Periodic Reporting for period 2 - MetalIntelligence (European Industrial Doctorate in future efficient minerals analysis, processing and training)
Reporting period: 2018-09-01 to 2020-08-31
For the EU to become more resource-efficient is recognised as a major societal challenge. However, for the metals sector, the pathway from searching for mineral deposits to metal production is complex and inefficiencies exist due to the involvement of multiple sectors (geosciences, chemical analysts, mining, processing, metallurgy). The overall aim of MetalIntelligence was to disrupt this compartmentalisation, by training 6 Early Stage Researchers (ESR) through a multidisciplinary network of academic and industry leaders with a very broad range of expertise and skills. The issues sought to be resolved were reducing waste, improving efficiency, lowering energy consumption, increasing female participation and reducing operator error.
The three main objectives were:
1. Making characterization of raw materials faster and more industry-relevant for more efficient mineral processing
2. Re-writing aspects of the models that are used to predict the behavior of minerals in a processing plant, to increase yield, minimize waste and lower energy consumption
3. Improving the training for operators of very complex minerals processing plants.
The team comprised three beneficiaries (two academic, in Ireland and Sweden, and one Industry, Finland) as well as partners from the UK, Ireland, and Finland who provided secondment opportunities and/or industrial supervision. The programme was arranged into six work packages:
1. Management
2. Training
3. Improvement mineral analysis in preparation for more efficient processing
4. Advanced modeling of dynamic minerals processing
5. Modern, technology-enhanced training for future professionals
6. Dissemination
The main conclusion of the network is that right in this point in time, the minerals processing industry is in a state of rapid transformation brought about by improved analytical power, improved modelling and training approach. The network has contributed to all three and at its conclusion 50% of the ESRs have already found employment in this industry.
The three main objectives were:
1. Making characterization of raw materials faster and more industry-relevant for more efficient mineral processing
2. Re-writing aspects of the models that are used to predict the behavior of minerals in a processing plant, to increase yield, minimize waste and lower energy consumption
3. Improving the training for operators of very complex minerals processing plants.
The team comprised three beneficiaries (two academic, in Ireland and Sweden, and one Industry, Finland) as well as partners from the UK, Ireland, and Finland who provided secondment opportunities and/or industrial supervision. The programme was arranged into six work packages:
1. Management
2. Training
3. Improvement mineral analysis in preparation for more efficient processing
4. Advanced modeling of dynamic minerals processing
5. Modern, technology-enhanced training for future professionals
6. Dissemination
The main conclusion of the network is that right in this point in time, the minerals processing industry is in a state of rapid transformation brought about by improved analytical power, improved modelling and training approach. The network has contributed to all three and at its conclusion 50% of the ESRs have already found employment in this industry.
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.
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.
MetalIntelligence demonstrated significant originality and innovation in both its doctoral training program and the integrated and interdisciplinary research approach to mineral characterisation and processing. The network combined high-quality academic and industry knowledge with state-of-the-art facilities to directly contribute to the objectives of the Resource Efficient Flagship Initiative for Europe 2020 through the development of more efficient mineral characterisation methods, as well as quantification methods for economically relevant trace elements. In particular, the following results were achieved:
• Combined SEM-EDX and EBSD will be used for automated mineral liberation analysis, which informs modeling of more efficient mineral processing. This contributes to reducing waste and more sustainable use of resources.
• Inexpensive and fast reflected light microscope mapping will be integrated to mineral liberation analysis. Eventually, this will allow remote mineral processing plants to regularly characterize their ore feed and improve efficiency.
• High-resolution X-ray computed tomography will be used as enhanced tools for ore characterization to be used in predictive models for mineral processing which will integrate geology and metallurgy.
• The comportment of hidden elements, including many high-tech metals, will be better understood with the workflows produced by the network. This will improve recovery and help the EU to become more self-sufficient in these metals.
• Innovative mineralogy-based modules have already been written to improve the accuracy of models of mineral processing plants.
• Surrogate code will be written to allow the digital-twin concept to be more effectively adopted in the industry thanks to much lower demand on computing power.
• Technology enhanced learning tools for the virtual training of processing plant personnel have been developed to improve the plant operation in terms economic output, efficient use of raw materials and energy savings.
• Combined SEM-EDX and EBSD will be used for automated mineral liberation analysis, which informs modeling of more efficient mineral processing. This contributes to reducing waste and more sustainable use of resources.
• Inexpensive and fast reflected light microscope mapping will be integrated to mineral liberation analysis. Eventually, this will allow remote mineral processing plants to regularly characterize their ore feed and improve efficiency.
• High-resolution X-ray computed tomography will be used as enhanced tools for ore characterization to be used in predictive models for mineral processing which will integrate geology and metallurgy.
• The comportment of hidden elements, including many high-tech metals, will be better understood with the workflows produced by the network. This will improve recovery and help the EU to become more self-sufficient in these metals.
• Innovative mineralogy-based modules have already been written to improve the accuracy of models of mineral processing plants.
• Surrogate code will be written to allow the digital-twin concept to be more effectively adopted in the industry thanks to much lower demand on computing power.
• Technology enhanced learning tools for the virtual training of processing plant personnel have been developed to improve the plant operation in terms economic output, efficient use of raw materials and energy savings.