IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v106y2013icp89-99.html
   My bibliography  Save this article

Multi-step-ahead prediction of NOx emissions for a coal-based boiler

Author

Listed:
  • Smrekar, J.
  • Potočnik, P.
  • Senegačnik, A.

Abstract

Until 2016 power plants within the EU will have to meet new limits on emissions as dictated by EU regulations. One of the major challenges is to reduce emissions of nitrogen oxides (NOx) due to health and ozone-formation concerns. Combustion optimisation is one of the primary measures for reducing NOx emissions from boilers burning coal, oil, or natural gas. The optimisation can be achieved by excess air control, boiler fine tuning and balancing the fuel and air flow to the various burners in order to reach minimum NOx formation.

Suggested Citation

  • Smrekar, J. & Potočnik, P. & Senegačnik, A., 2013. "Multi-step-ahead prediction of NOx emissions for a coal-based boiler," Applied Energy, Elsevier, vol. 106(C), pages 89-99.
    • Handle: RePEc:eee:appene:v:106:y:2013:i:c:p:89-99
    DOI: 10.1016/j.apenergy.2012.10.056
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261912007751
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2012.10.056?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. Zhou, Hao & Cen, Kefa & Fan, Jianren, 2004. "Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks," Energy, Elsevier, vol. 29(1), pages 167-183.
    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. Zeng, Guang & Xu, Mingchen & Tu, Yaojie & Li, Zhenwei & Cai, Yongtie & Zheng, Zhimin & Tay, Kunlin & Yang, Wenming, 2020. "Influences of initial coal concentration on ignition behaviors of low-NOx bias combustion technology," Applied Energy, Elsevier, vol. 278(C).
    2. 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).
    3. Park, Jun-Gyu & Jun, Hang-Bae & Heo, Tae-Young, 2021. "Retraining prior state performances of anaerobic digestion improves prediction accuracy of methane yield in various machine learning models," Applied Energy, Elsevier, vol. 298(C).
    4. 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).
    5. 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.
    6. Hodžić, Nihad & Kazagić, Anes & Smajević, Izet, 2016. "Influence of multiple air staging and reburning on NOx emissions during co-firing of low rank brown coal with woody biomass and natural gas," Applied Energy, Elsevier, vol. 168(C), pages 38-47.
    7. 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).
    8. Baklacioglu, Tolga & Turan, Onder & Aydin, Hakan, 2015. "Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks," Energy, Elsevier, vol. 86(C), pages 709-721.
    9. Li, Chong-Mao & Cui, Tao & Nie, Rui & Lin, Han & Shan, Yuli, 2019. "Does diversification help improve the performance of coal companies? Evidence from China's listed coal companies," Resources Policy, Elsevier, vol. 61(C), pages 88-98.
    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. Lv, You & Hong, Feng & Yang, Tingting & Fang, Fang & Liu, Jizhen, 2017. "A dynamic model for the bed temperature prediction of circulating fluidized bed boilers based on least squares support vector machine with real operational data," Energy, Elsevier, vol. 124(C), pages 284-294.
    12. Zheng, Wei & Wang, Chao & Yang, Yajun & Zhang, Yongfei, 2020. "Multi-objective combustion optimization based on data-driven hybrid strategy," Energy, Elsevier, vol. 191(C).
    13. 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.
    14. 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.
    15. Böhler, Lukas & Görtler, Gregor & Krail, Jürgen & Kozek, Martin, 2019. "Carbon monoxide emission models for small-scale biomass combustion of wooden pellets," Applied Energy, Elsevier, vol. 254(C).
    16. Ti, Shuguang & Chen, Zhichao & Li, Zhengqi & Kuang, Min & Xu, Guangyin & Lai, Jinping & Wang, Zhenfeng, 2018. "Influence of primary air cone length on combustion characteristics and NOx emissions of a swirl burner from a 0.5 MW pulverized coal-fired furnace with air staging," Applied Energy, Elsevier, vol. 211(C), pages 1179-1189.
    17. Choi, Minsung & Park, Yeseul & Li, Xinzhuo & Kim, Kibeom & Sung, Yonmo & Hwang, Taegam & Choi, Gyungmin, 2021. "Numerical evaluation of pulverized coal swirling flames and NOx emissions in a coal-fired boiler: Effects of co- and counter-swirling flames and coal injection modes," Energy, Elsevier, vol. 217(C).
    18. Choi, Minsung & Kim, Kibeom & Li, Xinzhuo & Deng, Kaiwen & Park, Yeseul & Seo, Minseok & Sung, Yonmo & Choi, Gyungmin, 2020. "Strategic combustion technology with exhaust tube vortex flame: Combined effect of biomass co-firing and air-staged combustion on combustion characteristics and ash deposition," Energy, Elsevier, vol. 203(C).
    19. 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).
    20. Navarkar, Abhishek & Hasti, Veeraraghava Raju & Deneke, Elihu & Gore, Jay P., 2020. "A data-driven model for thermodynamic properties of a steam generator under cycling operation," Energy, Elsevier, vol. 211(C).
    21. Ferracuti, Francesco & Fonti, Alessandro & Ciabattoni, Lucio & Pizzuti, Stefano & Arteconi, Alessia & Helsen, Lieve & Comodi, Gabriele, 2017. "Data-driven models for short-term thermal behaviour prediction in real buildings," Applied Energy, Elsevier, vol. 204(C), pages 1375-1387.
    22. Rahat, Alma A.M. & Wang, Chunlin & Everson, Richard M. & Fieldsend, Jonathan E., 2018. "Data-driven multi-objective optimisation of coal-fired boiler combustion systems," Applied Energy, Elsevier, vol. 229(C), pages 446-458.
    23. 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.
    24. Ögren, Yngve & Tóth, Pál & Garami, Attila & Sepman, Alexey & Wiinikka, Henrik, 2018. "Development of a vision-based soft sensor for estimating equivalence ratio and major species concentration in entrained flow biomass gasification reactors," Applied Energy, Elsevier, vol. 226(C), pages 450-460.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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).
    7. Fan, Weidong & Lin, Zhengchun & Li, Youyi & Zhang, Mingchuan, 2010. "Experimental flow field characteristics of OFA for large-angle counter flow of fuel-rich jet combustion technology," Applied Energy, Elsevier, vol. 87(8), pages 2737-2745, August.
    8. Xiao Wu & Jiong Shen & Yiguo Li & Kwang Y. Lee, 2015. "Steam power plant configuration, design, and control," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 4(6), pages 537-563, November.
    9. Liukkonen, Mika & Hälikkä, Eero & Hiltunen, Teri & Hiltunen, Yrjö, 2012. "Dynamic soft sensors for NOx emissions in a circulating fluidized bed boiler," Applied Energy, Elsevier, vol. 97(C), pages 483-490.
    10. 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).
    11. Tang, Yuting & Ma, Xiaoqian & Lai, Zhiyi & Zhou, Daoxi & Lin, Hai & Chen, Yong, 2012. "NOx and SO2 emissions from municipal solid waste (MSW) combustion in CO2/O2 atmosphere," Energy, Elsevier, vol. 40(1), pages 300-306.
    12. Azimi, Seyyed Shahabeddin & Namazi, Mohammad Hosain, 2015. "Modeling of combustion of gas oil and natural gas in a furnace: Comparison of combustion characteristics," Energy, Elsevier, vol. 93(P1), pages 458-465.
    13. Yu, Youhong & Chen, Lingen & Sun, Fengrui & Wu, Chih, 2007. "Neural-network based analysis and prediction of a compressor's characteristic performance map," Applied Energy, Elsevier, vol. 84(1), pages 48-55, January.
    14. Dios, M. & Souto, J.A. & Casares, J.J., 2013. "Experimental development of CO2, SO2 and NOx emission factors for mixed lignite and subbituminous coal-fired power plant," Energy, Elsevier, vol. 53(C), pages 40-51.
    15. 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.
    16. Mikulandrić, Robert & Lončar, Dražen & Cvetinović, Dejan & Spiridon, Gabriel, 2013. "Improvement of existing coal fired thermal power plants performance by control systems modifications," Energy, Elsevier, vol. 57(C), pages 55-65.
    17. Tan, Houzhang & Niu, Yanqing & Wang, Xuebin & Xu, Tongmo & Hui, Shien, 2011. "Study of optimal pulverized coal concentration in a four-wall tangentially fired furnace," Applied Energy, Elsevier, vol. 88(4), pages 1164-1168, April.
    18. 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.
    19. 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.
    20. 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.

    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:appene:v:106:y:2013:i:c:p:89-99. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.