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The 2015 Paris agreement on climate change has grown from an original 55 countries to 191 countries who have ratified the agreement1 which aims to limit global warming to below 2 degrees and preferably to 1.5 degrees Celsius. As world leaders commit to this agreement, industry too is stepping up to its responsibility to reduce emissions.
The mining industry is directly responsible for 4% to 7% of greenhouse-gas emissions globally, and indirectly to approximately 28% of emissions.2 Reducing emissions is a complex task, one that is different for each mining operation. Mining companies can look to operational efficiencies, recycling, renewables, electrification of equipment, and the diversification of mining portfolios, such as the reduction of coal mining and the inclusion of commodities that can be used in the creation of low-carbon technologies.
With all of these strategies at play, understanding the absolute carbon footprint created by each mine site becomes a complex calculation. The ability to monitor carbon emissions at a local level would assist day to day operations in reducing emissions, as well as corporate strategic planning, reporting, carbon accounting and compliance.
The release of greenhouse-gas emissions varies based on the commodity being mined, the process used, surface proximity and quality. For example, the extraction of lower grade minerals typically consumes larger amounts of energy, and mine sites that are heavily reliant on renewable energy may have fluctuating CO2 emissions as the weather changes and they require off-the-grid or carbon energy sources.
Through the application of artificial intelligence to existing data, mining operators are able to obtain valuable insights to help them reduce emissions as well as overhead operational costs. As investors demand greater transparency on climate change risk and initiatives, it is increasingly important for mine operators to consistently collect, analyse and learn how their initiatives are working, and monitor and plan for reduced emissions.
Discover how AI can support Energy Management Systems with automated analysis for continual energy performance improvementRead Article
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The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even.
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