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Skip to Search Results- 40Huang, Biao (Chemical and Materials Engineering)
- 5Forbes, Fraser (Chemical and Materials Engineering)
- 2Li, Zukui (Chemical and Materials Engineering)
- 2Prasad, Vinay (Chemical and Materials Engineering)
- 1Afacan, Artin (Chemical and Materials Engineering)
- 1Forbes, J.Fraser (Chemical and Materials Engineering)
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Spring 2024
The advent of Industry 4.0 integrates advanced digital technologies and Artificial Intelligence (AI) into system engineering. This research explores the potential of AI in smart automation for industries, bridging it with physics-informed approaches, particularly through Explainable Artificial...
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Spring 2024
In the bitumen extraction process, precise control of the froth-middling interface in the Primary Separation Cell (PSC) is critical for maximizing bitumen recovery. Traditional methods for monitoring this interface suffer from reliability issues due to sensor clogging and challenging process...
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Spring 2024
In the process industry, certain quality variables cannot be measured regularly due to technical limitations or economic constraints. Consequently, the industry relies on laboratory analysis to measure such quality variables. However, laboratory analysis introduces long time-delays in obtaining...
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Spring 2023
Process industries involve processes that have complex, interdependent, and sometimes uncontrollable/unobservable features that are subject to a variety of uncertainties such as operational fluctuations, sensory noises, process anomalies, human involvement, market volatility, and so forth. In the...
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Robust Generalized Weighted Probabilistic Principal Component Regression with Application in Data-driven Optimization
DownloadSpring 2022
The operations of the plant may deviate from the initial design due to the uncertainties and changes in the several conditions as a result of market demand, operation conditions, and safety regulations over time. To maintain productivity, safety, and efficiency, operators should ensure the...
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Robust Probabilistic Slow Feature Analysis for Soft Sensor Development and Model Quality Assessment
DownloadFall 2022
Model predictive control (MPC) is a popular advanced control technology. Unfortunately, over time the behaviour of the plant may deviate from its initial design conditions resulting in model-plant-mismatch. The detection and diagnosis of such mismatches is an important task to ensure that MPC...
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Modelling process dynamics by the discovery of partial differential equations using a data-driven and hybrid modelling approach
DownloadFall 2021
The abundance of data and advances in data acquisition technologies have made data-driven approaches attractive to solve a multitude of problems. Differential equations deliver underlying models for most physical processes. Obtaining the fundamental physics underlying any data in the form of...
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Spring 2020
Machine learning (ML) has shown great potential to create tremendous value and growth to all sectors around the world, enhancing productivity, health, and longevity of humanity. ML differentiates itself from all previous methods through its adaptive and self-learning capabilities. In recent...
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Spring 2020
Reinforcement learning (RL) has received wide attention in various fields lately. Model-free RL brings data-driven solutions that learn the control strategy directly from interaction with process data without the need for a process model. This is especially beneficial in the case of nonlinear...
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Fall 2020
Data-driven modeling approaches have been widely studied and applied to the process industries for inferential sensor development, process monitoring and fault detection and early warnings, etc. Essential information of process, like dynamic and relationships between process variables are buried...