Overview

This work covers a two-step multivariate statistical learning approach for batch process soft sensing. In this webinar, the authors apply different machine learning methods to reduce data dimensionality and to design soft sensors using personal care products as an example. They use existing techniques in a new way to give better visualisation and understanding.

Speaker

Dr Dongda Zhang, Lecturer in Process Systems Engineering and Machine Learning, University of Manchester

Dongda is a University Lecturer at the Department of Chemical Engineering, the University of Manchester and an Honorary Research Fellow at the Centre for Process Systems Engineering, Imperial College London. His research group focuses on Digital Chemical Engineering, with primary interests in chemical and biochemical Process Systems Engineering, Reaction Engineering, Machine Learning, and Industrial Data Analytics. He is currently an Associate Editor for Digital Chemical Engineering, a member of Editorial Board of Biochemical Engineering Journal, a member of BBSRC Pool of Experts for Bioprocess Systems Engineering, and a member of the Industrial Management Board, Centre for Process Analytics and Control Technology (CPACT).

The material presented in this webinar has not been peer-reviewed. Any opinions are the presenter's own and do not necessarily represent those of IChemE or the Process Management and Control Special Interest Group. The information is given in good faith but without any liability on the part of IChemE.

Time

09:00–10:00 BST.

Software

The presentation will be delivered via GoToWebinar®. Check system requirements.

You are advised to join the webinar at least ten minutes before the scheduled start time, to allow for your computer to connect.

Webinar archive

This webinar is free of charge and open to all to attend, but if you wish to access the slides and a recording to replay on demand then you will need to be a member of the Process Management and Control Special Interest Group.