Journal of South China University of Technology(Natural Science) >

2025 , Vol. 53 >Issue 7: 1 - 10

DOI: https://doi.org/10.12141/j.issn.1000-565X.240519

Energy, Power & Electrical Engineering

Emission Concentration Prediction of NO x from Waste Incinerator Based on MIC-PCA-LSTM Model

  • YAO Shunchun ,
  • LI Longqian ,
  • LIU Wen ,
  • LI Zhenghui ,
  • ZHOU Anli ,
  • LI Wenjing ,
  • CHEN Jianghong ,
  • LU Zhimin
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  • 1.School of Electric Power，South China University of Technology，Guangzhou 510640，Guangdong，China
    2.Guangzhou Huantou Huacheng Environmental Protection Energy Co. ，Ltd. ，Guangzhou 510830，Guangdong，China
姚顺春（1983—），男，博士，教授，主要从事能源低碳转化与清洁利用研究，E-mail： epscyao@scut.edu.cn
刘文（1983—），男，高级工程师，主要从事发电过程燃烧及其污染物排放优化控制研究。

Received date: 2024-10-25

  Online published: 2024-11-07

Supported by

the National Key Research and Development Program of China(2024YFC3909002);the Subproject of the National Key Research and Development Program of China(2024YFC3909004-02)

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Abstract

Accurately predicting the emission concentration of NO x at the outlet of the selective catalytic reduction (SCR) denitrification system in the waste incineration process is of great significance for enhancing data quality and optimizing ammonia injection. However, the waste incineration process exhibits significant nonlinearity, multivariate coupling, and time-series characteristics. These factors pose substantial challenges to achieving accurate prediction of NO x emissions. To solve this problem, this paper presents a prediction model for the emission concentration of NO x at the outlet of SCR denitrification system by integrating maximum information coefficient (MIC), principal component analysis (PCA) and long short-term memory (LSTM) neural networks. First, MIC is employed to assess the maximum normalized mutual information values among variables, and the input variables that exhibit the strong-est correlation with NO x emission concentration are selected while the redundant variables are eliminated based on the principle of maximum redundancy. Then, PCA is utilized to obtain the cumulative contribution rate of the va-riance of each principal component, extract the principal component features, and obtain the optimal input feature variable set. Finally, an emission prediction model of NO x at the outlet of SCR denitrification system is established based on the LSTM neural network. The results indicate that, as compared with the back propagation neural network model and the support vector machine model, the proposed model exhibits higher accuracy and generalization ability, achieving a mean absolute percentage error of 6.33%, a root mean squared error of 4.71 mg/m3 and a determination coefficient of 0.90. This research lays a theoretical foundation for achieving the intelligent control of SCR denitrification system in the waste incineration process.

Key words： waste incineration; selective catalytic reduction; emission concentration prediction; maximum information coefficient; principal component analysis; long short-term memory neural network

Cite this article

YAO Shunchun , LI Longqian , LIU Wen , LI Zhenghui , ZHOU Anli , LI Wenjing , CHEN Jianghong , LU Zhimin . Emission Concentration Prediction of NO x from Waste Incinerator Based on MIC-PCA-LSTM Model[J]. Journal of South China University of Technology(Natural Science), 2025 , 53(7) : 1 -10 . DOI: 10.12141/j.issn.1000-565X.240519

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