A smart look at monitoring while drilling (MWD) and optimizing using acoustic emission technique (AET).

Monitoring while drilling (MWD) is a crucial task in mining operations. Accurately measuring drill and rock-related operating parameters can significantly reduce the cost of drilling operations. This study explores the potential of monitoring drilling specific energy (SE) and optimizing drilling operations by processing vibroacoustic signals generated while drilling. For this purpose, 30 samples of different rocks, are used for drilling tests. During the drilling process, the acoustic and vibration signals are recorded and analyzed in the time, frequency, and time-frequency domains., and parameters related to the resulting spectra are extracted. After obtaining the vibroacoustic parameters for drilling, the relationship between them and the drilling SE was investigated. There is evidence that the progression of SE contributes to the magnitude of rock drilling vibroacoustic features, which could be employed to indicate energy conditions during drilling. Results obtained in this study have the potential to be used as the basis for an industrial monitoring system that can detect excessive energy consumption and advise the user of the end of the bit's useful life. This method can be an intelligent technique for measuring the behavior of real-time drilling operations based on the SE simply by installing vibroacoustic sensors on the drilling machines.

Environmentally sustainable mining in quarries to reduce waste production and loss of resources using the developed optimization algorithm.

The study of natural resources in the earth sciences focuses on the sustainable management of valuable materials like dimension stones. the quarrying of dimension stones is associated with environmental challenges such as significant amounts of waste production, and resource loss, mainly caused by discontinuities and fractures in the rock mass. Quarry optimization requires an optimal cutting pattern to increase the production of larger blocks while minimizing parameters that affect operational costs such as energy consumption. The algorithms used in the quarrying only focus on the number of blocks extracted, ignoring other factors such as energy consumption in the cutting of blocks. To address this issue, a new algorithm was developed in this study. The algorithm aims to optimize the quarrying process by analyzing the impact of discontinuities on waste production and cutting surfaces. It then provides an optimal cutting pattern for the quarry face based on the optimal value of these parameters. As a result, the use of this algorithm can serve as an efficient and valuable tool in dimension stone quarries. By implementing this algorithm, production costs, energy, and water consumption, cutting tools consumption, and waste production can be significantly reduced, leading to increased quarry profitability and decreased environmental problems.