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CLC number: TK229.6

On-line Access: 2018-04-04

Received: 2016-12-21

Revision Accepted: 2017-06-11

Crosschecked: 2018-03-07

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Zeng-yi Ma


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Journal of Zhejiang University SCIENCE A 2018 Vol.19 No.4 P.315-328


Development of a NOx emission model with seven optimized input parameters for a coal-fired boiler

Author(s):  Yue-lan Wang, Zeng-yi Ma, Hai-hui You, Yi-jun Tang, Yue-liang Shen, Ming-jiang Ni, Yong Chi, Jian-hua Yan

Affiliation(s):  State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   mazy@zju.edu.cn

Key Words:  Nitrogen oxide (NOx), Coal-fired boiler, Least squares support vector machine, Input parameters, Sensitivity analysis

Yue-lan Wang, Zeng-yi Ma, Hai-hui You, Yi-jun Tang, Yue-liang Shen, Ming-jiang Ni, Yong Chi, Jian-hua Yan. Development of a NOx emission model with seven optimized input parameters for a coal-fired boiler[J]. Journal of Zhejiang University Science A, 2018, 19(4): 315-328.

@article{title="Development of a NOx emission model with seven optimized input parameters for a coal-fired boiler",
author="Yue-lan Wang, Zeng-yi Ma, Hai-hui You, Yi-jun Tang, Yue-liang Shen, Ming-jiang Ni, Yong Chi, Jian-hua Yan",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

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%T Development of a NOx emission model with seven optimized input parameters for a coal-fired boiler
%A Yue-lan Wang
%A Zeng-yi Ma
%A Hai-hui You
%A Yi-jun Tang
%A Yue-liang Shen
%A Ming-jiang Ni
%A Yong Chi
%A Jian-hua Yan
%J Journal of Zhejiang University SCIENCE A
%V 19
%N 4
%P 315-328
%@ 1673-565X
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1600787

T1 - Development of a NOx emission model with seven optimized input parameters for a coal-fired boiler
A1 - Yue-lan Wang
A1 - Zeng-yi Ma
A1 - Hai-hui You
A1 - Yi-jun Tang
A1 - Yue-liang Shen
A1 - Ming-jiang Ni
A1 - Yong Chi
A1 - Jian-hua Yan
J0 - Journal of Zhejiang University Science A
VL - 19
IS - 4
SP - 315
EP - 328
%@ 1673-565X
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1600787

Optimizing the operation of coal-fired power plants to reduce nitrogen oxide (NOx) emissions requires accurate modeling of the NOx emission process. The careful selection of input parameters not only forms the basis of accurate modeling, but can also be used to reduce the complexity of the model. The present study employs the least squares support vector machine-supervised learning method to model NOx emissions based on historical real time data obtained from a 1000-MW once-through boiler. The initial input parameters are determined by expert knowledge and operational experience, while the final input parameters are obtained by sensitivity analysis, where the variation in model accuracy for a given set of data is analyzed as one or several input parameters are successively omitted from the calculations, while retaining all other parameters. Here, model accuracy is evaluated according to the mean relative error (MRE). This process reduces the parameters required for NOx emission modeling from an initial number of 33 to 7, while the corresponding MRE is reduced from 3.09% to 2.23%. Moreover, a correlation of 0.9566 between predicted and measured values was obtained by applying the model with just these seven input parameters to a validation dataset. As such, the proposed method for selecting input parameters serves as a reference for related studies.

This paperdescribes the selection procedure of input parameters in the prediction model for NOX emission in coal fired boiler plants. There are more than 33 variables that can be considered as input parameters in the NOx estimation model. Selection of appropriate input parameters is very complicated problem and has been received attention of many researchers. The authors propose a method of selection of most probable input parameters that can be used in the estimation successfully. I couldn't find any originality nor novelty in this paper, but the systematical selection procedure described in this paper deserves to attract attention of related engineers and researchers and may be very useful in the NOx estimation study.


创新点:1. 采用最小二乘支持向量机建立NOx排放模型; 2. 通过敏感性分析确定模型的最终输入参数.
方法:1. 根据专家知识及运行经验确定NOx排放模型的初始输入参数(图2); 2. 根据锅炉的运行历史数据,采用最小二乘支持向量机建立NOx排放模型; 3. 采用敏感性分析方法确定NOx排放模型的最终输入参数(图11),并用其进行建模以验证模型的有效性.
结论:1. 采用最小二乘支持向量机建立的1000 MW 超超临界前后墙对冲锅炉NOx排放模型,可靠性和精度较高; 2. 经过敏感性分析,NOx排放模型的输入参数由初始的33个降为7个,模型的复杂度降低且精度提高.

关键词:氮氧化物; 煤粉锅炉; 最小二乘支持向量机; 输入参数; 敏感性分析

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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