RESEARCH PROJECT TITLE
Machine learning driven monitoring of batch processes
SUPERVISOR
Moncef Chioua, Department of Chemical Engineering
AREAS OF EXPERTISE
Chemical engineering, Industry 4.0, Learning and inference theories, Multivariate analysis, Applied statistics, Modelling and simulation studies
DETAILED DESCRIPTION
Batch processes are commonly used in industries like pharmaceuticals, chemicals or semiconductors to produce drugs, polymers, specialty chemicals or other high-quality products. Large amounts of process data of different type are collected during batch operations and stored in process historians. Today, this valuable resource is not fully exploited because of the lack of dedicated tools and methods to extract reliable information from it. Detecting unintended deviations from normal operation or identifying the root cause of abnormal behavior becomes difficult with the ever-increasing amount and complexity of stored data.
Modern machine learning methods have the potential to support building robust and accurate batch process models able to answers process operators’ inquiries related to abnormal process situations and end product quality degradation.
I am looking for a PhD Candidate interested in working on the development of novel algorithms and computational tools to support the operation of batch process systems.
The work will be done in collaboration with industrial partners which will provide the selected candidate an opportunity to work on real-life case studies, to discuss with industrial practitioners and to benefit from the experience of industrial researchers in creating and deploying new technology.
Funding available, the student is also expected to contribute to funding applications.
The student must fulfill the conditions required for enrollment in graduate studies at Polytechnique Montreal in Chemical Engineering and have a sufficient level of English to carry out his/her research and write publications.
To apply, please send me an e-mail including a self-introduction, resume, transcripts and other supporting materials. Particular knowledge/expertise in one or more aspects of the project will be appreciated
