RESUMEN
Maximizing the use of explosives is crucial for optimizing blasting operations, significantly influencing productivity and cost-effectiveness in mining activities. This work explores the incorporation of machine learning methods to predict powder factor, a crucial measure for assessing the effectiveness of explosive deployment, using important rock characteristics. The goal is to enhance the accuracy of powder factor prediction by employing machine learning methods, namely decision tree models and artificial neural networks. The analysis finds key rock factors that have a substantial impact on the powder factor, hence enabling more accurate planning and execution of blasting operations. The analysis uses data from 180 blast rounds carried out at a dolomite mine in south-south Nigeria. It incorporates measures such as root mean square error (RSME), mean absolute error (MAE), R-squared (R2), and variance accounted for (VAF) to determine the best models for predicting powder factor. The results indicate that the decision tree model (MD4) outperforms alternative approaches, such as artificial neural networks and Gaussian Process Regression (GPR). In addition, the research presents an efficient artificial neural network equation (MD2) for estimating the values of optimum powder factor, demonstrating outstanding blasting fragmentation. In conclusion, this research provides significant information for improving the accuracy of powder factor prediction, which is especially advantageous for small-scale blasting operations.
RESUMEN
Arsenic contamination not only complicates mineral processing but also poses environmental and health risks. To address these challenges, this research investigates the feasibility of utilizing Hyperspectral imaging combined with machine learning techniques for the identification of arsenic-containing minerals in copper ore samples, with a focus on practical application in sorting and processing operations. Through experimentation with various copper sulfide ores, Neighborhood Component Analysis (NCA) was employed to select essential wavelength bands from Hyperspectral data, subsequently used as inputs for machine learning algorithms to identify arsenic concentrations. Results demonstrate that by selecting a subset of informative bands using NCA, accurate mineral identification can be achieved with a significantly reduced the size of dataset, enabling efficient processing and analysis. Comparison with other wavelength selection methods highlights the superiority of NCA in optimizing classification accuracy. Specifically, the identification accuracy showed 91.9% or more when utilizing 8 or more bands selected by NCA and was comparable to hyperspectral data analysis with 204 bands. The findings suggest potential for cost-effective implementation of multispectral cameras in mineral processing operations. Future research directions include refining machine learning algorithms, exploring broader applications across diverse ore types, and integrating hyperspectral imaging with emerging sensor technologies for enhanced mineral processing capabilities.
RESUMEN
Recent and past studies mainly focus on reducing the dead weight of structure; therefore, they considered lightweight aggregate concrete (LWAC) which reduces the dead weight but also affects the strength parameters. Therefore, the current study aims to use varied steel wire meshes to investigate the effects of LWAC on mechanical properties. Three types of steel wire mesh are used such as hexagonal (chicken), welded square, and expanded metal mesh, in various layers and orientations in LWAC. Numerous mechanical characteristics were examined, including energy absorption (EA), compressive strength (CS), and flexural strength (FS). A total of ninety prisms and thirty-three cubes were made. For the FS test, forty-five 100 × 100 × 500 mm prism samples were poured, thirty-three 150 × 150 × 150 mm cube samples were made, and forty-five 400 × 300 × 75 mm EA specimens were costed for fourteen days of curing. The experimental findings demonstrate that the FS was enhanced by adding additional forces that spread the forces over the section. One layer of chicken, welded, and expanded metal mesh enhances the FS by 52.96%, 23.76%, and 22.2%, respectively. In comparison to the remaining layers, the FS in a single-layer hexagonal wire mesh has the maximum strength, 29.49 MPa. The hexagonal wire mesh with a single layer had the greatest CS, measuring 36.56 MPa. When all three types of meshes are combined, the CS does not vary in this way and is estimated to be 29.79 MPa. In the combination of three layers, the chicken and expanded wire mesh had the most energy recorded prior to final failure, which was 1425.6 and 1108.7 J, whereas it was found the highest 752.3 J for welded square wire mesh. The energy absorption for the first layer with hexagonal wire mesh increased by 82.81% prior to the crack and by 88.34% prior to the ultimate failure. Overall, it was determined and suggested that hexagonal wire mesh works better than expanded and welded wire meshes.