Machine learning approach to monitoring and decarbonizing mineral processes

In Canada’s 2030 Agenda for Sustainable Development, one of the goals is to take urgent action to combat climatechange and its impacts. According to this agenda, the target is to reduce Canada’s total greenhouse gas (GHG)emissions by 30%. In order to transition to renewable energy, solar and wind power in combination with energystorage such as lithium-ion batteries can be used to replace fossil fuels. Although this is approach has a greatpotential in decreasing the carbon emissions in the long-term, it is also important to minimize the environmentaleffects of the production of these batteries. The proposed research aims to reduce carbon emissions in mineralprocessing activities using machine learning. An adaptable machine learning model will be developed to minimizefuel consumption by processing plants. This will allow increasing the production of required minerals for lithiumionbatteries in Canada while meeting the GHG reduction targets.

Intern: 
Ekin Tureoglu
Faculty Supervisor: 
Yuksel Asli Sari
Province: 
Ontario
University: 
Partner University: 
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