Optimizing multi-spectral ore sorting incorporating wavelength selection utilizing neighborhood component analysis for effective arsenic mineral detection.

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.

Soft ground micro TBM jack speed and torque prediction using machine learning models through operator data and micro TBM-log data synchronization.

Tunnel Boring Machines (TBMs) are pivotal in underground projects like subways, highways, and water supply tunnels. Predicting and monitoring jack speed and torque is crucial for optimizing TBM excavation efficiency. Conventionally, skilled operators manually adjust numerous tunnelling parameters to regulate the machine's progress. In contrast, machine learning (ML) algorithms offer a promising avenue where computers learn from operator actions to establish parameter relationships autonomously. This study introduces an innovative approach to enhancing operator monitoring and TBM data comprehension. A robust correlation between TBM operator behaviour and TBM logged data is established by leveraging an Optuna-assisted ML methodology-the research light on the intricate dynamics influencing TBM advance rate parameters. Operational data is collected from micro slurry tunnel boring machine (MSTBM) umbrella support excavations. The proposed framework harnesses Optuna, an advanced hyperparameter optimization platform, to dynamically refine jack speed and torque settings. Through meticulous analysis of the interplay between TBM operator decisions and real-time logged data, the AI model discerns patterns, empowering informed decision-making. Using Optuna, a range of models, including random forest (RF), K-nearest neighbours (kNN), decision tree (DT), XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN) were automatically compared and tuned. The best model's (RF) performance is evaluated through a correlation coefficient (R2) of 96%, mean squared error (MSE) of 119.7, and mean absolute error (MAE) of 4.42 for jack speed decision making while 83% of R2, MSE of 0.62, and MAE of 0.42 for the torque decision making. This intelligent model can assist the TBM operator in making decisions about TBM control.

Effect of rock properties on wear and cutting performance of multi blade circular saw with iron based multi-layer diamond segments.

This study is an attempt for comprehensive, combining experimental data with advanced analytical techniques and machine learning for a thorough understanding of the factors influencing the wear and cutting performance of multi-blade diamond disc cutters on granite blocks. A series of sawing experiments were performed to evaluate the wear and cutting performance of multi blade diamond disc cutters with varying diameters in the processing of large-sized granite blocks. The multi-layer diamond segments comprising the Iron (Fe) based metal matrix were brazed on the sawing blades. The segment's wear was studied through micrographs and data obtained from the Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-ray (EDS). Granite rock samples of nine varieties were tested in the laboratory to determine the quantitative rock parameters. The contribution of individual rock parameters and their combined effects on wear and cutting performance of multi blade saw were correlated using statistical machine learning methods. Moreover, predictive models were developed to estimate the wear and cutting rate based on the most significant rock properties. The point load strength index, uniaxial compressive strength, and deformability, Cerchar abrasivity index, and Cerchar hardness index were found to be the significant variables affecting the sawing performance.

Solid-liquid separation by sonochemistry: a new approach for the separation of mineral suspensions.

The effect of sonochemistry to acidify solutions was applied for the solid-liquid separation of three kinds of mineral suspensions. At first, the relationship was measured between zeta-potential and pH in these suspensions to find pH levels correspondent to the isoelectric points. Then sonication (200 kHz or 28 kHz) was applied to adjust pH to the isoelectric points and separated particles from solutions by still-standing and spontaneous precipitation. Compared to the conventional methods using filters and chemical agents, the advantage of this sonochemical separation is two-fold. First, it does not require the maintenance of filters. Second, separated particles are easy to use since they are not mixed with pH adjusters and chemical flocculants. Isoelectric zone (ion strength 0.01, concentration 0.001 wt.%) of green tuff, andesite and titanium dioxide suspensions tested in this study were pH 1.1-3.7, 0.8-3.4, 2.7-5.7, respectively. The sonication of green tuff and andesite suspensions at 200 kHz changed the pH to the isoelectric zone despite the pH buffering effect of eluted alkali earth metals, and successfully precipitated the particles. On the contrary, the sonication of these suspensions at 28 kHz failed to adjust pH to the isoelectric zone, and the particles did not precipitate. In addition, the degradation of particles was observed in the SEM photographs of particles sonicated at 28 kHz, whereas no significant change was detected in particles sonicated at 200 kHz. Thus, it is concluded that the optimal frequency is about 200 kHz because its strong chemical effect can easily adjust the pH while its relatively weak physical effect prevents the degradation of particles.