Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction. (1st October 2022)

• Record Type: Journal Article
• Authors: Tang, Zhenhao; Wang, Shikui; Chai, Xiangying; Cao, Shengxian; Ouyang, Tinghui; Li, Yang
• Is Part Of: Energy. Volume 256(2022)
• Publication Date: 2022-10-01
• Subjects: Autoencoder -- Extreme learning machine -- Deep learning -- NOx emission concentration prediction; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.124552
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: An automatic encoder (AE) extreme learning machine (ELM)-AE-ELM model is proposed to predict the NOx emission concentration based on the combination of mutual information algorithm (MI), AE, and ELM. First, the importance of practical variables is computed by the MI algorithm, and the mechanism is analyzed to determine the variables related to the NOx emission concentration. Then, the time delay correlations between the selected variables and NOx emission concentration are further analyzed to reconstruct the modeling data. Subsequently, the AE is applied to extract hidden features within the input variables. Finally, an ELM algorithm establishes the relationship between the NOx emission concentration and deep features. The experimental results on practical data indicate that the proposed model shows promising performance compared to state-of-art models. Highlights: A MI-based method for calculating delay time between NOx emission and input variables is developed. A new feature extraction method based on auto-encoder is developed. An AE-ELM-based method for predicting NOx emissions is developed. Case study on NOx emissions at a 1000 MW coal-fired power plant. The proposed model outperformed other comparable models.