Prediction of NO x Concentration at Inlet Section of SCR Denitrification Reactor Based on Bidirectional Long Short-Term Memory Network and Least Squares Support Vector Machine Model

doi:10.12096/j.2096-4528.pgt.24008

Abstract

Abstract:

Objectives To address the delayed dynamic adaptation between the ammonia injection command and the NO _x concentration at the inlet section of the selective catalytic reduction (SCR) denitrification reactor, as well as the insufficient ammonia injection accuracy in current SCR denitrification ammonia injection control systems, a prediction model for the NO _x concentration at the inlet section of the SCR denitrification reactor of a 660 MW coal-fired unit is developed. Methods The sampled data from the coal-fired unit are preprocessed in combination with the flue duct structure of the unit. Subsequently, a bidirectional long short-term memory network (BiLSTM) is applied to generate a preliminary prediction of the NO _x concentration at the inlet section of the SCR denitrification ammonia injection reactor. Next, a least squares support vector machine (LSSVM) is utilized to correct the prediction residuals, thereby minimizing the prediction error of the model to the greatest extent. Finally, in order to evaluate the prediction performance of the proposed model, simulation and real-time validation are carried out. Results This model exhibits excellent performance in predicting the NO _x concentration at the inlet section of the SCR reactor under various unit load conditions and variable coal quality conditions. The prediction error in real-time validation is only 5.772 mg/m³, with an advance prediction capability of 8.5 s. Conclusions This prediction model can provide accurate feedforward information for the ammonia injection control system.

Key words: coal-fired unit, data preprocessing, neural network prediction, denitrification, bidirectional long short-term memory network (BiLSTM), least squares support vector machine (LSSVM), selective catalytic reduction (SCR) reactor, NO _x concentration prediction

CLC Number:

TK 323

Wengen ZENG, Donglin CHEN, Mingzhu TANG, Shuqi WANG, Zhangmao HU. Prediction of NO _x Concentration at Inlet Section of SCR Denitrification Reactor Based on Bidirectional Long Short-Term Memory Network and Least Squares Support Vector Machine Model[J]. Power Generation Technology, 2025, 46(5): 996-1004.

Figures/Tables 18

Tab. 1 Preliminary collected features for NO x concentration prediction model

序号	特征
1	机组负荷(有功功率)
2	一次风机出口流量
3	空预器出口二次风量
4	锅炉总风量
5	锅炉含氧量
6	锅炉水煤比
7	给煤机A瞬时煤量
8	给煤机B瞬时煤量
9	给煤机C瞬时煤量
10	给煤机D瞬时煤量
11	磨煤机A分离器出口风粉温度
12	磨煤机B分离器出口风粉温度
13	磨煤机C分离器出口风粉温度
14	磨煤机D分离器出口风粉温度
15	SCR反应器进口NO _x 浓度

Fig. 1 Data cleaning flowchart

Fig. 2 Filtering diagram of unit load data

Fig. 3 Box plot of boiler coal quality data

Fig. 4 Histogram of variance explanation ratio of collected features

Tab. 2 Predictor variables of NO x concentration prediction model

序号	变量
1	机组负荷(有功功率)
2	一次风机出口流量
3	锅炉水煤比
4	给煤机A瞬时煤量
5	给煤机B瞬时煤量
6	给煤机C瞬时煤量
7	给煤机D瞬时煤量
8	磨煤机A分离器出口风粉温度
9	磨煤机B分离器出口风粉温度
10	磨煤机C分离器出口风粉温度
11	磨煤机D分离器出口风粉温度

Fig. 5 Schematic diagram of flue measurement points layout in SCR denitrification system and CEMS sampling

Fig. 6 Relationship between unit load and flue gas velocity (in front of ESP entrance)

Fig. 7 Schematic diagram of sampling data time alignment in DCS

Fig. 8 BiLSTM-LSSVM model structure and training process

Fig. 9 Training performance of BiLSTM and BiLSTM-LSSVM models

Tab. 3 Comparison table of training metrics for predictive models

模型	R_E/(mg/m³)	M_E/%	R
BiLSTM	18.401	6.021	0.910
BiLSTM-LSSVM	10.532	3.755	0.941

Fig. 10 Prediction performance of BiLSTM and BiLSTM-LSSVM models

Fig. 11 Prediction error distribution of BiLSTM and BiLSTM-LSSVM models

Fig. 12 Stacked prediction error diagram of BiLSTM and BiLSTM-LSSVM models

Tab. 4 Comparison of prediction performance metrics of each prediction model for new data

模型	R_E/(mg/m³)	M_E/%	R
LSSVM	21.533	8.353	0.884
LSTM	17.804	6.103	0.915
BiLSTM	16.032	5.592	0.924
BiLSTM-LSSVM	10.256	3.926	0.964

Fig. 13 Real-time validation curve of BiLSTM-LSSVM prediction model

Tab. 5 Parameters for ammonia injection in SCR denitrification system (side A)

参数	最大值	最小值	平均值	标准差
脱硝效率/%	92.525	76.886	85.771	3.520
氨逃逸率/(×10^-6)	0.599	0.598	0.599	0.000 265
出口NO _x 质量浓度/(mg/m³)	47.433	30.297	38.650	3.236

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