IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v192y2020ics0360544219322923.html
   My bibliography  Save this article

Prediction of the NOx emissions from thermal power plant using long-short term memory neural network

Author

Listed:
  • Yang, Guotian
  • Wang, Yingnan
  • Li, Xinli

Abstract

Coal combustion in thermal power plant is the main source of the NOx emission. An effective prediction model should be established for reducing NOx emission. This paper focuses on the application of long-short term memory (LSTM) neural network in modeling the relationship between operational parameters and NOx emission of a 660 MW boiler. Principal component analysis (PCA) method is used to eliminate the coupling between original variables, then the NOx emission model based on LSTM is established. The dropout strategy and Adam optimizer are adopted to improve the network performance. Compared with the least squares support vector machine (LSSVM), the proposed model has higher prediction accuracy, faster response speed, stronger generalization ability, and is more competitive in the modeling of NOx emission. The difference between the LSTM model and the traditional recurrent neural network (RNN) model is also compared. The results show that the performance of LSTM model is better than that of RNN model under the same model structure and parameters. In addition, a NOx prediction model for high-dimensional data is established and achieves good prediction performance. Thus, LSTM is capable to model the NOx emission for coal-fired boilers and is superior to other traditional modeling methods.

Suggested Citation

  • Yang, Guotian & Wang, Yingnan & Li, Xinli, 2020. "Prediction of the NOx emissions from thermal power plant using long-short term memory neural network," Energy, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:energy:v:192:y:2020:i:c:s0360544219322923
    DOI: 10.1016/j.energy.2019.116597
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219322923
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.116597?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wei, Zhongbao & Li, Xiaolu & Xu, Lijun & Cheng, Yanting, 2013. "Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 683-692.
    2. Li, Qingwei & Yao, Guihuan, 2017. "Improved coal combustion optimization model based on load balance and coal qualities," Energy, Elsevier, vol. 132(C), pages 204-212.
    3. Tan, Peng & Xia, Ji & Zhang, Cheng & Fang, Qingyan & Chen, Gang, 2016. "Modeling and reduction of NOX emissions for a 700 MW coal-fired boiler with the advanced machine learning method," Energy, Elsevier, vol. 94(C), pages 672-679.
    4. Wang, Chunlin & Liu, Yang & Zheng, Song & Jiang, Aipeng, 2018. "Optimizing combustion of coal fired boilers for reducing NOx emission using Gaussian Process," Energy, Elsevier, vol. 153(C), pages 149-158.
    5. Liukkonen, M. & Heikkinen, M. & Hiltunen, T. & Hälikkä, E. & Kuivalainen, R. & Hiltunen, Y., 2011. "Artificial neural networks for analysis of process states in fluidized bed combustion," Energy, Elsevier, vol. 36(1), pages 339-347.
    6. Lv, You & Liu, Jizhen & Yang, Tingting & Zeng, Deliang, 2013. "A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 319-329.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    2. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    3. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    4. Mehdi Jadidi & Luke Di Liddo & Seth B. Dworkin, 2021. "A Long Short-Term Memory Neural Network for the Low-Cost Prediction of Soot Concentration in a Time-Dependent Flame," Energies, MDPI, vol. 14(5), pages 1-18, March.
    5. Wen, Xiaoqiang & Li, Kaichuang & Wang, Jianguo, 2023. "NOx emission predicting for coal-fired boilers based on ensemble learning methods and optimized base learners," Energy, Elsevier, vol. 264(C).
    6. Li, Xinli & Wang, Yingnan & Zhu, Yun & Yang, Guotian & Liu, He, 2021. "Temperature prediction of combustion level of ultra-supercritical unit through data mining and modelling," Energy, Elsevier, vol. 231(C).
    7. Kwak, Sanghyeok & Choi, Jaehong & Lee, Min Chul & Yoon, Youngbin, 2021. "Predicting instability frequency and amplitude using artificial neural network in a partially premixed combustor," Energy, Elsevier, vol. 230(C).
    8. Nan Li & You Lv & Yong Hu, 2022. "Prediction of NOx Emissions from a Coal-Fired Boiler Based on Convolutional Neural Networks with a Channel Attention Mechanism," Energies, MDPI, vol. 16(1), pages 1-11, December.
    9. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    10. Gao, Wei & Liu, Ming & Yin, Junjie & Zhao, Yongliang & Chen, Weixiong & Yan, Junjie, 2023. "An improved control strategy for a denitrification system using cooperative control of NH3 injection and flue gas temperature for coal-fired power plants," Energy, Elsevier, vol. 282(C).
    11. Ding, Xiaosong & Feng, Chong & Yu, Peiling & Li, Kaiwen & Chen, Xi, 2023. "Gradient boosting decision tree in the prediction of NOx emission of waste incineration," Energy, Elsevier, vol. 264(C).
    12. Yılmaz, Semih & Kumlutaş, Dilek & Yücekaya, Utku Alp & Cumbul, Ahmet Yakup, 2021. "Prediction of the equilibrium compositions in the combustion products of a domestic boiler," Energy, Elsevier, vol. 233(C).
    13. Li, Ruilian & Zeng, Deliang & Li, Tingting & Ti, Baozhong & Hu, Yong, 2023. "Real-time prediction of SO2 emission concentration under wide range of variable loads by convolution-LSTM VE-transformer," Energy, Elsevier, vol. 269(C).
    14. Hou, Guolian & Gong, Linjuan & Hu, Bo & Su, Huilin & Huang, Ting & Huang, Congzhi & Fan, Wei & Zhao, Yuanzhu, 2022. "Application of fast adaptive moth-flame optimization in flexible operation modeling for supercritical unit," Energy, Elsevier, vol. 239(PA).
    15. Jia, Xiongjie & Sang, Yichen & Li, Yanjun & Du, Wei & Zhang, Guolei, 2022. "Short-term forecasting for supercharged boiler safety performance based on advanced data-driven modelling framework," Energy, Elsevier, vol. 239(PE).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lv, You & Lv, Xuguang & Fang, Fang & Yang, Tingting & Romero, Carlos E., 2020. "Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants," Energy, Elsevier, vol. 192(C).
    2. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    3. Wen, Xiaoqiang & Li, Kaichuang & Wang, Jianguo, 2023. "NOx emission predicting for coal-fired boilers based on ensemble learning methods and optimized base learners," Energy, Elsevier, vol. 264(C).
    4. Tang, Zhenhao & Wang, Shikui & Chai, Xiangying & Cao, Shengxian & Ouyang, Tinghui & Li, Yang, 2022. "Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction," Energy, Elsevier, vol. 256(C).
    5. Halil Akbaş & Gültekin Özdemir, 2020. "An Integrated Prediction and Optimization Model of a Thermal Energy Production System in a Factory Producing Furniture Components," Energies, MDPI, vol. 13(22), pages 1-29, November.
    6. Xie, Peiran & Gao, Mingming & Zhang, Hongfu & Niu, Yuguang & Wang, Xiaowen, 2020. "Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network," Energy, Elsevier, vol. 190(C).
    7. Li, Qingwei & Wu, Jiang & Wei, Hongqi, 2018. "Reduction of elemental mercury in coal-fired boiler flue gas with computational intelligence approach," Energy, Elsevier, vol. 160(C), pages 753-762.
    8. Chuanpeng Zhu & Pu Huang & Yiguo Li, 2022. "Closed-Loop Combustion Optimization Based on Dynamic and Adaptive Models with Application to a Coal-Fired Boiler," Energies, MDPI, vol. 15(14), pages 1-16, July.
    9. Li, Qingwei & Yao, Guihuan, 2017. "Improved coal combustion optimization model based on load balance and coal qualities," Energy, Elsevier, vol. 132(C), pages 204-212.
    10. Tan, Peng & He, Biao & Zhang, Cheng & Rao, Debei & Li, Shengnan & Fang, Qingyan & Chen, Gang, 2019. "Dynamic modeling of NOX emission in a 660 MW coal-fired boiler with long short-term memory," Energy, Elsevier, vol. 176(C), pages 429-436.
    11. Tan, Peng & Xia, Ji & Zhang, Cheng & Fang, Qingyan & Chen, Gang, 2016. "Modeling and reduction of NOX emissions for a 700 MW coal-fired boiler with the advanced machine learning method," Energy, Elsevier, vol. 94(C), pages 672-679.
    12. Li, Ruilian & Zeng, Deliang & Li, Tingting & Ti, Baozhong & Hu, Yong, 2023. "Real-time prediction of SO2 emission concentration under wide range of variable loads by convolution-LSTM VE-transformer," Energy, Elsevier, vol. 269(C).
    13. Hyuk Choi & Ju-Hong Lee & Ji-Hoon Yu & Un-Chul Moon & Mi-Jong Kim & Kwang Y. Lee, 2023. "One-Step Ahead Control Using Online Interpolated Transfer Function for Supplementary Control of Air-Fuel Ratio in Thermal Power Plants," Energies, MDPI, vol. 16(21), pages 1-18, November.
    14. Lei Han & Lingmei Wang & Hairui Yang & Chengzhen Jia & Enlong Meng & Yushan Liu & Shaoping Yin, 2023. "Optimization of Circulating Fluidized Bed Boiler Combustion Key Control Parameters Based on Machine Learning," Energies, MDPI, vol. 16(15), pages 1-23, July.
    15. Mollo, Malebo & Kolesnikov, Andrei & Makgato, Seshibe, 2022. "Simultaneous reduction of NOx emission and SOx emission aided by improved efficiency of a Once-Through Benson Type Coal Boiler," Energy, Elsevier, vol. 248(C).
    16. Ding, Xiaosong & Feng, Chong & Yu, Peiling & Li, Kaiwen & Chen, Xi, 2023. "Gradient boosting decision tree in the prediction of NOx emission of waste incineration," Energy, Elsevier, vol. 264(C).
    17. Rossi, Francesco & Velázquez, David, 2015. "A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant," Energy, Elsevier, vol. 89(C), pages 528-544.
    18. Zheng, Wei & Wang, Chao & Yang, Yajun & Zhang, Yongfei, 2020. "Multi-objective combustion optimization based on data-driven hybrid strategy," Energy, Elsevier, vol. 191(C).
    19. Liukkonen, M. & Hiltunen, T., 2014. "Adaptive monitoring of emissions in energy boilers using self-organizing maps: An application to a biomass-fired CFB (circulating fluidized bed)," Energy, Elsevier, vol. 73(C), pages 443-452.
    20. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:192:y:2020:i:c:s0360544219322923. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.