Development of a roof bolter drilling control process to reduce the generation of respirable dust

The drilling operation in the roof bolting process, especially in hard rock, generates excessive respirable coal and quartz dusts, which could expose the roof bolting operator to continued health risks. Previous research has shown that the amount of respirable dust produced is dependent on the main drilling parameters, specifically the drilling rotational and penetration rate. In this paper, a roof bolter drilling control process was proposed to reduce the generation of respirable dust. Based on the analysis of laboratory drilling test results, a rational drilling control process (adjusting rotational and penetration rates) to achieve the optimal drilling parameter for different rock types was proposed. In this process, the ratio between specific energy and rock uniaxial compressive strength was used as the index to determine the optimal operation point. The recommended drilling operation range for the rock type used in the experiment was provided, and the reduction in respirable dust generation was demonstrated. By following this control process, the drilling efficiency can be monitored in real time, so the system can stay in a relatively high-energy efficiency with less respirable dust production from the drilling source. This algorithm is targeted to be incorporated into the current roof bolter drilling control system for drilling automation so that a safe and productive drilling operation can be conducted in a healthy working environment.

Intelligent Calibration of a Heavy-Duty Mechanical Arm in Coal Mine

Accurate positioning of an airborne heavy-duty mechanical arm in coal mine, such as a roof bolter, is important for the efficiency and safety of coal mining. Its positioning accuracy is affected not only by geometric errors but also by nongeometric errors such as link and joint compliance. In this paper, a novel calibration method based on error limited genetic algorithm (ELGA) and regularized extreme learning machine (RELM) is proposed to improve the positioning accuracy of a roof bolter. To achieve the improvement, the ELGA is firstly implemented to identify the geometric parameters of the roof bolter’s kinematics model. Then, the residual positioning errors caused by nongeometric facts are compensated with the regularized extreme learning machine (RELM) network. Experiments were carried out to validate the proposed calibration method. The experimental results show that the root mean square error (RMSE) and the mean absolute error (MAE) between the actual mast end position and the nominal mast end position are reduced by more than 78.23%. It also shows the maximum absolute error (MAXE) between the actual mast end position and the nominal mast end position is reduced by more than 58.72% in the three directions of Cartesian coordinate system.

Data Analytic Approaches for Mining Process Improvement—Machinery Utilization Use Case

This paper investigates the application of process mining methodology on the processes of a mobile asset in mining operations as a means of identifying opportunities to improve the operational efficiency of such. Industry 4.0 concepts with related extensive digitalization of industrial processes enable the acquisition of a huge amount of data that can and should be used for improving processes and decision-making. Utilizing this data requires appropriate data processing and data analysis schemes. In the processing and analysis stage, most often, a broad spectrum of data mining algorithms is applied. These are data-oriented methods and they are incapable of mapping the cause-effect relationships between process activities. However, in this scope, the importance of process-oriented analytical methods is increasingly emphasized, namely process mining (PM). PM techniques are a relatively new approach, which enable the construction of process models and their analytics based on data from enterprise IT systems (data are provided in the form of so-called event logs). The specific working environment and a multitude of sensors relevant for the working process causes the complexity of mining processes, especially in underground operations. Hence, an individual approach for event log preparation and gathering contextual information to be utilized in process analysis and improvement is mandatory. This paper describes the first application of the concept of PM to investigate the normal working process of a roof bolter, operating in an underground mine. By applying PM, the irregularities of the operational scheme of this mobile asset have been identified. Some irregularities were categorized as inefficiencies that are caused by either failure of machinery or suboptimal utilization of the same. In both cases, the results achieved by applying PM to the activity log of the mobile asset are relevant for identifying the potential for improving the efficiency of the overall working process.

Field study results of a 3rd generation roof bolter canopy air curtain for respirable coal mine dust control

A 3rd generation roof bolter canopy air curtain (CAC) has been developed and constructed by J.H. Fletcher & Co., Inc. As with the previous generation of the CAC, this design uses the principle of providing uniform airflow across the canopy area as recommended by the National Institute for Occupational Safety and Health. The new modifications include a plenum that is constructed of a single flat aluminum plate, smaller-diameter airflow openings, and a single row of perimeter nozzles designed to prevent mine air contaminated by respirable dust from entering the CAC protection zone. Field testing was conducted on this new 3rd generation design showing reductions in coal mine respirable dust exposure for roof bolter operators. Dust control efficiencies for the CAC for the left bolter operator (intake side) ranged from approximately 26%–60%, while the efficiencies for the CAC for the right bolter operator (return side) ranged from 3% to 47%.