Lee Rippon

Lee Rippon

Lee Rippon is a PhD student studying Chemical and Biologial Engineering (CHBE) at UBC. He also holds BASc and MASc degrees from UBC in CHBE where his research experience includes applications of compressive sensing, adaptive control, system identification and process monitoring on sheet and film processes. His current research interests include applying process analytics and machine learning techniques to historical process data to perform fault detection, isolation, and diagnosis in a kraft pulping process.



📚 Program/Degree
PhD, since 2017
📝 Research
Process Analytics and Machine Learning Techniques for the Kraft Pulping Process
👥 Co-supervisor(s)
📨 Contact
leeripp@chbe.ubc.ca
🔗 Website

DAIS Lab Publications

  1. Conference Proceedings
    Cross-directional controller performance monitoring for paper machines
    , , , , , , ,
    In proceedings of American Control Conference, Chicago, USA.
    pp. 4970 – 4975. 2015
  2. Conference Proceedings
    Noncausal modeling and closed-loop optimal input design for cross-directional processes of paper machines
    , , , , , , ,
    American Control Conference.
    pp. 2837-2842. 2017
    [PDF] [Slides]
  3. Patent
    Closed-loop model parameter identification techniques for industrial model-based process controllers
    , , , , , ,
    US Patent App. 15. 2018
  4. Conference Proceedings Invited Paper
    Pattern and Knowledge Extraction using Process Data Analytics: A Tutorial
    , , , , ,
    In Proceedings of ADCHEM Conference, Shengyang, China.
    pp. 13-18. 2018
    [Code]
  5. Journal Paper
    Data-Driven Dynamic Modeling and Online Monitoring for Multiphase and Multimode Batch Processes with Uneven Batch Durations
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    Industrial & Engineering Chemistry Research. 2019 [PDF]
  6. Journal Paper
    Machine Direction Adaptive Control on a Paper Machine
    , , , , ,
    Industrial & Engineering Chemistry Research. 2019 [PDF]
  7. Conference Proceedings
    Process Analytics And Machine Learning to Predict Arc Loss In An Electric Arc Furnace
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    In proceedings of the 59th Conference of Metallurgists. 2020 [PDF] [Video]
  8. Journal Paper
    Representation Learning and Predictive Classification: Application with an Electric Arc Furnace
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    Computers & Chemical Engineering. 2021 [PDF]

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