Book
Industrial Data Science for Batch Reactor Monitoring and Fault Detection
| العنوان: | Industrial Data Science for Batch Reactor Monitoring and Fault Detection |
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| المؤلفون: | Arzac, I. Imanol, Vallerio, Mattia, Perez-Galvan, Carlos, Navarro-Brull, Francisco J. |
| المصدر: | Machine Learning and Hybrid Modelling for Reaction Engineering ; page 358-403 ; ISBN 9781839165634 9781839165634 9781837670185 9781837670178 |
| بيانات النشر: | Royal Society of Chemistry |
| سنة النشر: | 2023 |
| الوصف: | Batch processes show several sources of variability, from raw materials’ properties to initial and evolving conditions that change during the different events in the manufacturing process. In this chapter, we will illustrate with an industrial example how to use machine learning to reduce this apparent excess of data while maintaining the relevant information for process engineers. Two common use cases will be presented: (1) AutoML analysis to quickly find correlations in batch process data and (2) trajectory analysis to monitor and identify anomalous batches leading to process control improvements. |
| نوع الوثيقة: | book part |
| اللغة: | English |
| ردمك: | 978-1-83916-563-4 978-1-83767-017-8 1-83916-563-4 1-83767-017-X |
| DOI: | 10.1039/bk9781837670178-00358 |
| الاتاحة: | http://dx.doi.org/10.1039/bk9781837670178-00358 https://books.rsc.org/books/edited-volume/chapter-pdf/1794076/bk9781837670178-00358.pdf |
| رقم الانضمام: | edsbas.EEDDA15B |
| قاعدة البيانات: | BASE |
| ردمك: | 9781839165634 9781837670178 1839165634 183767017X |
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| DOI: | 10.1039/bk9781837670178-00358 |