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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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Fall 2014
In this thesis, time-varying behaviour, nonlinearity and switching dynamics are generally treated as multi-modal behaviour. Two multi-model modelling techniques, i.e., the linear parameter varying (LPV) technique and the switched modelling technique, are investigated to model the multi-modal...
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Fall 2013
State inference and identification of discrete-time, non-linear, stochastic state-space models (SSMs) are considered here. A novel sequential Monte Carlo (SMC) based Bayesian method for simultaneous on-line state inference and identification of non-linear SSMs is proposed. Extension of the method...
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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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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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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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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 Gaussian Process Regression with a mixture of two Gaussian distributions as a noise model
DownloadSpring 2018
Increasingly many complex processes from the different fields of biological systems, engineering or econometrics are often required to be controlled. Hence, in such cases, we deal with identification of underlying complex processes which is essential for control design, optimization, and process...
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Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization
DownloadFall 2015
Availability of large amounts of industrial process data is allowing researchers to explore new data-based modelling methods. In this thesis, Gaussian process (GP) regression, a relatively new Bayesian approach to non-parametric data based modelling is investigated in detail. One of the primary...
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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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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...