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- 2Primary Separation Vessel
- 2Process Control
- 1Additive Manufacturing
- 1Car-Parrinello Molecular Dynamics
- 1Computational Chemistry
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Fall 2019
Data is becoming more valuable as there are still many uncertainties and hidden information that have yet to be discovered. For this reason, the application of data analysis and machine learning in the industry is becoming more popular. For example, SAGD (steam assisted gravity drainage) is a...
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Investigations of interactions between membrane and PEI/siRNA nanoparticles and their implication for non-viral gene delivery
DownloadSpring 2021
Cell entry of polynucleotide-based therapeutic agents can be promoted by nanoparticle (NP) mediated delivery. This dissertation investigates membrane penetration of polynucleotide NPs, using mainly computational approaches, accompanied by some experiments. Major emphasis was placed on...
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Spring 2024
Despite recent advancements in molecular dynamics (MD) methods, the computational costs of \emph{ab initio} molecular dynamics simulations for explicit solvation systems are still too significant. If accuracy is to be left uncompromised, new methods must be employed to reduce computational...
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Machine Learning for Robust Tracking of Interface Level Inside a Primary Separation Vessel in the Presence of Occlusions and Noise
DownloadSpring 2021
A Primary Separation Vessel (PSV), used in the oil sands industry, is an important process equipment, where Bitumen is separated from the oil sand using a density based separation process. The interface level between a bitumen rich layer (froth) and a layer that has moderate amounts of bitumen in...
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Microstructural Analysis of Ni-WC Metal Matrix Composites Deposited Using Plasma Transferred Arc Additive Manufacturing
DownloadFall 2023
The objective of this thesis is to examine how the composition and solidification rate influences the thermal degradation of WC and the resulting microstructure of WC-Ni metal matrix composites (MMCs) deposited using plasma transferred arc additive manufacturing (PTA-AM). Scanning electron...
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Monitoring of Industrial Processes via Non-stationary Probabilistic Slow Feature Analysis Machine Learning Algorithm
DownloadSpring 2020
This research develops a first of its kind machine learning (ML) algorithm, called probabilistic slow feature analysis (PSFA), that monitors and detects faults for non-stationary industrial processes. The novelty of this ML algorithm is that it can monitor and detect faults for non-stationary...
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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...