In the steelmaking industry, process control models need to be based on a sound physical understanding of the process but should also account for many uncertainties due to the nature and complexity of the environment in which the process is carried out. As a result, it is crucial to extract useful process control information from the raw data stream acquired by the industrial sensors. The proposed project aims at developing advanced algorithms to improve the estimation of key control parameters in the Argon-Oxygen Decarburization (AOD) process, by leveraging on Machine Learning approaches and tools applied to manufacturing data. This research, while being a valuable training for a high-talented student in Canada, will help the partner organization Tenova Goodfellow Inc. in maintaining its leadership in process optimization applied to steel making furnaces.
Tenova Goodfellow Inc
Engineering - computer / electrical
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