Prediction of NOx Concentration at SCR Inlet Based on BMIFS-LSTM
As the main energy source for thermal power generation, coal generates a large amount of NOx during its incineration in boilers, and excessive NOx emissions can cause serious pollution to the air environment. Selective catalytic reduction denitrification (SCR) selects the optimal amount of ammonia t...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/13/5/686 |
| _version_ | 1797501642025730048 |
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| author | Meiyan Song Jianzhong Xue Shaohua Gao Guodong Cheng Jun Chen Haisong Lu Ze Dong |
| author_facet | Meiyan Song Jianzhong Xue Shaohua Gao Guodong Cheng Jun Chen Haisong Lu Ze Dong |
| author_sort | Meiyan Song |
| collection | DOAJ |
| description | As the main energy source for thermal power generation, coal generates a large amount of NOx during its incineration in boilers, and excessive NOx emissions can cause serious pollution to the air environment. Selective catalytic reduction denitrification (SCR) selects the optimal amount of ammonia to be injected for denitrification based on the measurement of NOx concentration by the automatic flue gas monitoring system. Since the automatic flue gas monitoring system has a large delay in measurement, it cannot accurately reflect the real-time changes of NOx concentration at the SCR inlet when the unit load fluctuates, leading to problems such as ammonia escape and NOx emission exceeding the standard. In response to these problems, this paper proposes an SCR inlet NOx concentration prediction algorithm based on BMIFS-LSTM. An improved mutual information feature selection algorithm (BMIFS) is used to filter out the auxiliary variables with maximum correlation and minimum redundancy with NOx concentration, and reduce the coupling and dimensionality among the variables in the data set. The dominant and auxiliary variables are then fed together into a long short-term memory neural network (LSTM) to build a prognostic model. Simulation experiments are conducted using historical operation data of a 300 MW thermal power unit. The experimental results show that the algorithm in this paper reduces the average relative error by 3.45% and the root mean square error by 1.50 compared with the algorithm without auxiliary variable extraction, which can accurately reflect the real-time changes of NOx concentration at the SCR inlet, solve the problem of delay in NOx concentration measurement, and reduce the occurrence of atmospheric pollution caused by excessive NOx emissions. |
| first_indexed | 2024-03-10T03:21:27Z |
| format | Article |
| id | doaj.art-696ea364f6574e6da4016b204c08a6d7 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T03:21:27Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-696ea364f6574e6da4016b204c08a6d72023-11-23T10:01:15ZengMDPI AGAtmosphere2073-44332022-04-0113568610.3390/atmos13050686Prediction of NOx Concentration at SCR Inlet Based on BMIFS-LSTMMeiyan Song0Jianzhong Xue1Shaohua Gao2Guodong Cheng3Jun Chen4Haisong Lu5Ze Dong6Xi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, ChinaXi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, ChinaXi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, ChinaXi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, ChinaNanjing NR Electric Co., Ltd., Nanjing 211102, ChinaNanjing NR Electric Co., Ltd., Nanjing 211102, ChinaHebei Technology Innovation Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding 071066, ChinaAs the main energy source for thermal power generation, coal generates a large amount of NOx during its incineration in boilers, and excessive NOx emissions can cause serious pollution to the air environment. Selective catalytic reduction denitrification (SCR) selects the optimal amount of ammonia to be injected for denitrification based on the measurement of NOx concentration by the automatic flue gas monitoring system. Since the automatic flue gas monitoring system has a large delay in measurement, it cannot accurately reflect the real-time changes of NOx concentration at the SCR inlet when the unit load fluctuates, leading to problems such as ammonia escape and NOx emission exceeding the standard. In response to these problems, this paper proposes an SCR inlet NOx concentration prediction algorithm based on BMIFS-LSTM. An improved mutual information feature selection algorithm (BMIFS) is used to filter out the auxiliary variables with maximum correlation and minimum redundancy with NOx concentration, and reduce the coupling and dimensionality among the variables in the data set. The dominant and auxiliary variables are then fed together into a long short-term memory neural network (LSTM) to build a prognostic model. Simulation experiments are conducted using historical operation data of a 300 MW thermal power unit. The experimental results show that the algorithm in this paper reduces the average relative error by 3.45% and the root mean square error by 1.50 compared with the algorithm without auxiliary variable extraction, which can accurately reflect the real-time changes of NOx concentration at the SCR inlet, solve the problem of delay in NOx concentration measurement, and reduce the occurrence of atmospheric pollution caused by excessive NOx emissions.https://www.mdpi.com/2073-4433/13/5/686NOx concentrationLSTMmutual information feature selectionSCRmodel prediction |
| spellingShingle | Meiyan Song Jianzhong Xue Shaohua Gao Guodong Cheng Jun Chen Haisong Lu Ze Dong Prediction of NOx Concentration at SCR Inlet Based on BMIFS-LSTM Atmosphere NOx concentration LSTM mutual information feature selection SCR model prediction |
| title | Prediction of NOx Concentration at SCR Inlet Based on BMIFS-LSTM |
| title_full | Prediction of NOx Concentration at SCR Inlet Based on BMIFS-LSTM |
| title_fullStr | Prediction of NOx Concentration at SCR Inlet Based on BMIFS-LSTM |
| title_full_unstemmed | Prediction of NOx Concentration at SCR Inlet Based on BMIFS-LSTM |
| title_short | Prediction of NOx Concentration at SCR Inlet Based on BMIFS-LSTM |
| title_sort | prediction of nox concentration at scr inlet based on bmifs lstm |
| topic | NOx concentration LSTM mutual information feature selection SCR model prediction |
| url | https://www.mdpi.com/2073-4433/13/5/686 |
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