Publications

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

Published in Conference of Metallurgists - Under review, 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." Conference of Metallurgists. Under review. https://com.metsoc.org/

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

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

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

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

Published in U.S. Patent Application No. 15/636, 419, 2018

Patent pending 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. (2018). "Closed-loop model parameter identification techniques for industrial model-based process controllers." U.S. Patent Application. No. 15/636, 419. https://patents.google.com/patent/US20180081348A1/en

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

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

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

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