Efficiency of standardized image processing in the fragmentation prediction in the case of Sungun open-pit mine

The paper proposes a standardized image-processing procedure with the use of sieve analysis results for calibration which is utilized to measure the size distribution of fragmentation at Sungun mine. Through this procedure, a number of 19 bench blasting in various levels have been initially selected as the target of the study for each, multiple photos were taken immediately after blast from suitable perspectives and locations of the muckpiles surfaces. The number of image sampling was chosen adequately high to achieve further reliability of the whole photography procedure. Then fragments of each muckpile were separately mixed by a loader, where another image sampling from these new muckpiles, bucket of loaders, and haulage trucks was performed. For the purpose of sieve analysis, seven sieves with the mesh sizes between 1.27 cm (0.5 in) and 25.4 cm (10 in) were designed, manufactured, and then installed at Sungun semi-industrial laboratory. Additionally, three mass samples of the mixed fragments were randomly chosen among the 19 muckpiles for sieving. During image analysis stage, “sieve shift” and “mass power” factors, required to obtain standardized size distribution, were precisely assigned when the results obtained by the image analysis software was in accordance with the sieving results. In order to validate the reliability of the image processing, a comparative analysis of the achieved results was made with the results of the original Kuz–Ram model [Cunningham (1983) The Kuz–Ram model for prediction of fragmentation from blasting. In: Proceedings of the first international symposium on rock fragmentation by blasting, Lulea, Sweden, pp 439–454]. Finally, the image-processing procedure was found to be more efficient, with results close-matched to the real results of the sieve analysis.     

Assessment of workplace accident risks in underground collieries by integrating a multi-goal cause-and-effect analysis method with MCDM sensitivity analysis

This study introduces a method using a multi-goal fuzzy cognitive map (FCM) and multi-criteria decision making based on sensitivity analysis to assess the risks associated with working accidents in underground collieries. Safety, stoppage in operation, and operational and capital costs are considered as the main goals during the FCM process with significant emphasis on safety. Workplace accidents data from Kerman underground collieries are statistically evaluated to find the degrees of occurrence probability, severity, and work-disability duration as the main risk factors. The causes and effects of accidents are analyzed using FCM based on three goals and the effects of risk factors. A sensitivity analysis on the weights of the goals is conducted with the aim of increasing the workplace safety in TOPSIS environment after solving the designed multi-goal FCM. Results indicate that “gas poisoning,” “roof fall,” and “debris and destruction” take the first three ranks and impose high risks to the system. By contrast, “collision, hit, and crash” presents the lowest risk among all accidents.     

Prediction of Dust Emission Due to Open Pit Mine Blasting Using a Hybrid Artificial Neural Network

Natural Resources Research - This study developed a new perspective of artificial neural networks using dimensional analysis to be applicable to certain prediction problems. To this end,...     

Optimization of Blasting-Associated Costs in Surface Mines Using Risk-based Probabilistic Integer Programming and Firefly Algorithm

Natural Resources Research - Blasting outcomes have significant impacts on downstream mining operations such as loading, hauling, crushing, milling, and mineral processing. An ideal blasting plan...