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Sequential optimal experiment design for an industrial polymerization process
This project aims to develop an accurate model of an industrial polymerization process using a minimal number of experiments. The approach combines basic physical understanding of the process with data-driven modeling techniques to efficiently capture process behavior. An adaptive experimental design strategy will be used, where each experiment is chosen based on results from previous ones to maximize learning while minimizing experimental effort. The resulting model will support process simulation, design, and optimization, helping improve efficiency in polymer production.
Faculty Supervisor:
Li Xi
Student:
Partner:
Zhejiang University
Discipline:
Engineering
Sector:
Education
University:
McMaster University
Program:
Globalink Research Award
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.