A well-structured training programme has been conducted, involving (i) training though intersectoral individual research project and locally delivered training for each ESR as part of their PhD studies, and (ii) a series of network-wide training events. The latter comprised an induction program, Mineral Characterisation, HSC software and Geometallurgical training, in addition to Communication, Dissemination and Outreach, IPR, Entrepreneurship and Business Development and Leadership and Project Management training.
The main results achieved are:
ESR-1 has developed new grain segmentation methods for faster and more accurate identification of mineral grains by scanning-electron-microscope (SEM) based methodologies, specifically electron back scatter diffraction (EBSD).
ESR-2 has advanced the statistical analysis of multi-layer optical, major elemental and trace element information contained within petrographic thin sections and polished ore sections. This involved the adaptation of software developed in the digital histopathology field (e.g. for automated searches of cancer cells). To our knowledge, the co-analysis of reflected light image pixels and BSE and EDX maps and the superimposed machine-assisted learning of phase recognition is the first of its kind with huge potential for time savings in minerals characterisation.
ESR3 has developed tools to extract mineralogical and textural information from ore samples acquired using X-ray tomography. Moreover, with the mineralogical and textural information of the ore samples, a 3D liberation model has been built to predict particle population from the intact ore texture. Such a model would be very useful in process forecasting as the particles can be used in process simulation tools.
ESR4 has made a detailed mineralogical and textural characterization of a complex polymetallic ore body and developed a mineralogy-based geometallurgical methodology with the aim to better understand the deportment of target metals. This approach combines the advantages of different micro-analytical characterization tools to better track the metal-hosting minerals in the flotation process, especially in the presence of refractory or ‘invisible’ target metals.
ESR5 has created a dynamic process simulation model of lithium concentrator plant with grinding and flotation circuits. The dynamic component of the simulation makes the training more immersive, the operator being required to consider transient phases of the process when changing its settings. Moreover, this process simulation model is reusable for other purposes such as process design and scheduling, as well as for process operation optimization using model predictive control and process advisors.
Finally, ESR6 has developed a training evaluation for flotation simulator-based training. The training was evaluated using the first two levels of Kirkpatrick’s training evaluation model: Reaction, measuring the attendee's satisfaction, and Learning, evaluating the knowledge and skills retention. The reaction evaluation showed a high level of satisfaction from the part of the operators.
The results have been presented at multiple conferences and are at various stages of peer-reviewed publication. At the end of the project, 4 papers have been published, 1 is under review, 7 exist as full drafts and at least 5 more will be written in 2021.
