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- W3185509812 abstract "Abstract In the modern chemical world, engineers have access to millions of data points at their fingertips. Using this data properly can help us to recognize vast improvements in the way plants are run, especially in process monitoring. In this paper three aspects of process monitoring are considered: visualization, fault identification, and fault diagnosis. For each aspect, the use of modern machine learning techniques for addressing these issues is discussed, and improvements over outdated methods are illustrated. This proposed approach is tested using the Tennessee Eastman Process (TEP) for several types of faults." @default.
- W3185509812 created "2021-08-02" @default.
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- W3185509812 date "2021-01-01" @default.
- W3185509812 modified "2023-10-14" @default.
- W3185509812 title "Real-Time Chemical Process Monitoring with UMAP" @default.
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- W3185509812 doi "https://doi.org/10.1016/b978-0-323-88506-5.50321-1" @default.
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