A deep learning-based operation optimization strategy for BFG/coal co-firing boiler

• Main Authors: Jian-Guo Wang, Jin-Qiu Min, Li-Lan Liu, Bang-Hua Yang, Shi-Wei Ma, Min-Rui Fei, Yi-Min Guo, Yuan Yao, Yi-Ping Wu
• Format: Conference Proceeding
• Language: English
• Subjects: Atmospheric modeling; auto-coder; BFG/coal co-frring; Biological neural networks; Boilers; Data models; data-driven; deep learning; Machine learning; Predictive models; thermal efficiency; Training
• ISSN: 2161-2927
• DOI: 10.23919/ChiCC.2017.8028907

How to operate a BFG/coal co-firing boiler in high efficiency is challenging for a gas/solid multi-fuel combustion system. Taking operation data from a real boiler, this study proposes an operation optimization strategy of BFG/coal co-firing boiler based on deep learning. Firstly, the thermal efficiency model is constructed based on deep learning with all the actual sampling data, which outperform the other modeling methods. Then, the high efficiency data being selected as labeled data, a deep learning-based model of air flow is constructed. It needs to be emphasized that, to take full advantage of all available information and improve the model accuracy, all the labeled and unlabeled data are utilized in auto-coder learning of the air flow modeling. Finally, the constructed air flow model is used for the operation optimization and the thermal efficiency model is used to evaluate the level efficiency improvement. The high accuracy of the proposed modeling approaches makes the implementation of the operation optimization strategy readily practicable.