The present study focuses on the optimization of alkali activation of ferronickel (FeNi) slag for the production of alkali-activated materials (AAMs). The effect of the main factors including molarity and SiO₂/Na₂O molar ratio in the activating solution, pre-curing and curing time, curing temperature, and aging period on the compressive strength and other properties of the final products is assessed. Emphasis is paid to the study of the effect of low curing temperature to decrease the overall footprint of alkali activation. Several analytical techniques including X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and Scanning electron microscopy (SEM) are used for the identification of the mineralogy and the morphology of the precursor and the final products. The experimental results indicate that the produced AAMs achieve a maximum compressive strength of 99 MPa using NaOH molarity 8 mol/L (M), SiO₂/Na₂O molar ratio 1, curing temperature 80 °C, curing time 24 h, and aging period 7 days. SEM/EDS–energy dispersive X-ray spectroscopy analysis indicates that alkali activation results in a homogeneous binding phase, characteristic of the expected iron-rich matrix, while the microstructure of the AAMs is characterized by a glassy and smooth surface without the presence of any visible cracks or defects. The produced AAMs may be used as alternative binders for the replacement of cement in the production of concrete or as construction elements.

Greece has mineral resources in which strategic elements such as nickel (Ni) and cobalt (Co) are present. In this study, characterizations of the metal concentrations of three laterite deposits, as well as minerals from a kiln originating from a mineral processing plant, were carried out. The aim of this research was to integrate technological characterization data on the quantities of cobalt (Co) and nickel (Ni) present in the lateritic profiles of three mineral deposits from Greece and in kiln dust. The study was conducted at the Mineral Processing Laboratory of UPC/Bases Manresa. Aliquots were constituted for particle-size distribution tests by wet sieving followed by laser granulometry. Scanning electron microscopy (SEM) was used to determine the mineralogical compositions. Sink–float tests were conducted to estimate the particle liberation. Mineral liberation analysis (MLA) was performed to understand the liberation and the association of minerals in the samples.

Asbolane is a secondary source of cobalt (Co) and manganese (Mn), essential for battery and alloy production. Enhancing the utilization of low-grade ores, typically containing ~1.2% Co and 14.7% Mn, is vital for conserving high-grade resources. However, fine grinding for such ores presents challenges for conventional gravity separation. This study investigates the effectiveness of the Multi-Gravity Separator (MGS) in processing finely disseminated asbolane ore from Agios Ioannis, Greece. The study was conducted at the Mineral Processing Laboratory of UPC/Bases Manresa. Two size fractions, D₈₀ (−100 +50 µm and −50 µm), were tested under varying drum speeds, tilt angles, and wash water flows. Response surface methodology (RSM) was implemented using Python-optimized (version 3.15) process parameters. The results demonstrate that a concentrate with 2.6% Co and 32.5% Mn can be obtained, achieving 82.1% Co recovery. Independent and multi-objective optimizations confirm MGS as a viable method for recovering Co and Mn from complex low-grade ores, with reduced overgrinding-related energy losses essential for production. The study aimed to implement and enhance low-grade asbolane ore from a feed containing 2.6% Co and 32.5% Mn. Variables were optimized with a multi-objective target, demonstrating their effectiveness.

The growing demand for Critical Raw Materials (CRMs) requires mining practices that align with sustainability and environmental, social, and governance (ESG) principles, while mining training increasingly benefits from advanced digital tools. Virtual Reality (VR) can provide high-resolution site representations that support both interactive learning and data-oriented analysis without operational risk. This study presents a VR-based framework for the quantitative assessment of pit lake rehabilitation using Virtual Excursions (VEs) developed from panoramic imagery and supported by machine-learning correction. High-resolution 360° panoramic images were used to extract geometric characteristics of a rehabilitated pit lake at the LARCO GMMSA Euboea mine site, Greece, including surface area, shoreline length, mean diameter, and maximum diameter. These image-derived estimates were validated against ground-truth data from field surveys and mine-closure documentation. To reduce systematic deviations associated with panoramic image measurements, a supervised multiple linear regression model was applied as a correction step. Validation based on Root Mean Square Error (RMSE) and the coefficient of determination ($R^2$) showed substantial improvement of the corrected estimates relative to the uncorrected image-based measurements. The results demonstrate that panoramic VR imagery can support site-specific quantitative environmental assessment in addition to its educational value. Although the present findings are limited to a single pit lake case study, the proposed workflow provides a structured basis for integrating immersive visualization, image-based measurement, and regression-based correction in post-mining rehabilitation assessment.