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Computer Science > Machine Learning

arXiv:2206.14496 (cs)
[Submitted on 29 Jun 2022]

Title:Auto-Encoder-Extreme Learning Machine Model for Boiler NOx Emission Concentration Prediction

Authors:Zhenhao Tang, Shikui Wang, Xiangying Chai, Shengxian Cao, Tinghui Ouyang, Yang Li
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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.
Comments: Accepted by Energy
Subjects: Machine Learning (cs.LG); Systems and Control (eess.SY)
Cite as: arXiv:2206.14496 [cs.LG]
  (or arXiv:2206.14496v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2206.14496
Journal reference: Energy 256 (2022) 124552
Related DOI: https://doi.org/10.1016/j.energy.2022.124552

Submission history

From: Yang Li [view email]
[v1] Wed, 29 Jun 2022 09:34:08 UTC (855 KB)
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