Diesel selective catalytic reduction emission prediction based on physical model data-driven and variational autoencoder-fully connected neural network-improved Bayesian algorithm (VAE-FCNN-IBO)

In order to accurately predict NOx and NH3 concentrations downstream of the diesel engine selective catalytic reduction (SCR) system and to improve computational efficiency, this paper constructs a diesel engine SCR model and combines a data-driven approach with the design of a fully connected neura...

Celý popis

Podrobná bibliografie

Vydáno v:	Energy (Oxford) Ročník 337; s. 138611
Hlavní autoři:	Wenlong liu, Gao, Ying, Zhu, Qi, You, Yuelin, Xia, Bocong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 15.11.2025
Témata:	Diesel engine Fully connected neural network Improved Bayesian algorithm Selective catalytic reduction Variational autoencoder Variational autoencoder Diesel engine Improved Bayesian algorithm Fully connected neural network Selective catalytic reduction
ISSN:	0360-5442
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!