A hybrid multiscale filter along with an improved adaptive SVR technique for fault diagnosis and machine learning modeling: forecasting the octane number of gasoline in isomerization reactor
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-08128-x.pdf
Reference65 articles.
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3. Bassam A, Conde-Gutierrez RA, Castillo J, Laredo G, Hernandez JA (2014) Direct neural network modeling for separation of linear and branched paraffins by adsorption process for gasoline octane number improvement. Fuel 124:158–167. https://doi.org/10.1016/J.FUEL.2014.01.080
4. Hernández-Pichardo ML, Macías-Salinas R (2016) Modeling the n-hexane isomerization over iron promoted Pt/WO x-ZrO2 catalysts using artificial neural networks. Ind Eng Chem Res 55:8883–8889. https://doi.org/10.1021/acs.iecr.6b01821
5. Yuceer M (2010) Artificial neural network models for HFCS isomerization process. Neural Comput Appl 19:979–986. https://doi.org/10.1007/s00521-010-0437-x
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