Prediction of particle agglomeration during nanocolloid drying using machine learning and reduced-order modeling
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Published:2024-07
Issue:
Volume:294
Page:120097
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ISSN:0009-2509
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Container-title:Chemical Engineering Science
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language:en
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Short-container-title:Chemical Engineering Science
Author:
Kameya KyokoORCID,
Ogata Hiroyuki,
Sakoda Kentaro,
Takeda Masahiro,
Kameya YukiORCID
Reference60 articles.
1. Mass transfer rate in gas-liquid Taylor flow: Sherwood numbers from numerical simulations;Albrand;Chem. Eng. Sci.,2023
2. Aravamuthan, S., Kangde, S., 2023. SAE Technical Paper 2023-01-0155, 2023 Prediction of Buckling and Maximum Displacement of Hood Oilcanning Using Machine Learning. SAE Technical Paper Series. https://doi.org/10.4271/2023-01-0155.
3. Bazaz, M.A., Mashuq-un-Nabi, Janardhanan, S., 2012. A Review of Parametric Model Order Reduction Techniques. IEEE International Conference on Signal Processing, Computing and Control., Solan, India, 2012, pp. 1–6. https://doi.org/10.1109/ISPCC.2012.6224356.
4. A review on functionally gradient materials (fgms) and their applications;Bhavar;IOP Conf. s. Mater. Sci. Eng.,2017
5. Radial Basis Functions;Buhmann,2003