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Future Blog Post

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Blog Post number 4

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Blog Post number 3

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Blog Post number 2

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Blog Post number 1

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Portfolio item number 1

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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

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

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 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/

Conference Proceeding talk 3 on Relevant Topic in Your Field

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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.

bcdata - Polynomial regression and the kernel trick

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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.

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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

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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 experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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