r. mikaeil

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.

The noise of drilling in the dimension stone business is unbearable for both the workplace and the people who work there. In order to reduce the negative effects drilling has on the health of the environment, the drilling noise has to be measured, assessed, and controlled. The main purpose of this work is to investigate an experimental-intelligent method to predict the noise value of drilling in the dimension stone industry. For this purpose,135 laboratory tests are designed on five types of rocks (four types of hard rock and one type of soft rock), and their results are measured in the first step. In the second step, due to the unpredicted and uncertain issues in this case, artificial intelligence (AI) approaches are applied, and the modeling is conducted using three intelligent systems (IS), namely an adaptive neuro-fuzzy inference system-SCM (ANFIS-SCM), an adaptive neuro-fuzzy inference system-FCM (ANFIS-FCM), and the radial basis function network (RBF) neural network. 75% of the samples are considered for training, and the rest for testing. Several models are constructed, and the results indicate that although there is no significant difference between the models according to the performance indices, the proposed construction of ANFIS-SCM can be considered as an efficient tool in the evaluation of drilling noise. Finally, several scenarios are designed with different input modes, and the results obtained prove that the types of rock and the drill bits are more important than the operational characteristics of the machine.

Predicting the amperage consumption of cutting machines could be one of the critical steps in optimizing the energy-consuming points for the dimension stone cutting industry. Hence, the study of the relationship between the operational characteristics of cutting machines and rocks with focusing on the machine's energy-consuming is unavoidable. For this purpose, in the first step, laboratory studies under different operating conditions at different cutting depths and feed rates are performed on 12 soft and hard rock samples. In the continuation of the laboratory studies, the rock samples are transferred to the rock mechanics laboratory in order to determine the mechanical properties (uniaxial compressive strength and modulus of elasticity). The statistical studies are performed in the SPSS software in order to predict the electrical current consumption of the cutting machine according to the mechanical characteristics of the rock samples, cutting depth, and feed rate. The statistical models proposed in this work can be used with a high reliability in order to estimate the electrical current consumed in the cutting process.

In most rock drilling operations, the low rate of penetration (ROP) can be primarily attributed to the presence of the cuttings produced during drilling and the thermal stresses caused by friction at the bit-rock interface, which can be exacerbated with the increasing strength, hardness, and abrasivity of the drilled rock. In order to improve ROP, drill bit lifetime, and cutting power, it is necessary to minimize the process forces due to the mechanical bit-rock interaction and the thermal stresses generated in the drill hole. Any improvement in these areas is extremely important from both the technical and the economic perspectives. This improvement can be achieved by the use of appropriate cooling/lubricating fluids in the drilling process in order to increase ROP, reduce the temperature of the drilling environment, and create a clean drill hole free of cuttings. In this work, a series of laboratory drilling tests are performed to investigate and compare ROP in the drilling of seven samples of hard and soft rock in the presence of six different cooling-lubricating fluids. The drilling tests are performed on the cubic specimens with a laboratory-scale drilling rig at several different rotation speeds and thrust forces. The statistical analyses are performed in order to investigate the relationship between ROP and the mechanical properties of the rock, properties of the fluid, and machining parameters of the drilling rig. These analyses show that under similar conditions in terms of mechanical properties of the rock using Syncool with a concentration of 1:100 and soap water with a concentration of 1:120 instead of pure water leads to the average 31% and 37% increased ROP in granite, 36% and 43% increased ROP in marble, and 47% and 61% increased ROP in travertine, respectively. These results demonstrate the good performance of these cooling/lubricating fluids in increasing ROP.

The blasting method is one of the most important operations in most open-pit mines that has a priority over the other mechanical excavation methods due to its cost-effectiveness and flexibility in operation. However, the blasting operation, especially in surface mines, imposes some environmental problems including the ground vibration as one of the most important ones. In this work, an evaluation system is provided to study and select the best blasting pattern in order to reduce the ground vibration as one of the hazards in using the blasting method. In this work, 45 blasting patterns used for the Sungun copper mine are studied and evaluated to help determine the most suitable and optimum blasting pattern for reducing the ground vibration. Additionally, due to the lack of certainty in the nature of ground and the analyses relating to this drilling system, in the first step, a combination of the imperialist competitive algorithm and k-means algorithm is used for clustering the measured data. In the second step, one of the multi-criteria decision-making methods, namely TOPSIS (Technique for Order Performance by Similarity to Ideal Solution), is used for the final ranking. Finally, after evaluating and ranking the studied patterns, the blasting pattern No. 27 is selected. This pattern is used with the properties including a hole diameter of 16.5 cm, number of holes of 13, spacing of 4 m, burden of 3 m, and ammonium nitrate fuel oil of 1100 Kg as the most appropriate blasting pattern leading to the minimum ground vibration and reduction of damages to the environment and structures constructed around the mine.

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.

Predicting the ampere consumption in carbonate rock sawing process is very important for the determination of the electrical energy cost per unit of production. In addition, ampere consumption prediction may be used for selecting the optimum operation parameters to obtain high production rate. In this study, it is aimed to develop fuzzy classification systems to evaluate and classify the carbonate rock based on physical and mechanical properties such as Brazilian tensile strength, equal quartz content, grain size, uniaxial compressive strength, Young modulus and Mohs hardness. Varieties of seven carbonate rocks such as Azarshahr travertine, Hajiabad travertine, Dare-bukhari travertine, Harsin marble, Salsali marble, Anarak marble and Haftooman marble were classified by developed fuzzy classification system. To validate the classification’s results, ampere consumption was recorded during sawing process for each studied rocks. The results of the study show that the three-class fuzzy classification system is capable to evaluate the carbonate rock saw-ability and ampere consumption during soft dimensional stone sawing process.

Since، in geomechanical evaluation of rock masses، many parameters affect the rock mass behavior simultaneously، rock engineering classifications are suitable approaches for studying and prediction of rock mass behaviors. Classic classifications in rock engineering have suffer from some practical limitations، particularly. These limitations are more obvious in when classification is made near the boundaries. y conditions. In this study in order to increase the abilities and expending the applications of rock mass drillability index (RDi)، fuzzy theory has been used. For this purpose، six parameters of the rock mass which are used in RDi classification، including uniaxial compressive strength (UCS)، joints dipping، Mohs hardness، joints aperture، joints spacing and grain size have been used. Then، some fuzzy functions have been defined on these parameters and finally the class of each rock mass has been identified. In order to compare the results of classic classification with results of fuzzy classification، a case study was done on rock masses of limestone mine of Shahrood cement factory. The results show that the fuzzy logic based classification produces clearer better results than classic system especially in rock masses with boundary condition. During this research، a software was prepared for doing theto calculate the RDi scoresions and classifying the rock mass. Theis program has been written by coded in Visual C++ and has a contains a graphical main window thatinterface with all input parameters which are related (rock mass characteristics) and also the out output parameters of the program (which are RDi score and class of rock mass). are illustrated in it.