Abstract
Life cycle assessment (LCA) methods are increasingly being suggested for monitoring environmental performances in the mining sector. These methods provide comprehensive insights into the environmental impacts of mining activities, helping to identify areas for improvement and drive sustainable practices.
The DINAMINE project, supported by European Union´s Horizon Europe research and innovation programme (Grant Agreement No. 101091541), aims to improve the mining industry through the integration of digital technologies such as robotics, artificial intelligence, and automation. These technologies will enhance the efficiency and sustainability of small and medium-sized mines, supported by a comprehensive mine management system known as the Integrated Smart Mine Planning and Managing (ISMP) system.
The ISMP system is central to the project, providing a state-of-the-art platform for data collection, management, and visualization. It includes modules like the Mine Information Model, Tailing Information Model, and Mineral Processing Model, among others. The system visualizes important mining parameters and employs a traffic light system for simplifying day-to-day operations.
A significant aspect of this project involves collaboration with mine operators and project participants to determine the most relevant sensors for real-time measurement. Gamification elements are incorporated to engage plant operators, allowing them to compete on various LCIA metrics. Additionally, Exiobase-provided indicators are used to integrate social aspects into the assessments.
The methodology developed employs a hybrid input-output life cycle assessment (I-O LCA) framework, integrated with EXIOBASE data and life cycle impact assessment (LCIA) methodological choices. The MARIO Python package is used to manipulate the background database such as aggregation of sectors or geographical locations. By combining process-based LCA databases for assets acquired prior to the years covered by EXIOBASE, a dynamic assessment that integrates both process-based and environmentally extended input-output (EEIO) databases is achieved. This approach addresses the challenge of discounting global warming potential values for assets produced in different years than the process-related emissions.
This dual approach allows for real-time identification of environmental impact hotspots, enabling immediate and targeted interventions. The hybrid I-O LCA framework, in conjunction with the MARIO Python package and EXIOBASE data, demonstrates faster response times compared to traditional LCA reporting systems, allowing for more timely decision-making and adaptive management practices within the mining sector.
While the methodology presents significant advancements, challenges remain. These include integrating process-based and EEIO databases, managing data resolution discrepancies, and addressing skewed results caused by irregular use and early retirement of assets. Despite these challenges, the potential for real-time, dynamic environmental assessments offers a promising avenue for enhancing the sustainability and efficiency of mining operations. The insights gained from this research provide a foundation for further refinement and adaptation of LCA methodologies in the mining sector and beyond.
The DINAMINE project, supported by European Union´s Horizon Europe research and innovation programme (Grant Agreement No. 101091541), aims to improve the mining industry through the integration of digital technologies such as robotics, artificial intelligence, and automation. These technologies will enhance the efficiency and sustainability of small and medium-sized mines, supported by a comprehensive mine management system known as the Integrated Smart Mine Planning and Managing (ISMP) system.
The ISMP system is central to the project, providing a state-of-the-art platform for data collection, management, and visualization. It includes modules like the Mine Information Model, Tailing Information Model, and Mineral Processing Model, among others. The system visualizes important mining parameters and employs a traffic light system for simplifying day-to-day operations.
A significant aspect of this project involves collaboration with mine operators and project participants to determine the most relevant sensors for real-time measurement. Gamification elements are incorporated to engage plant operators, allowing them to compete on various LCIA metrics. Additionally, Exiobase-provided indicators are used to integrate social aspects into the assessments.
The methodology developed employs a hybrid input-output life cycle assessment (I-O LCA) framework, integrated with EXIOBASE data and life cycle impact assessment (LCIA) methodological choices. The MARIO Python package is used to manipulate the background database such as aggregation of sectors or geographical locations. By combining process-based LCA databases for assets acquired prior to the years covered by EXIOBASE, a dynamic assessment that integrates both process-based and environmentally extended input-output (EEIO) databases is achieved. This approach addresses the challenge of discounting global warming potential values for assets produced in different years than the process-related emissions.
This dual approach allows for real-time identification of environmental impact hotspots, enabling immediate and targeted interventions. The hybrid I-O LCA framework, in conjunction with the MARIO Python package and EXIOBASE data, demonstrates faster response times compared to traditional LCA reporting systems, allowing for more timely decision-making and adaptive management practices within the mining sector.
While the methodology presents significant advancements, challenges remain. These include integrating process-based and EEIO databases, managing data resolution discrepancies, and addressing skewed results caused by irregular use and early retirement of assets. Despite these challenges, the potential for real-time, dynamic environmental assessments offers a promising avenue for enhancing the sustainability and efficiency of mining operations. The insights gained from this research provide a foundation for further refinement and adaptation of LCA methodologies in the mining sector and beyond.
