Mining Technology: Transactions of the Institutions of Mining and Metallurgy

Overburden removal is a major activity of surface coal mining and accounts for over 60–70% of the costs. Cast blasting is integral to overburden removal using draglines. Knowledge of cast blasting was combined with data analytics and machine learning algorithms to predict cast blast percentage. In a typical study, the cast percentage is predicted as a function of key input variables, namely (1) height to burden (H/b) ratio, (2) height to width (H/W) ratio, (3) length to width (L/W) ratio, (4) effective in-hole explosive density (d_e – te/m³), (5) powder factor (PF) (m³/kg – volume of rock broken per kg of explosive), and (6) average delay per unit width of burden (ms/m). Random forest algorithm was used under five-fold cross-validation with 68 datasets split into 57 for training and 11 for testing purposes. The model produced an R ² value of 69.16% and 67.37% respectively on the training and testing data.

A FLAC^3D based model has been developed to estimate the extent of coal wall spalling for a longwall face having a working depth of 406.5 m during the main weighting period. The results obtained from the model helped in characterizing two key phases of main weighting, namely, peak stress, and main fall period based on vertical stress concentration factor, percentage of yield zones, and extent of yielding ahead of the face. The maximum extent of coal wall spalling of 2 m was observed during the main fall period. Spalling is mainly concentrated near the upper-middle and the top sections of the face.

The mining industry is currently undergoing a significant evolution in its production processes to search for greater sustainability in its operations. Historically, identifying the low performance of equipment occurs with inspections on the mining faces or through management reports. However, through the advent of technology and greater processing and data transmission capacity, many companies have developed research to improve the decision-making process to identify events in real-time to increase mine safety and act on low-performance equipment. Thus, this paper presents the development of a cyber-physical system to control loading productivity for open-pit mines. Furthermore, the tool allows analysis of the productivity, which generates alarms in low-performance situations measured according to pre-established targets. In the practical applications of the method, the increase in loading productivity reached 4.71% by reducing process variability. In addition, the stability of the loading operation resulted in the lowest queues, fuel consumption and greenhouse gas emissions.

Flash floods generated by runoff from abandoned spoil heaps have posed great risks to neighbouring areas but there has been little study on the process, so it is difficult to evaluate the effectiveness of runoff retention or detention measures. These events are likely to become more frequent in a future climate change scenario. To provide a means by which interested parties can study such events, including methods of mitigation, a computer modelling approach is proposed using free open-source software. Unlike conventional tools that solve the shallow water equations, the software tool uses a cellular automata approach to reduce the computational overhead. It is also easy to use so it is suitable for use by practitioners who are not hydrology experts. The method is illustrated by modelling a historical flood in the UK involving spoil heap runoff. Good correlation was observed between the characteristics of the actual and simulated flood event.

For the scaling process to be successful, it is important to first detect the loose rock. Even today, this task is mainly performed by experienced personnel. This leads to opportunities for increased potential to use sensor driven digital assistance systems. This paper presents a review and analysis of sensor-based loose rock detection methods considering the specific conditions of testing and use. The investigations can be classified into three categories based on their respective sensor technology approach. The opportunities for automating the scaling process highly depend on the method used. If scaling is conducted manually, the pre-entry examination could be automated. For the automation of the entire process, the use of machinery is more beneficial. Long wave infrared (LWIR) cameras or visual techniques have a great potential. However, in the case of LWIR, innovative methods for generating a sufficient heat flow between air and rock need to be developed in future.