Conclusion: The study established an acceptable EC correlation between data using the MAHA MPM-4M aerosol DPM analyser for samples collected from the manifold exhaust sample point and samples collected from the vehicle exhaust. The study also showed an acceptable correlation between EC data using the MAHA MPM-4M LLS device and the NIOSH 5040 quartz filter analysis from samples collected at both the manifold and the engine exhaust.
The study also showed an acceptable correlation between EC data using the MAHA MPM-4M LLS device and the NIOSH 5040 quartz filter analysis from samples collected at both the manifold and the engine exhaust.
Although it was thought that a revised correction factor would be required for different engine types, this was not found to be the case based on the data produced from this study. Despite the fact that the sample size for some engine types was small, these engines exhibited a similar degree of variance in EC between the MAHA MPM-4M LLS device and the NIOSH 5040 quartz filter analysis to that of engine types with a larger sample size.
Based on the results of this research, the current correction factor used in LLS devices such as the MAHA MPM-4M requires updating from 0.46 to 0.65 when sampling from the exhaust and 0.67 when sampling from the manifold exhaust.
The ability to take samples directly from the manifold exhaust for EC analysis has advantages over taking samples from the vehicle exhaust. These include eliminating issues relating to water vapour in the sample, control over probe insertion and position and more realistic data in relation to engine emissions and condition prior to other devices that may be fitted to the engine.
There seems to be an unfounded perception within the coal mining industry that the accuracy of the LLS and other DPM devices that are currently is use is absolute. Given the equipment and testing variables that can occur during the routine EC and TPM engine testing, results will vary between operators. While an acceptable correlation between LLS devices and the NIOSH 5040 quartz filter analysis has been established during this study, the focus should remain with good engine maintenance and perhaps the adoption of EC value ranges as opposed to a specific concentration as an upper limit.
While a number of analysis outliers were evident from the results, the number was relatively small and did not impact on the overall findings.
The use of the ERP chamber vessel for engine manifold sampling and the Freudenberg sampling system for quartz filter sampling confirmed the observations by Dr Brian Davies in the 2013, Wollongong University, Coal Services Health and Safety Trust research as being suitable devices for this type of testing.
Working closely with the UAV operator in Adelaide, UC will develop a prototype SDR system to allow for UAV communications from above ground monitor stations to the mining face via a UAV deployed node based mesh network that has non-line of sight coverage using COFDM technology. UC will also develop the transmitters and receivers that are mounted on the aircraft.Task 3: Prototype Demonstration
A demo of the UAV operating in an underground mine using the prototype communications system with 8 battery powered nodes to control the UAV, stream thermal and normal video, and transmit gas sensor data from the UAV back to the surface. The nodes will be small enough to be carried and deployed by the UAV and will provide at least 3 hours of battery life.Task 4: Routing Algorithm Optimisation
Development of a self-configuring energy efficient routing algorithm for the UAV Communication system will be focused in this task. Data routing is one of the core challenges in the UAV Communication system since the router connectivity may change frequently and latency and dropouts could be catastrophic to the vehicle. The designed routing algorithm must support a multi-hop communication paradigm and provide alternative connections in the event of the failure of current routes.Task 5: Intrinsically Safe Configuration Design
UC will work with Strata and the Mine Safety Testing Centre (MSTC) to test the prototype UAV communication system, and provide test reports demonstrating and providing independent proof, for compliance with national and international IS standards.
At the completion of the project, CS Health will have demonstrated whether a 30-45 minute musculoskeletal screen can identify risk trends in conjunction with data collected from the Order 41 periodic health surveillance medical. Upon identification of these trends, a targeted intervention can be designed to address the workforce. These results will allow industry to identify training requirements that are associated with specific roles within a mine leading to a reduction in common injuries.
To determine whether currently utilised respirator filters effectively filter out Diesel Particulate Matter and provide worker protection; by testing respirator filters used in mining workplaces against DPM, and by measuring the sizes of particles that are penetrating the filters to determine whether that poses an additional health risk for workers.
The outcome of the projects will be a comprehensive and detailed description of the whole body vibration exposures associated with the operation of underground coal mining equipment at two exemplar sites.