Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction

Zhenhao Tang, Shikui Wang, Xiangying Chai, Shengxian Cao, Tinghui Ouyang and Yang Li

Energy, 2022, vol. 256, issue C

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.

Keywords: Autoencoder; Extreme learning machine; Deep learning; NOx emission concentration prediction (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:256:y:2022:i:c:s0360544222014554

DOI: 10.1016/j.energy.2022.124552

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