The main advantage of X-ray microcomputed tomography (µCT) as a non-destructive imaging tool lies in its ability to analyze the three-dimensional (3D) interior of a sample, therefore eliminating the stereological error exhibited in conventional two-dimensional (2D) image analysis. Coupled with the correct data analysis methods, µCT allows extraction of textural and mineralogical information from ore samples. This study provides a comprehensive overview on the available and potentially useful data analysis methods for processing 3D datasets acquired with laboratory µCT systems. Our study indicates that there is a rapid development of new techniques and algorithms capable of processing µCT datasets, but application of such techniques is often sample-specific. Several methods that have been successfully implemented for other similar materials (soils, aggregates, rocks) were also found to have the potential to be applied in mineral characterization. The main challenge in establishing a µCT system as a mineral characterization tool lies in the computational expenses of processing the large 3D dataset. Additionally, since most of the µCT dataset is based on the attenuation of the minerals, the presence of minerals with similar attenuations limits the capability of µCT in mineral segmentation. Further development on the data processing workflow is needed to accelerate the breakthrough of µCT as an analytical tool in mineral characterization.

Industrial activities, such as mining, electroplating, cement production, and metallurgical operations, as well as manufacturing of plastics, fertilizers, pesticides, batteries, dyes or anticorrosive agents, can cause metal contamination in the surrounding environment. This is an acute problem due to the non-biodegradable nature of metal pollutants, their transformation into toxic and carcinogenic compounds, and bioaccumulation through the food chain. At the same time, platinum group metals and rare earth elements are of strong economic interest and their recovery is incentivized. Microbial interaction with metals or metals-bearing minerals can facilitate metals recovery in the form of nanoparticles. Metal nanoparticles are gaining increasing attention due to their unique characteristics and application as antimicrobial and antibiofilm agents, biocatalysts, in targeted drug delivery, for wastewater treatment, and in water electrolysis. Ideally, metal nanoparticles should be homogenous in shape and size, and not toxic to humans or the environment. Microbial synthesis of nanoparticles represents a safe, and environmentally friendly alternative to chemical and physical methods. In this review article, we mainly focus on metal and metal salts nanoparticles synthesized by various microorganisms, such as bacteria, fungi, microalgae, and yeasts, as well as their advantages in biomedical, health, and environmental applications.

While particle combustion and chalcopyrite oxidation in suspension smelting is well understood, few studies are available regarding the melt-melt reactions and the separation between copper matte and slag in matte smelting. In the present work, experimental investigations in air and argon atmosphere were conducted using a mixture of synthetic slag and chalcopyrite concentrate. The sequential reaction and separation processes occurring in matte smelting are outlined. Possible limiting factors in the overall process are also proposed. The result of the present work forms an important foundation for future work in the kinetic rate formulation of molten phase reactions between copper matte and slag in matte smelting.

Texture is one of the critical parameters that affect the process behavior of ore minerals. Traditionally, texture has been described qualitatively, but recent works have shown the possibility to quantify mineral textures with the help of computer vision and digital image analysis. Most of these studies utilized 2D computer vision to evaluate mineral textures, which is limited by stereological error. On the other hand, the rapid development of X-ray microcomputed tomography (µCT) has opened up new possibilities for 3D texture analysis of ore samples. This study extends some of the 2D texture analysis methods, such as association indicator matrix (AIM) and local binary pattern (LBP) into 3D to get quantitative textural descriptors of drill core samples. The sensitivity of the methods to textural differences between drill cores is evaluated by classifying the drill cores into three textural classes using methods of machine learning classification, such as support vector machines and random forest. The study suggested that both AIM and LBP textural descriptors could be used for drill core classification with overall classification accuracy of 84–88%.

Advancements in modern mineral processing has been driven by technology and fuelled by market economics of supply and demand. Over the last three decades, the demand for various minerals has steadily increased, while the mineral processing industry has seen an unavoidable increase in the treatment of complex ores, continuous decline in plant feed grade and poor plant performance partly due to blending of ores with dissimilar properties. Despite these challenges, production plant data that are routinely generated are usually underutilised. In this contribution and aligned with the direction of the 4th industrial revolution, we highlight the value of legacy metallurgical plant data and the concept of a dry laboratory approach. This study is presented in two parts. In the current paper (Part I), a comprehensive review of the potential for the combination of modern analytical technology with data analytics to generate a new competence for process optimisation are provided. To demonstrate the value of data within the extractive metallurgy discipline, we employ data analytics and simulation to examine gold plant performance and the flotation process in two separate case studies in the second paper (Part II). This was done with the aim of showcasing relevant plant data insights, and extract parameters that should be targeted for plant design and performance optimisation. We identify several promising technologies that integrate well with existing mineral processing plants and testing laboratories to exploit the concept of a dry laboratory, in order to enhance pre-existing mineral processing chains. It also sets the passage in terms of the value of innovative analysis of existing and simulation data as part of the new world of data analytics. Using data- and technology-driven initiatives, we propose the establishment of dry laboratories and data banks to ultimately leverage integrated data, analytics and process simulation for effective plant design and improved performance.