Tailings affected by neutral mine drainage (NMD) have received comparatively little attention despite their potential to release trace elements, including rare earth elements (REE) and sulphate to downstream hydrological systems. This study presents a comprehensive geochemical characterisation of apatite iron ore (AIO) tailings from Kiirunavaara mine in northern Sweden, providing baseline geochemical and mineralogical data needed to evaluate the long-term stability of REE and other elements of potential concern in NMD conditions. Three vertical cores (KI_01, KI_02, and KI_03; up to 10 m deep) and groundwater samples were examined. The tailings were dominated by silicate minerals (ca. 74 wt
ABSTRACT Integrated earth modelling aims to combine all available geoscientific information to derive one common earth model. However, combining multiple parameters in a meaningful way remains challenging. In this study, we aim to improve mineral exploration workflows through the integration of new magnetotelluric data with other geophysical and petrophysical data to enhance local geological understanding of the northern Kiruna mining district, Norrbotten, Sweden. The area is economically important and geoscientifically interesting. We derived three‐dimensional (3D) geophysical models based on new magnetotelluric data and previously collected gravity and magnetic data. The magnetotelluric data and the resulting 3D electrical‐resistivity model are described in detail. Additionally, a new petrophysical dataset containing surface and subsurface samples and their density, electrical resistivity and magnetic susceptibility is presented and linked to the 3D geophysical models. The geophysical 3D models are interpreted using information gained from the measured rock properties, allowing for correlation and cross‐validation between models and geology. Integration of a clustering approach into the mineral exploration workflow allows for a holistic interpretation of all information available in the area. These interpretations and models improve understanding of the Per Geijer mineral system, its immediate surroundings and the relationships between local rock types and their geophysical and mineralogical signatures.
Belt conveyor systems are widely used in industrial settings for bulk material transport, valued for their high capacity and minimal reliance on manual intervention. However, foreign objects on the conveyor can pose significant risks to the system’s operation and downstream equipment. Given the critical role these systems play in maintaining production efficiency, along with their high level of automation and limited opportunities for manual inspection, there is a clear need for continuous, fully automated monitoring, which this paper aims to address. A multi-step solution has been developed which utilizes video stream data from a camera positioned above a conveyor belt. Frames are pre-processed to determine whether the belt is operating, and the edges of the bulk material are detected to establish a region of interest (ROI) for object detection. Each frame is then passed through an object detection pipeline, incorporating several image processing techniques. Edge detection of the ore to establish an ROI is occasionally inconsistent, leading to false detections at the ore edges due to dark thresholding. To mitigate these false detections, a convolutional neural network (CNN) with pre-trained weights from the MobileNetV2 model was implemented as the final step in the object detection process. This object detection system is deployed on an edge device at LKAB Narvik and connected to a SaaS platform that provides predictive maintenance and decision support. The system offers direct insights into whether operations should be halted based on the size and composition of detected objects. Results show a significant reduction in false detections, particularly at the ore edges, and the combination of light and dark thresholding allows for the detection of both high- and low-intensity objects.
Coal replacement with hydrogen is a strategy for reducing carbon emissions from high-temperature industrial processes. Hydrogen lancing is a direct way for introducing hydrogen to existing coal-fired kilns. This work investigates the effects of hydrogen lancing on nitrogen oxides (NOx) emissions and ignition behaviour in a pilotscale furnace that employs a 30 % coal replacement with hydrogen lancing. The investigation encompasses the impacts of lancing distance, angling, and velocity. Advanced measurement techniques, including spectrometry and monochromatic digital cameras, characterise the flame and assess emissions. The results indicate that the 30 % coal replacement by hydrogen lancing enhances combustion and reduces the emissions of carbon monoxides (CO). The flame characteristics vary with the location of the hydrogen injection, generally becoming more-intense than during coal combustion. NOx emissions during lancing are similar or up to double the emissions observed for pure coal combustion, depending on the lancing configuration. Increasing the distance between the hydrogen lance and coal burner increases NOx emissions.
