Computational Intelligent Impact Force Modeling And Monitoring In Hislo Conditions For Maximizing Surface Mining Efficiency Safety And Health
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Computational Intelligent Impact Force Modeling and Monitoring in HISLO Conditions for Maximizing Surface Mining Efficiency, Safety, and Health
"Shovel-truck systems are the most widely employed excavation and material handling systems for surface mining operations. During this process, a high-impact shovel loading operation (HISLO) produces large forces that cause extreme whole body vibrations (WBV) that can severely affect the safety and health of haul truck operators. Previously developed solutions have failed to produce satisfactory results as the vibrations at the truck operator seat still exceed the 'Extremely Uncomfortable Limits.' This study was a novel effort in developing deep learning-based solution to the HISLO problem. This research study developed a rigorous mathematical model and a 3D virtual simulation model to capture the dynamic impact force for a multi-pass shovel loading operation. The research further involved the application of artificial intelligence and machine learning for implementing the impact force detection in real time. Experimental results showed the impact force magnitudes of 571 kN and 422 kN, for the first and second shovel pass, respectively, through an accurate representation of HISLO with continuous flow modelling using FEA-DEM coupled methodology. The novel 'DeepImpact' model, showed an exceptional performance, giving an R2, RMSE, and MAE values of 0.9948, 10.750, and 6.33, respectively, during the model validation. This research was a pioneering effort for advancing knowledge and frontiers in addressing the WBV challenges in deploying heavy mining machinery in safe and healthy large surface mining environments. The smart and intelligent real-time monitoring system from this study, along with process optimization, minimizes the impact force on truck surface, which in turn reduces the level of vibration on the operator, thus leading to a safer and healthier working mining environments"--Abstract, page iii.
Proceedings of the International Workshop on Numerical Modeling for Underground Mine Excavation Design
Author: Department of Health and Human Services
language: en
Publisher: CreateSpace
Release Date: 2013-10
Reliable prediction of mine stability, surface subsidence, mine water inflow, and mine gas emissions is essential not only for improving mine safety and reducing coal production costs, but also for assessing and managing the environmental impact of mining. This paper describes an integrated approach to simulation and prediction of mining-induced surface subsidence, mine groundwater inflow, aquifer interference, and mine gas emission. It involves a combination of site geological, geotechnical, and hydrogeological characterization; study of surface subsidence and subsurface rock caving mechanisms; monitoring of pore pressure changes of the surrounding strata, mine water inflows, and mine gas emission; and three-dimensional (3-D) numerical modeling. Central to this integrated approach is a 3-D computer code called COSFLOW developed by CSIRO Exploration and Mining of Australia in collaboration with NEDO and JCOAL of Japan to address the coal mine-related issues. COSFLOW incorporates unique features (e.g., Cosserat continuum formulation) that make it ideal for simulating coal mining-related issues and examining the interaction between rock fracture, aquifer interference and water flow, and gas emission.
Improving Efficiency of Truck-shovel Materials Handling Systems in Surface Mining Through Simulation and Optimization Tools
"The mining industry is characterized by high technical and financial risks. First of all, ore quantity within a deposit cannot be, at least in the feasibility stage, fully calculated due to sparse data and ore grade heterogeneity. In the standard approach, all decisions regarding a mining operation are made using estimations or simulations, which add risk to an operation. Furthermore, commodity prices fluctuate widely in the market. As witnessed recently, serious price slumps can be experienced and force the mining companies to operate at a loss or narrow profit margin. As a result, a mining company produces a material, which is not delineated accurately and whose sale price is not known. Therefore, mining companies put a specific emphasis on the best practices such that the effect of uncertainties is minimized. One way to manage this is to maximize the utilization of mining trucks and shovels under uncertainty as the operating and opportunity cost of mining equipment is very high.In this context, this research developed new modelling, simulation and optimization approaches to improve the performance of truck-shovel systems. First, the compliance between truck and shovel fleet was measured by integrating reliability theory and the match factor equation. In doing so, the opportunity cost of mining equipment was reduced by decreasing the waiting time of the trucks and the idle time of the shovels. Also, the research provides reliability analysis for mining equipment and the operators' effect on the reliability change. Moreover, a Petri net simulation model of the materials handling system is created by assessing randomness associated with data variations, ambiguity, and vagueness. The uncertain parameters (such as the cycle time of the trucks, the loading time, ore grade, payload, fillfactor, operators' effect) were included in the simulation model. This model was used to compare the dispatching and the short-term mine planning objectives such as blending in the case of multiple waste dumps and processors. The simulation model also tracked the fuel consumption of the haul trucks. Furthermore, the relationship among the interrelated mining activities (drilling, blasting, loading, hauling and crushing) was investigated. The fragmentation size is the factor which affects the costs and performances of all activities. Hence, it was optimized through a system-wide optimization approach to minimize the total bench production cost in surface mining operations.In conclusion, a novel two-stages real-time optimization framework was proposed using knowledge from the aforementioned aspects. In the first stage, a Petri net simulation model is used to decide the production targets and divide the trucks into sub-fleets for each working zone. The working zones may include more than one shovel. In the second stage, the trucks are simultaneously dispatched to the shovels by linear programming. Also, the conformity of the sub-fleets is dynamically tracked by the match factor value to minimize the shovel idle times and truck queues. If required, the trucks are moved among the sub-fleets. The case studies proved that the proposed approaches reduced actual operating and opportunity costs in mining operations. Thus, utility obtained from truck and shovel systems were increased"--