Presenting an engineering classification system for coal spontaneous combustion potential

The phenomenon of coal spontaneous combustion is one of the common hazards in coal mines and also one of the important reasons for the loss of coal in piles and mines. Based on previous researches, different types of coals have different spontaneous combustion characteristics. For coal loss prevention, a measure is necessary for prediction of coal spontaneous combustion. In this study, a new engineering classification system called ＂Coal Spontaneous Combustion Potential Index （CSCPI）＂ is presented based on the Fuzzy Delphi Analytic Hierarchy Process （FDAHP） approach. CSCPI classifies coals based on their spontaneous combustion capability. After recognition of the roles of the effective parameters influencing the initiation of a spontaneous combustion, a series of intrinsic, geological, and mining characteristics of coal seams are investigated. Then, the main stages of the implementation of the FDAHP method are studied and the weight of each parameter involved is calculated. A classification list of each parameter is formed, the CSCPI system is described, and the engineering classifying system is subsequently presented. In the CSCPI system, each coal seam can be rated by a number from 0 to 100; a higher number implies a greater ease for the coal spontaneous combustion capability. Based on the CSCPI system, the propensity of spontaneous combustion of coal can be classified into three potential levels： low, medium, and high. Finally, using the events of coal spontaneous combustion occurring in one of the Iranian coal mines, Eastern Alborz Coal Mines, an initial validation of the mentioned systematic approach is conducted. Comparison of the results obtained in this study illustrate a relatively good agreement.

A new framework for evaluation of rock fragmentation in open pit mines

The main purpose of blasting in open pit mines is to produce the feed for crushing stage with the optimum dimensions from in situ rocks. The size distribution of muck pile indicates the efficiency of blasting pattern to reach the required optimum sizes. Nevertheless, there is no mature model to predict fragmentation distribution to date that can be used in various open pit mines. Therefore, a new framework to evaluate and predict fragmentation distribution is presented based on the image analysis approach. For this purpose, the data collected from Jajarm bauxite mine in Iran were used as the sources in this study. The image analysis process was performed by Split-Desktop software to find out fragmentation distribution, uniformity index and average size of the fragmented rocks. Then, two different approaches including the multivariate regression method and the decision-making trial and evaluation laboratory(DEMATEL) technique were incorporated to develop new models of the uniformity index and the average size to improve the Rosin-Rammler function. The performances of the proposed models were evaluated in four blasting operation sites. The results obtained indicate that the regression model possesses a better performance in prediction of the uniformity index and the average size and subsequently the fragmentation distribution in comparison with DEMATEL and conventional Rosin-Rammler models.

Reliability-based maintenance scheduling of hydraulic system of rotary drilling machines

Hydraulic system has a critical and important role in drilling machines.Any failure in this system leads to problems in power system and machine operation.Since the failure cannot be prevented entirely,it is important to minimize its probability.Reliability is one of the most effcient and important method to study safe operation probability of hydraulic systems.In this research,the reliability of hydraulic system of four rotary drilling machines in Sarcheshmeh Copper Mine in Iran has been analyzed.The data analysis shows that the time between failures(TBF)of Machines A and C obey the Weibull(2P)and Weibull(3P)distribution,respectively.Also,the TBF of Machines B and D obey the lognormal distribution.With regard to reliability plots of hydraulic systems,preventive reliability-based maintenance time intervals for 80%reliability levels for machines in this system are 10 h.

Reliability and maintainability analysis of electrical system of drum shearers

The reliability and maintainability of electrical system of drum shearer at Parvade. 1 Coal Mine in central Iran was analyzed. The maintenance and failure data were collected during 19 months of shearer operation. According to trend and serial correlation tests, the data were independent and identically distributed （iid） and therefore the statistical techniques were used for modeling. The data analysis show that the time between failures （TBF） and time to repair （TTR） data obey the lognormal and Weibull 3 parameters distribution respectively. Reliability-based preventive maintenance time intervals for electrical system of the drum shearer were calculated with regard to reliability plot. The reliability-based maintenance intervals for 90%, 80%, 70% and 50% reliability level are respectively 9.91, 17.96, 27.56 and 56.1 h. Also the calculations show that time to repair （TTR） of this system varies in range 0.17-4 h with 1.002 h as mean time to repair （MTTR）. There is a 80% chance that the electrical system of shearer of Parvade. 1 mine repair will be accomplished within 1.45 h.

Prediction of rotary drilling penetration rate in iron ore oxides using rock engineering system

Prediction of the drilling penetration rate is one of the important parameters in mining operations. This parameter has a direct impact on the mine planning and cost of mining operations, Generally, effective parameters on the penetration rate is divided into two classes： rock mass properties and specifications of the machine, The chemical components of intact rock have a direct effect in determining rock mechan- ical properties, Theses parameters usually have not been investigated in any research on the rock drill- ability, In this study, physical and mechanical properties of iron ore were studied based on the amount of magnetite percent, According to the results of the tests, the effective parameters on the pen- etration rate of the rotary drilling machines were divided into three classes： specifications of the machi- nes, rock mass properties and chemical component of intact rock, Then, the rock drillahility was studied using rock engineering systems, The results showed that feed, rotation, rock mass index and iron oxide percent have important effect on penetration rate, Then a quadratic equation with 0,896 determination coefficient has been obtained, Also, the results showed that chemical components can he described as new parameters in rotary drill penetration rate,

Evaluating performance of cutting machines during sawing dimension stones

The performance of cutting machines in terms of energy consumption and vibration directly affects the production costs. In this work, our aim was to evaluate the performance of cutting machines using hybrid intelligent models. For this purpose, a systematic experimental work was performed. A database of the carbonate and granite rocks was established, in which the physical and mechanical properties of these rocks (i.e., UCS, elastic modulus, Mohs hardness, and Schmiazek abrasivity factor) and the operational parameters (i.e., depth of cut and feed rate) were considered as the input parameters. The predictive models were developed incorporating a combination of the multi-layered perceptron artificial neural networks and genetic algorithm (GANN-BP) and the support vector regression method and Cuckoo optimization algorithm (COA-SVR). The results obtained indicated that the performance of the developed GANN-BP and COA-SVR models was close to each other and that these models had good agreements with the measured values. These results also showed that these proposed models were suitable tools in evaluating the performance of cutting machines.