Mining Contract: Improving Health and Safety of Mining Operations Through Development of the Smart Bit Concept for Automation of Mechanical Rock Excavation Units and Dust Mitigation
Automation of some pieces of mining equipment will allow for workers to be removed from areas of a mine in which dust and ground control conditions may pose a hazard. In developing a Smart Bit or Smart Pick concept, this research could advance a key component of automating the mechanical excavation units in various mining operations. This work will also develop a load sensing system to monitor the cutting forces on the pick, wireless communication of the data from the drum to the machine, and pattern recognition algorithms to identify the rock being cut using machine learning (ML) and artificial intelligence (AI) systems. These systems, combined with remote and automated machine control, will enable the machines to operate without the need for exposure of personnel to possible health and safety risks.
Contract Status & Impact
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Two of the persisting health and safety issues in the U.S. mining industry are ground control and dust. Statistics show that despite steady advancements in ground control methods and techniques, injuries and fatalities attributed to ground control issues remain, suggesting there is room for improvement or a new set of solutions. Statistics also show respiratory diseases have been on the rise since the mid-1990s, and there is a pressing need to address this negative trend. One solution that could address a number of these issues is automating various mining activities and removing miners from the potentially unsafe and unhealthy work conditions. The automation of rock excavation units could be a first step in that direction.
Excavation of soft rock in mining applications in dominated by mechanical excavation units such as roadheaders, continuous miners (CM), longwall drum shearers or plough, borer miners, and surface miners. These machines serve various underground mines and some surface mining operations in a number of sectors that extract a variety of commodities. The production rates of these machines are relatively high and they offer safer working environments than compared to drill and blast operations, which is the alternative means of rock excavation in mining operations. The cutting tools used on these machines are primarily drag-type tools, including conical/point attack picks and radial tools. The tools are the interface between the machine and the rock and pass through various formations at the face while working.
Under this contract, the research team plans to instrument picks to be able to measure the cutting forces on a real-time basis and then communicate that information with machine operators. This information is a critical part of the automation of the excavation units and can be used to identify the various formations being cut, which allows for horizon control. The measured forces will also allow operators to identify missing or broken tools as well as picks that have lost their tip or are dull. Worn tools tend to generate more dust, which is a major issue in mining operations. Further, as tools wear, the cutting forces of machines must gradually increase to make up for the decrease in bit sharpness, further grinding down the bits. Dull tools or picks that have lost their tip require more energy to cut, which in turn generates more dust when mining the face. However, having the ability to sense the load on the cutters would allow operators to monitor bit conditions and change them as needed to minimize dust generation at the source.
Smart Bit or Smart Pick technology may allow for certain excavation units to transition to automation. With sensors integral to the cutting tools that are in contact with rock formations, these tools will be equipped to identify rock type and cutting conditions without the need for additional optical or other sensory systems. This concept could be applied to other rock excavation units used in underground or surface mining.
This capacity-building contract and the resulting research by the Colorado School of Mines will support a variety of masters and PhD students. The PhD thesis topics are expected to be as follows:
- Design and Fabrication of Load Sensor
- Dust Study and Characterization as a Function of Cutting Geometry
- Design of Load Sensory System and Data Transmission
- Initial Data Analysis and Pattern Recognition for Rock Identification While Cutting
- Correlation of Pick Wear and Dust Generation
- Data Analytics and Deep Learning of Test Data for Incorporation of Pick Wear
- 60 Years of Rockbursting in the Coeur D'Alene District of Northern Idaho, USA: Lessons Learned and Remaining Issues
- Best Practices for Controlling Respirable Dust in Coal Mines
- Comparison of Ground Conditions and Ground Control Practices in the United States and Australia
- Determination of In Situ Deformation Modulus for Cemented Rockfill
- Dynamic Failure in Deep Coal: Recent Trends and a Path Forward
- Numerical Model Calibration for Simulating Coal Pillars, Gob and Overburden Response
- Safety Breakthrough -- Reduced Exposure Mining System (REMS)
- Status of a Tapered Element, Oscillation Microbalance-Based Continuous Respirable Coal Mine Dust Monitor
- Technology News 486 - Floor Heaters Can Increase Operator's Dust Exposure in Enclosed Cabs
- Testing and Evaluation of an Inflatable Temporary Ventilation Control Device