Simon Michaux

The successful implementation of geometallurgy largely depends on the continuous collection of high-quality, multi-scale, multi-dimensional quantitative data on the geology, geochemistry, mineralogy, texture, and physical properties of an orebody. This can then be used to build and improve, amongst other things, ore deposit models, comminution strategies, waste management, and downstream mineral processing and extractive metallurgy routes. The present contribution provides a brief overview of the key types of data collected, and analytical techniques used, in geometallurgical programs. It also highlights important developments that are currently underway, which may generate a significant impact in the near future.

The technologies used in mineral process engineering are evolving. The digital mineral processing solutions are based on advances in our ability to instrumentally measure phenomena at several stages of the beneficiation circuit, manage the data in real-time, and to analyze these data using machine learning to develop the next generation of process control. The main purpose of this study is to overview various digital solutions for mineral processing plants and characterization laboratories while emphasizing their utilization in the current state of the digitization process of the GTK Mintec. This study highlights the specialized digital technologies that are particularly relevant for mineral processing and beneficiation. The digital solutions studied in this article include digital twin, machine vision, information management system, sensors, smart equipment, machine learning techniques, process control system, robotic cell, and Internet of Things applied across the whole chain of studying materials from the mineralogical examinations through the bench-scale studies to the pilot test trials. The aim is to provide a clear view on the different aspects of digitizing mineral processing plants based upon the lessons learned from the development plans in GTK Mintec.

Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area.

Recent advances in concrete recycling technology focus on novel fragmentation techniques to obtain aggregate fractions with low cement matrix content. This study assesses the aggregate liberation effectiveness of four different treatment processes including standard and innovative concrete fragmentation techniques. Lab-made concrete samples were subjected to either standard mechanical crushing technique (SMT) or electrodynamic fragmentation (EDF). For both fragmentation processes, the influence of a microwave weakening pre-treatment technique (MWT) was investigated. A detailed analysis of the particle size distribution was carried out on samples after fragmentation. The >5.6 mm fraction was more deeply characterized for aggregate selective liberation (manual classification to separate liberated aggregates) and for cement matrix content (thermogravimetric measurements). Results highlight that EDF treatment is more effective than SMT treatment to selectively liberate aggregates and to decrease the cement matrix content of the >5.6 mm fraction. EDF fully liberates up to 37 wt.% of the >5.6 mm natural aggregates, while SMT only liberates 14–16 wt.%. MWT pre-treatment positively affects aggregate liberation and cement matrix removal only if used in combination with SMT; no significant effect in combination with EDF was recorded. These results of this study can provide insights to successfully implement innovative technology in concrete recycling plants.