135, respectively. These hazards can be mitigated by applying some preservative activities such as timely cutting wire replacement, utilizing an intelligent system for cutting tool control, necessary personal training, and considering some preservative points.
J. Ziaei; S. Ghadernejad; A. Jafarpour; R. Mikaeil
Abstract
One of the most crucial factors involved in the optimum design and cost estimation of rock sawing process is the rock abrasivity that could result in a significant cost increase. Various methods including direct and indirect tests have been introduced in order to measure rock abrasivity. The Schimazek’s ...
Read More
One of the most crucial factors involved in the optimum design and cost estimation of rock sawing process is the rock abrasivity that could result in a significant cost increase. Various methods including direct and indirect tests have been introduced in order to measure rock abrasivity. The Schimazek’s F-abrasiveness factor ( ) is one of the most common indices to assess rock abrasivity. is the function of three rock parameters including the Brazilian tensile strength ( ), median grain size ( ), and equivalent quartz content ( ). By considering its formulation, it has been revealed that the coefficient of each parameter is equal, which is not correct because each parameter plays a different role in the rock abrasion process. This work aims to modify the original form of by introducing three correction factors. To calculate these correction factors, an integrated method based on a combination of the statistical analysis and probabilistic simulation is applied to a dataset of 15 different andesite rocks. Based on the results obtained, the values of -0.36, 0.3, and -0.89 are suggested as the correction factors of , and , respectively. The performance of the modified Schimazek’s F-abrasiveness factor ( ) is checked not only by the wear rate of diamond wire but also by the cutting rate of the wire sawing process of Andesite rocks. The results obtained indicate that the wear rate and cutting rate of andesite rocks can be reliably predicted using . However, it should be noted that this work is a preliminary one on the limited rock types and further studies are required by incorporating different rock types.
J. Abdollahei Sharif; A. Jafarpour; S. Yousefi
Abstract
The computer-based 3D modeling of ore bodies is one of the most important steps in the resource estimation, grade determination, and production scheduling of open-pit mines. In the modeling phase, the volume of the orebody model is required to be filled by the blocks and sub-blocks. The determination ...
Read More
The computer-based 3D modeling of ore bodies is one of the most important steps in the resource estimation, grade determination, and production scheduling of open-pit mines. In the modeling phase, the volume of the orebody model is required to be filled by the blocks and sub-blocks. The determination of Block Size (BS) is important due to the dependence of the geostatistical issues and calculations related to mining capabilities on it. There are some factors effective in the determination of an optimal BS including the metal content, estimation error, recovery percentage, mining ability, safety, and dilution. In this work, an optimal BS is determined using a two-stage approach. In the proposed approach, the Fuzzy Delphi Analytic Hierarchy Process (FDAHP) and Fuzzy Multi-Objective Optimization by Ratio Analysis (FMOORA) methods are used. In the first phase, the weight of each criterion is calculated based on the opinions of the experts using the FDAHP method. In the second phase, the FMOORA method is applied in order to determine a suitable BS for the design and operation of mining considering the extracted weights in the previous phase. The block model of the Sungun copper mine is studied as a case study to evaluate the capability of the proposed approach. The results of implementation of this approach are desirable because of converting the opinions of the experts to fuzzy values, weighing the experts according to the experience and technical knowledge, weighting the criteria by FDAHP, and choosing the optimal option with FMOORA. Furthermore, the 12.5×12.5×12.5 m3 block (A5) is chosen as an appropriate BS, which is compatible with the real conditions of the studied mine.
A. Aryafar; R. Mikaeil; F. Doulati Ardejani; S. Shaffiee Haghshenas; A. Jafarpour
Abstract
The process of pollutant adsorption from industrial wastewaters is a multivariate problem. This process is affected by many factors including the contact time (T), pH, adsorbent weight (m), and solution concentration (ppm). The main target of this work is to model and evaluate the process of pollutant ...
Read More
The process of pollutant adsorption from industrial wastewaters is a multivariate problem. This process is affected by many factors including the contact time (T), pH, adsorbent weight (m), and solution concentration (ppm). The main target of this work is to model and evaluate the process of pollutant adsorption from industrial wastewaters using the non-linear multivariate regression and intelligent computation techniques. In order to achieve this goal, 54 industrial wastewater samples gathered by Institute of Color Science & Technology of Iran (ICSTI) were studied. Based on the laboratory conditions, the data was divided into 4 groups (A-1, A-2, A-3, and A-4). For each group, a non-linear regression model was made. The statistical results obtained showed that two developed equations from the A-3 and A-4 groups were the best models with R2 being 0.84 and 0.74. In these models, the contact time and solution concentration were the main effective factors influencing the adsorption process. The extracted models were validated using the t-test and F-value test. The hybrid PSO-based ANN model (particle swarm optimization and artificial neural network algorithms) was constructed for modelling the pollutant adsorption process under different laboratory conditions. Based on this hybrid modeling, the performance indices were estimated. The hybrid model results showed that the best value belonged to the data group A-4 with R2 of 0.91. Both the non-linear regression and hybrid PSO-ANN models were found to be helpful tools for modeling the process of pollutant adsorption from industrial wastewaters.
Exploitation
E. Bakhtavar; A. Jafarpour; S. Yousefi
Abstract
In order to catch up with reality, all the macro-decisions related to long-term mining production planning must be made simultaneously and under uncertain conditions of determinant parameters. By taking advantage of the chance-constrained programming, this paper presents a stochastic model to create ...
Read More
In order to catch up with reality, all the macro-decisions related to long-term mining production planning must be made simultaneously and under uncertain conditions of determinant parameters. By taking advantage of the chance-constrained programming, this paper presents a stochastic model to create an optimal strategy for producing bimetallic deposit open-pit mines under certain and uncertain conditions. The uncertainties of grade, price per product, and capacities of the various stages in the process of production of the final product were considered. The results of solving the deterministic and stochastic models showed that the stochastic model had a greater compatibility and performance than the other ones.
