Dynamic optimisation of CO2 electrochemical reduction processes driven by intermittent renewable energy: Hybrid deep learning approach

Journal article

Peer reviewed

Xin Yee Tai, Lei Xing, Yue Zhang, Qian Fu, Oliver Fisher, Steve D.R. Christie and Jin Xuan

Digital Chemical Engineering, Vol.9, p.100123

12/2023

Metrics

20 Record Views

Title: Dynamic optimisation of CO2 electrochemical reduction processes driven by intermittent renewable energy: Hybrid deep learning approach
Creators: Xin Yee Tai
Lei Xing - University of Surrey, School of Chemistry and Chemical Engineering
Yue Zhang - Chongqing University
Qian Fu
Oliver Fisher - University of Surrey, School of Chemistry and Chemical Engineering
Steve D.R. Christie - Loughborough University
Jin Xuan - University of Surrey, School of Chemistry and Chemical Engineering
Publication Details: Digital Chemical Engineering, Vol.9, p.100123
Date published: 12/2023
Identifiers: 99818311502346
Academic Unit: Institute for Sustainability; School of Chemistry and Chemical Engineering
Language: English
Resource Type: Journal article
SDG (Scopus 2023): SDG 7| SDG 13