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Rippon, Lee Dale

University of British Columbia

The optimization of legacy industrial processes is critical for the economic viability of many rural communities in Canada. Automation and advanced process control is of paramount importance for many large-scale industrial processes to maintain viability in a constricting regulatory environment that is increasingly competitive economically. However, with legacy industrial processes comes a rich history of automation, including large quantities of underappreciated process data. This dissertation is about leveraging existing historical data with machine learning and process analytics to generate novel data-driven solutions to outstanding process faults. An application-driven approach provides insights into the full stack of considerations including identifying and framing data-driven opportunities for control of complex industrial processes, acquiring the necessary resources, preparing the data, developing and evaluating methods, and deploying sustainable solutions. Contributions are made to help address highly troublesome faults in two distinct industrial processes. The first industrial case study involves mitigating the impact of unexpected loss of plasma arc in an electric arc furnace that is key to a 60,000 tonne/year pyrometallurgy operation. A convolutional neural network classifier is trained to learn a representation from the operating data that enables prediction of the arc loss events. The operating data and problem formulation are published as a novel benchmark challenge to address observed shortcomings with existing fault detection benchmark literature. The second industrial case study involves advanced monitoring of a rotary lime kiln in a 152,000 tonne/year kraft pulp mill to mitigate faults such as ring formation and refractory wear. A novel shell temperature visualization strategy is published that enables improved monitoring and empowers researchers and industry professionals to obtain value from thermal camera data. Various approaches are studied for monitoring ring formation. Aberrations in shell temperatures led to the discovery of a novel phenomenon known as rotational aliasing that has important implications for measurement and analysis of shell temperature data. Finally, inferential sensing of residual calcium carbonate content is studied to help optimize specific energy and reduce emissions.

Text

2023-10-05

Attribution 4.0 International

http://hdl.handle.net/2429/86076

Chemical and Biological Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by/4.0/