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Skip to Search Results- 37Huang, 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 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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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...
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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 2019
Measurements in the process industry can arrive with fast or slow sampling rates. Fast measurements such as flowrate and temperature are sampled frequently and are obtained instantly after sampling. The slow measurements, which are usually related with chemical quality variables such as product...
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Spring 2019
In the oil sands extraction process, bitumen (crude oil) is separated from the sands in the Primary Separation Vessel (PSV) through a water-based gravity separation process. The interface between froth (crude oil) and middlings (water and sand) is the most important control variable in the PSV...
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Spring 2019
Predictive modeling has proven to be a valuable tool in process industry to estimate hard-to-measure variables that cannot be measured online. Those variables usually require LAB analysis to be quantified, which is time-consuming and costly. Predictive modeling can be used for both regression and...