Lee Rippon

Lee Rippon

Interested in process control and artificial intelligence. Particularly, in ML algorithms for APC and FDD in process systems applications.

Posts by Collection

portfolio

publications

Moving-horizon predictive input design for closed-loop identification

Published in ADCHEM Whistler, IFAC-PapersOnLine, 2015

Designing optimal excitation signals for closed-loop system identification.

Recommended citation: Yousefi, M., Rippon, L. D., Forbes, M. G., Gopaluni, R. B., Loewen, P. D., Dumont, G. A., & Backstrom, J. (2015). "Moving-horizon predictive input design for closed-loop identification." IFAC-PapersOnLine. 48(8), 135-140. https://www.sciencedirect.com/science/article/pii/S240589631501037X

Cross-directional controller performance monitoring for paper machines

Published in American Control Conference (ACC) Chicago, IEEE, 2015

Development of a performance index that monitors a paper machine control system.

Recommended citation: Lu, Q., Rippon, L. D., Gopaluni, R. B., Forbes, M. G., Loewen, P. D., Backstrom, J., & Dumont, G. A. (2015). "Cross-directional controller performance monitoring for paper machines." American Control Conference (ACC). (pp. 4970-4975). IEEE. https://ieeexplore.ieee.org/abstract/document/7172113

Sheet profile estimation and machine direction adaptive control

Published in University of British Columbia, Vancouver, 2017

A comparative analysis of MD-CD separation strategies is presented along with a comprehensive adaptive control framework for paper machines.

Recommended citation: Rippon, L. D. (2017). "Sheet profile estimation and machine direction adaptive control." University of British Columbia, Vancouver. MASc dissertation. https://open.library.ubc.ca/cIRcle/collections/ubctheses/24/items/1.0347279

Noncausal modeling and closed-loop optimal input design for cross-directional processes of paper machines

Published in American Control Conference (ACC) Seattle, IEEE, 2017

Optimal inputs for closed-loop identification are designed with a noncausal transfer function model.

Recommended citation: Lu, Q.,Rippon, L. D., Gopaluni, R. B., Forbes, M. G., Loewen, P. D., Backström, J., & Dumont, G. A. (2017). "Noncausal modeling and closed-loop optimal input design for cross-directional processes of paper machines." American Control Conference (ACC). (pp. 2837-2842). IEEE. https://ieeexplore.ieee.org/abstract/document/7963381

Pattern and knowledge extraction using process data analytics: A tutorial

Published in ADCHEM Shenyang, IFAC-PapersOnLine, 2018

This tutorial was accompanied by a conference workshop at ADCHEM in China and together they were designed to familiarize control engineers with advances in statistical machine learning.

Recommended citation: Tsai, Y., Lu, Q., Rippon, L., Lim, S., Tulsyan, A., & Gopaluni, B. (2018). "Pattern and knowledge extraction using process data analytics: A tutorial." IFAC-PapersOnLine. 51(18), pp. 13-18. https://www.sciencedirect.com/science/article/pii/S240589631831913X

Data-driven dynamic modeling and online monitoring for multiphase and multimode batch processes with uneven batch durations

Published in Industrial & Engineering Chemistry Research, 2019

A modeling and monitoring strategy applied to a multiphase and multimode batch penicillin fermentation processes that involves linear dynamics, k-means clustering and expectation maximization.

Recommended citation: Wang, K., Rippon, L., Chen, J., Song, Z., & Gopaluni, R. B. (2019). "Data-driven dynamic modeling and online monitoring for multiphase and multimode batch processes with uneven batch durations." Industrial & Engineering Chemistry Research. 58(30), 13628-13641. https://pubs.acs.org/doi/abs/10.1021/acs.iecr.9b00290

Machine direction adaptive control on a paper machine

Published in Industrial & Engineering Chemistry Research, 2019

This work addresses the sheet profile estimation problem with a novel compressive sensing strategy and the adaptive control problem with a comprehensive monitoring, optimal input design and system identification strategy.

Recommended citation: Rippon, L. D., Lu, Q., Forbes, M. G., Gopaluni, R. B., Loewen, P. D., & Backström, J. U. (2019). "Machine direction adaptive control on a paper machine." Industrial & Engineering Chemistry Research. 58(26), 11452-11473. https://pubs.acs.org/doi/abs/10.1021/acs.iecr.8b06067

Process analytics and machine learning to predict arc losss in an electric arc furnace

Published in 59th Conference of Metallurgists, 2020

An inferential sensor is developed to warn operators of a high risk of impending arc loss so that they can take corrective actions and avoid the process fault.

Recommended citation: Rippon, L. D., Yousef, I., Hosseini, B., Beaulieu, J. F., Prevost, C., Shah, S. L., & Gopaluni, R. B. (2020). "Process analytics and machine learning to predict arc losss in an electric arc furnace." 59th Conference of Metallurgists 2020. https://dais.chbe.ubc.ca/assets/preprints/2020C5_Rippon_COM.pdf

Closed-loop model parameter identification techniques for industrial model-based process controllers

Published in Patent No.: US 10,761,522 B2, 2020

Patent granted: two-stage closed-loop identification strategy that leverages ARX and output-error models.

Recommended citation: Lu, Q., Rippon, L. D., Gopaluni, R. B., Forbes, M. G., Loewen, P. D., Backström, J., & Dumont, G. A. (2020). "Closed-loop model parameter identification techniques for industrial model-based process controllers." U.S. Patent. No.: US 10,761,522 B2. https://patentimages.storage.googleapis.com/f2/80/e4/202e0d00ddd3f5/US10761522.pdf

Representation Learning and Predictive Classification: Application with an Electric Arc Furnace

Published in Computers & Chemical Engineering, 2021

A comprehensive representation learning and predictive classification framework is presented for development of the inferential sensor from large quantities of historical industrial process data.

Recommended citation: Rippon, L. D., Yousef, I., Hosseini, B., Bouchoucha, A., Beaulieu, J. F., Prevost, C., Ruel, M., Shah, S. L., & Gopaluni, R. B. (2021). "Representation learning and predictive classification: Application with an electric arc furnace." Computers & Chemical Engineering. https://www.sciencedirect.com/science/article/abs/pii/S009813542100082X

talks

bcdata - Polynomial regression and the kernel trick

Published:

As a co-organizer of the 2017 bcdata Data Science Workshop part of my duties included providing a presentation (a Jupyter notebook) in the first week of the workshop on least-squares, ridge, polynomial and kernel regression methods. Another duty involved moderating the career panel discussion. Finally, the second week of the workshop involved group projects where we ultimately presented on data insights from vehicle time series messages, a project supported by moj.io.

ADCHEM 2018 Shenyang - Advanced Tools for Data Analytics

Published:

As an organizer for this workshop myself and a fellow graduate student were responsible for developing and delivering the majority of the workshop material including presentation slides and interactive case studies delivered as Jupyter notebooks. Material that I was responsible for developing and presenting included classification algorithms, regression techniques, dimensionality manipulation methods and advanced learning algorithms.

AIChE Spring Meeting - Advanced Tools for Process Data Analytics

Published:

Process industries have been using data analytics in various forms for more than three decades. In particular, statistical techniques such as principal component analysis (PCA), partial least squares (PLS) and canonical variate analysis (CVA) have been used widely. This workshop introduces the essential machine learning algorithms and software tools for graduate students, experienced researchers and engineers working in the industry. In particular, several known and emerging applications of these algorithms in soft sensing, state and parameter estimation, process monitoring, fault detection and diagnosis, and control will be presented.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.