Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction
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DOI: 10.1016/j.energy.2022.124552
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Cited by:
- Wu, Zheng & Zhang, Yue & Dong, Ze, 2023. "Prediction of NOx emission concentration from coal-fired power plant based on joint knowledge and data driven," Energy, Elsevier, vol. 271(C).
- Han, Zhezhe & Tang, Xiaoyu & Xie, Yue & Liang, Ruiyu & Bao, Yongqiang, 2024. "Prediction of heavy-oil combustion emissions with a semi-supervised learning model considering variable operation conditions," Energy, Elsevier, vol. 288(C).
- Zhu, Yukun & Yu, Cong & Fan, Wei & Yu, Haiquan & Jin, Wei & Chen, Shuo & Liu, Xia, 2023. "A novel NOx emission prediction model for multimodal operational utility boilers considering local features and prior knowledge," Energy, Elsevier, vol. 280(C).
- Wen, Xiaoqiang & Li, Kaichuang & Wang, Jianguo, 2023. "NOx emission predicting for coal-fired boilers based on ensemble learning methods and optimized base learners," Energy, Elsevier, vol. 264(C).
- Yu, Jianxi & Petersen, Nils & Liu, Pei & Li, Zheng & Wirsum, Manfred, 2022. "Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development," Energy, Elsevier, vol. 260(C).
- Beichen Zhao & Binshan Ju & Chaoxiang Wang, 2023. "Initial-Productivity Prediction Method of Oil Wells for Low-Permeability Reservoirs Based on PSO-ELM Algorithm," Energies, MDPI, vol. 16(11), pages 1-17, June.
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Keywords
Autoencoder; Extreme learning machine; Deep learning; NOx emission concentration prediction;All these keywords.
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