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 climate
change 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 energy
storage such as lithium-ion batteries can be used to replace fossil fuels. Although this is approach has a great
potential in decreasing the carbon emissions in the long-term, it is also important to minimize the environmental
effects of the production of these batteries. The proposed research aims to reduce carbon emissions in mineral
processing activities using machine learning. An adaptable machine learning model will be developed to minimize
fuel consumption by processing plants. This will allow increasing the production of required minerals for lithiumion
batteries in Canada while meeting the GHG reduction targets.

Faculty Supervisor:

Yuksel Asli Sari

Student:

Partner:

Emily Thorn Corthay Inc.

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Queen's University

Program: