基于LSTM动态修正一维机理模型的CFB机组NO x 排放浓度预测方法

doi:10.11949/0438-1157.20241379

化工学报 ›› 2025, Vol. 76 ›› Issue (7): 3416-3425.DOI: 10.11949/0438-1157.20241379

基于LSTM动态修正一维机理模型的CFB机组NO _x 排放浓度预测方法

王芳¹^,²^,³(), 马素霞¹(), 田营⁴, 刘众元⁵

^1.太原理工大学电气与动力工程学院，山西太原 030024
^2.太原锅炉集团有限公司，山西太原 030024
^3.高效储热与低碳供热山西省重点实验室，山西太原 030008
^4.国电电力大同发电有限责任公司，山西大同 037003
^5.国网山西省电力公司电力科学研究院，山西太原 030001

收稿日期:2024-11-28 修回日期:2024-12-29 出版日期:2025-07-25 发布日期:2025-08-13
通讯作者: 马素霞
作者简介:王芳（1989—），女，博士，讲师，wangfang05@tyut.edu.cn
基金资助:
国家自然科学基金面上项目(61973226);山西省自然科学基金项目(20240302123189)

NO _x emission prediction method of CFB unit based on 1D mechanism model dynamicly corrected with LSTM

Fang WANG¹^,²^,³(), Suxia MA¹(), Ying TIAN⁴, Zhongyuan LIU⁵

^1.College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
^2.Taiyuan Boiler Group Corporation Limited, Taiyuan 030024, Shanxi, China
^3.Shanxi Provincial Key Laboratory of High Efficiency Heat Storage and Low Carbon Heat Supply, Taiyuan 030008, Shanxi, China
^4.GD Power Datong Power Generation Corporation Limited, Datong 037003, Shanxi, China
^5.State Grid Shanxi Electric Power Company Electric Power Research Institute, Taiyuan 030001, Shanxi, China

Received:2024-11-28 Revised:2024-12-29 Online:2025-07-25 Published:2025-08-13
Contact: Suxia MA

摘要/Abstract

摘要：

CFB机组NO _x 排放浓度实时监测方法的不足主要表现在：基于机理模型的NO _x 排放浓度预测方法在某些不同于其建模条件的工况下，预测误差较大；基于机器学习模型的方法预测精度高，但缺乏物理意义，可解释性差。为此，提出一种CFB机组NO _x 排放浓度预测融合模型，首先构建一维循环流化床整体半经验模型模拟炉内燃烧给出NO _x 排放浓度初始预测值，其次基于长短期记忆人工神经网络构建误差校正模型，对初始预测值进行动态修正。以两台CFB机组为研究对象，结果表明，提出的模型优于单一的一维半经验模型以及长短期记忆网络、BP神经网络等模型。机理与机器学习方法的结合使得融合模型既具备高的预测精度，又具有良好的可解释性。

关键词: 循环流化床, 动态建模, 测量, NO _x 排放浓度, LSTM神经网络

Abstract:

The current monitoring methods for NO _x emission concentration of CFB unit still have some shortcomings. Mechanism based NO _x emission concentration prediction methods may have large prediction errors under certain operating conditions different from the modeling conditions. Machine learning based methods have high prediction accuracy, but lack physical significance and have poor interpretability. To this end, a fusion model for predicting NO _x emission concentration of CFB unit is proposed. Firstly, a one-dimensional circulating fluidized bed overall semi-empirical model is constructed to simulate the combustion in the furnace to give the initial prediction value of NO _x emission concentration. Secondly, an error correction model is constructed based on the long short-term memory artificial neural network to dynamically correct the initial prediction value. Taking two CFB units as research objects, the results show that the proposed model is superior to the single one-dimensional semi empirical model, as well as models such as long short-term memory network and BP neural network. The combination of mechanism and machine learning methods enables the fusion model to have both high prediction accuracy and good interpretability.

Key words: circulating fluidized bed, dynamic modeling, measurement, NO _x emission concentration, LSTM neural network

中图分类号:

TK 39

王芳, 马素霞, 田营, 刘众元. 基于LSTM动态修正一维机理模型的CFB机组NO _x 排放浓度预测方法[J]. 化工学报, 2025, 76(7): 3416-3425.

Fang WANG, Suxia MA, Ying TIAN, Zhongyuan LIU. NO _x emission prediction method of CFB unit based on 1D mechanism model dynamicly corrected with LSTM[J]. CIESC Journal, 2025, 76(7): 3416-3425.

图/表 14

图1 循环流化床模型小室分布

Fig.1 Cell division in CFB model

表1 化学反应及反应速率

Table 1 Chemical reactions and reaction rates

化学反应	反应速率	系数
$C O + 12 O 2 → C O 2$	$R C O = k C C O C O 2 0.5$	$k = 1.0 × 1012 e x p - 16000 T$
$H C N + 12 O 2 → N C O + 12 H 2$	$R H C N = k C O 2 C H C N$	$k = 2.14 × 105 e x p - 10000 T$
$N C O + 12 O 2 → N O + C O$	$R N C O = k C O 2 C H C N k 1 k 1 + k 2 C N O$	$k 2 k 1 = 1.02 × 109 e x p - 25460 T$
$N H 3 + 54 O 2 → N O + 32 H 2 O$	$R N H 3, 1 = k C O 2 C N H 3$	$k = 2.73 × 1014 e x p - 38160 T$
$N H 3 + 34 O 2 → 12 N 2 + 32 H 2 O$	$R N H 3, 2 = k C O 2 C N H 3 C O 2 + 0.054$	$k = 3.38 × 107 e x p - 10000 T$
$N O + C O → 12 N 2 + C O 2$	$R N O - C O = K T k 1 C N O k 2 C C O + k 3 k 1 C N O + k 2 C C O + k 3$	$K T = 1.952 × 1010 e x p - 19000 T$
$2 N O + C → N 2 + C O 2$	$R N O - C = π N d c 2 k C N O$	$k = 1.3 × 105 e x p - 17111 T$

表1 化学反应及反应速率

Table 1 Chemical reactions and reaction rates

化学反应	反应速率	系数
$C O + 12 O 2 → C O 2$	$R C O = k C C O C O 2 0.5$	$k = 1.0 × 1012 e x p - 16000 T$
$H C N + 12 O 2 → N C O + 12 H 2$	$R H C N = k C O 2 C H C N$	$k = 2.14 × 105 e x p - 10000 T$
$N C O + 12 O 2 → N O + C O$	$R N C O = k C O 2 C H C N k 1 k 1 + k 2 C N O$	$k 2 k 1 = 1.02 × 109 e x p - 25460 T$
$N H 3 + 54 O 2 → N O + 32 H 2 O$	$R N H 3, 1 = k C O 2 C N H 3$	$k = 2.73 × 1014 e x p - 38160 T$
$N H 3 + 34 O 2 → 12 N 2 + 32 H 2 O$	$R N H 3, 2 = k C O 2 C N H 3 C O 2 + 0.054$	$k = 3.38 × 107 e x p - 10000 T$
$N O + C O → 12 N 2 + C O 2$	$R N O - C O = K T k 1 C N O k 2 C C O + k 3 k 1 C N O + k 2 C C O + k 3$	$K T = 1.952 × 1010 e x p - 19000 T$
$2 N O + C → N 2 + C O 2$	$R N O - C = π N d c 2 k C N O$	$k = 1.3 × 105 e x p - 17111 T$

图2 GA-1D模型流程图

Fig.2 Flow chart of the GA-1D model

图3 锅炉结构图

Fig.3 Boiler structure diagram

图4 敏感性分析结果

Fig.4 Sensitivity analysis results

图5 不同隐含层节点数的RMSE

Fig.5 RMSE results with different numbers of neurons in hidden layer

图6 不同回溯步长的RMSE

Fig.6 RMSE results with different look-back timesteps

图7 融合模型流程图

Fig.7 Fusion model flowchart

图8 床压模拟值与实际值对比

Fig.8 Comparison between simulated and actual bed pressure values

图9 NO x 、NH3、HCN沿炉膛高度分布

Fig.9 Distribution of NO x, NH3, HCN along the height of the furnace

图 10 三种模型预测值与实际值对比

Fig.10 Comparison between predicted values and actual values of three models

表2 三种模型预测效果对比

Table 2 Prediction performance comparison of three models

模型	RMSE	R²	MAE
1D model	15.70	0.94	12.24
LSTM model	12.80	0.96	9.88
1D-LSTM model	10.76	0.97	8.47

表3 五种模型预测效果对比

Table 3 Prediction performance comparison of five models

模型	RMSE	R²	MAE
BP	13.96	0.95	11.74
ELM	13.17	0.95	11.05
1D-BP	13.25	0.95	10.95
1D-ELM	12.12	0.96	9.88
1D-LSTM	10.76	0.97	8.47

图11 五种模型预测值与实际值对比

Fig.11 Comparison between predicted values and actual values of five models

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基于LSTM动态修正一维机理模型的CFB机组NO _x 排放浓度预测方法

NO _x emission prediction method of CFB unit based on 1D mechanism model dynamicly corrected with LSTM

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基于LSTM动态修正一维机理模型的CFB机组NO x 排放浓度预测方法

NO x emission prediction method of CFB unit based on 1D mechanism model dynamicly corrected with LSTM

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基于LSTM动态修正一维机理模型的CFB机组NO _x 排放浓度预测方法

NO _x emission prediction method of CFB unit based on 1D mechanism model dynamicly corrected with LSTM