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1 Ergebnisse
1
Predicting Spatiotemporal Distributions in a Bubbling Fluid..:
Hanbin Zhong
;
Xiaodong Yu
;
Juntao Zhang
...
doi:10.1021/acs.iecr.3c03812.s001. , 2024
Link:
https://doi.org/10.1021/acs.iecr.3c03812.s001
RT Journal T1
Predicting Spatiotemporal Distributions in a Bubbling Fluidized Bed for Biomass Fast Pyrolysis Using Convolutional Neural Networks
UL https://suche.suub.uni-bremen.de/peid=base-ftdeakinunifig:oai:figshare.com:article_25225478&Exemplar=1&LAN=DE A1 Hanbin Zhong A1 Xiaodong Yu A1 Juntao Zhang A1 Long Jiao A1 Ben Niu A1 Ruiyuan Tang A1 Xiaogang Shi A1 Vasilevich Sergey Vladimirovich A1 Qingang Xiong YR 2024 K1 Biophysics K1 Biotechnology K1 Computational Biology K1 Biological Sciences not elsewhere classified K1 Mathematical Sciences not elsewhere classified K1 Information Systems not elsewhere classified K1 still computationally costly K1 generate continuous outputs K1 convolutional neural networks K1 computational fluid dynamics K1 clarifying intrinsic characteristics K1 biomass fast pyrolysis K1 e K1 length K1 prediction step size K1 optimize bubbling fluidized K1 bubbling fluidized bed K1 predicting spatiotemporal distributions K1 developed model paves K1 bubbling fluidized K1 prediction length K1 spatiotemporal distributions K1 developed based K1 term prediction K1 spatiotemporal transport K1 averaged distributions K1 several orders K1 reaction behaviors K1 promising approaches K1 optimizing operations K1 model centered K1 magnitude increase K1 learning rate K1 deep learning K1 crucial technology K1 computation efficiency K1 carbon neutrality K1 1000 frames K1 10 frames JF doi:10.1021/acs.iecr.3c03812.s001 LK http://dx.doi.org/https://doi.org/10.1021/acs.iecr.3c03812.s001 DO https://doi.org/10.1021/acs.iecr.3c03812.s001 SF ELIB - SuUB Bremen
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