Automated installation of primary roof support material can potentially increase productivity and operator safety in the roadway development process within underground coal mining. Although the broader manufacturing s...Automated installation of primary roof support material can potentially increase productivity and operator safety in the roadway development process within underground coal mining. Although the broader manufacturing sector has benefited from automation, several challenges exist within the Australian underground coal industry which makes it difficult to fully exploit these technologies. At the University of Wollongong a series of reprogrammable electromechanical manipulators have been designed to overcome these challenges and automatically handle the installation of roof and rib containment consumables on a continuous miner. The automated manipulation removes personnel from hazards in the immediate face area, particularly those associated with working in a confined and unstable working environment in close proximity to rotating and moving equipment. In a series of above ground trials the automated system was successfully demonstrated without human intervention and proven to be capable of achieving cycle times at a rate of 10 m per operating hour, consistent with that required to support high capacity longwall mines. The trials also identified a number of refinements which could further improve both cycle times and system reliability when considering the technology for underground use. The results have concluded that conventional manual handling practices on a continuous miner can be eliminated, and that the prototypes have significantly reduced the technical risk in proceeding to a full underground trial.展开更多
Open pit mining operations utilize large scale and expensive equipment. For the mines implementing shovel and truck operation system, trucks constitute a large portion of these equipment and are used for hauling the m...Open pit mining operations utilize large scale and expensive equipment. For the mines implementing shovel and truck operation system, trucks constitute a large portion of these equipment and are used for hauling the mined materials. In order to have sustainable and viable operation, these equipment need to be utilized efficiently with minimum operating cost. Maintenance cost is a significant proportion of the overall operating costs. The maintenance cost of a truck changes non-linearly depending on the type, age and truck types. A new approach based on stochastic integer programming （SIP） techniques is used for annually scheduling a fixed fleet of mining trucks in a given operation, over the life of mine （multi-year time horizon） to minimize maintenance cost. The maintenance cost data in mining usually has uncertainty caused from the variability of the operational conditions at mines. To estimate the cost, usually historic data from different operations for new mines, and/or the historic data at the operating mines are used. However, maintenance cost varies depending on road conditions, age of equipment and many other local conditions at an operation. Traditional models aim to estimate the maintenance cost as a deterministic single value and financial evaluations are based on the estimated value. However, it does not provide a confidence on the estimate. The proposed model in this study assumes the truck maintenance cost is a stochastic parameter due to the significant level of uncertainty in the data and schedules the available fleet to meet the annual production targets. The scheduling has been performed by applying both the proposed stochastic and deterministic approaches. The approach provides a distribution for the maintenance cost of the optimized equipment schedule minimizing the cost.展开更多
基金the Australian Coal Association Research Programthe Roadway Development Task Group for their ongoing support with funding and review
文摘Automated installation of primary roof support material can potentially increase productivity and operator safety in the roadway development process within underground coal mining. Although the broader manufacturing sector has benefited from automation, several challenges exist within the Australian underground coal industry which makes it difficult to fully exploit these technologies. At the University of Wollongong a series of reprogrammable electromechanical manipulators have been designed to overcome these challenges and automatically handle the installation of roof and rib containment consumables on a continuous miner. The automated manipulation removes personnel from hazards in the immediate face area, particularly those associated with working in a confined and unstable working environment in close proximity to rotating and moving equipment. In a series of above ground trials the automated system was successfully demonstrated without human intervention and proven to be capable of achieving cycle times at a rate of 10 m per operating hour, consistent with that required to support high capacity longwall mines. The trials also identified a number of refinements which could further improve both cycle times and system reliability when considering the technology for underground use. The results have concluded that conventional manual handling practices on a continuous miner can be eliminated, and that the prototypes have significantly reduced the technical risk in proceeding to a full underground trial.
文摘Open pit mining operations utilize large scale and expensive equipment. For the mines implementing shovel and truck operation system, trucks constitute a large portion of these equipment and are used for hauling the mined materials. In order to have sustainable and viable operation, these equipment need to be utilized efficiently with minimum operating cost. Maintenance cost is a significant proportion of the overall operating costs. The maintenance cost of a truck changes non-linearly depending on the type, age and truck types. A new approach based on stochastic integer programming （SIP） techniques is used for annually scheduling a fixed fleet of mining trucks in a given operation, over the life of mine （multi-year time horizon） to minimize maintenance cost. The maintenance cost data in mining usually has uncertainty caused from the variability of the operational conditions at mines. To estimate the cost, usually historic data from different operations for new mines, and/or the historic data at the operating mines are used. However, maintenance cost varies depending on road conditions, age of equipment and many other local conditions at an operation. Traditional models aim to estimate the maintenance cost as a deterministic single value and financial evaluations are based on the estimated value. However, it does not provide a confidence on the estimate. The proposed model in this study assumes the truck maintenance cost is a stochastic parameter due to the significant level of uncertainty in the data and schedules the available fleet to meet the annual production targets. The scheduling has been performed by applying both the proposed stochastic and deterministic approaches. The approach provides a distribution for the maintenance cost of the optimized equipment schedule minimizing the cost.
