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.

The influence of the physical, geometric and chemical properties of particles on the results of aggregate separation by means of a laboratory ring jig is presented in this paper. The experiment was based on separation of chalcedonite particles in a narrow particle size fraction composed separately of regular and irregular particles, which was prepared in accordance with patent inventions. On its basis, the geometric properties—projection diameter and (volumetric and dynamic) shape coefficients—as well as physical properties—particle density—were determined in products of the regular and irregular particles. The terminal settling velocities of the regular and irregular particles were calculated for a randomly selected sample of particles in each obtained separation product. The statistical analysis of the geometric properties of the particles allowed to evaluate the influence of these parameters on aggregate processing with respect to selection of particles homogenous in terms of their shapes. The comparison of the particle shapes’ influence on the chalcedonite feed separation effects was made by the means of the values of the shape coefficients: the dynamic and volumetric ones. Additionally, tests were carried out using Raman spectroscopy in order to determine the mechanisms of density change in the aggregate. The research goal was realised through detecting and analysing the polymorphic forms of the silica and allogenic minerals precipitated on the surface and inside the chalcedonite particles.