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Skip to Search Results- 1Huang, Biao (Chemical and Materials Engineering)
- 1Lefsrud, Lianne (Chemical and Materials Engineering)
- 1Li, Zukui (Chemical and Materials Engineering)
- 1Li, Zukui (Department of Chemical and Materials Engineering)
- 1Liu, Jinfeng (Chemical and Materials Engineering)
- 1Macciotta, Renato (Civil and Environmental Engineering)
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Analyzing the risks associated with railway transportation of hazardous materials and developing process models for railway incidents with high potential for release using machine learning and data analytics
DownloadSpring 2023
The Canadian economy relies heavily on its transportation network. It supports hundreds of thousands of jobs, contributes billions to the economy, and facilitates the movement of goods within the country as well as internationally. Railways provide affordable and efficient transportation to over...
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Fall 2023
With the advances made in machine learning and data science, data-driven modeling and optimization techniques have garnered significant attention in recent years. However, despite the availability of various data-driven methods for addressing optimization problems under uncertainty, their...
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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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Machine learning-based design and techno-economic assessments of adsorption processes for CO2 capture
DownloadFall 2021
Cyclic adsorption processes are widely considered for various industrial gas separations, including CO2 capture. The flexibility to configure a variety of process cycles is an attractive process design feature of these processes. Despite such flexibility for process design, computationally...