IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v273y2023ics0360544223006217.html
   My bibliography  Save this article

Predicting the NOx emissions of low heat value gas rich-quench-lean combustor via three integrated learning algorithms with Bayesian optimization

Author

Listed:
  • Yan, Peiliang
  • Fan, Weijun
  • Zhang, Rongchun

Abstract

With the increased attention to low heat value gas fuels in recent years, research on NOx emissions from the combustors of low heat value gas fuels is necessary. This study introduces integrated learning algorithms to the NOx pollution emission prediction from gas turbine combustors. The combustor is a self-designed low heat value gas rich-quench-lean combustor and 92 sets of emission data have been obtained using simulation methods. The simulation data has been experimentally verified. The effect of four control parameters on NOx emissions has been investigated: inlet air temperature, inlet air mass flow rate, swirler installation angle, and combustor lean burn zone length. Pearson correlation coefficients show that four control parameters have very low correlations. The three integrated learning algorithms used in this study are the random forest regression algorithm, the extreme gradient boosting algorithm, and the natural gradient boosting algorithm. The hyperparameters of the three integrated learning algorithms are optimised using the Bayesian optimization algorithm. Three integrated learning algorithms are utilized to predict pollutant emission characteristics and the results show that NGboost provides the best predictions and Random Forest the worst, with NGboost suffering from overfitting problems. The results of the algorithm predictions were analyzed to understand the impact of different control parameters on the NOx emissions. A feature importance evaluation shows that the influence of inlet air temperature on NOx emissions far outweighs the other three parameters.

Suggested Citation

  • Yan, Peiliang & Fan, Weijun & Zhang, Rongchun, 2023. "Predicting the NOx emissions of low heat value gas rich-quench-lean combustor via three integrated learning algorithms with Bayesian optimization," Energy, Elsevier, vol. 273(C).
  • Handle: RePEc:eee:energy:v:273:y:2023:i:c:s0360544223006217
    DOI: 10.1016/j.energy.2023.127227
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223006217
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.127227?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. Miao, Zhuang & Chen, Xiaodong & Baležentis, Tomas, 2021. "Improving energy use and mitigating pollutant emissions across “Three Regions and Ten Urban Agglomerations”: A city-level productivity growth decomposition," Applied Energy, Elsevier, vol. 283(C).
    2. Dong, Zhikun & Chen, Yaoran & Zhou, Dai & Su, Jie & Han, Zhaolong & Cao, Yong & Bao, Yan & Zhao, Feng & Wang, Rui & Zhao, Yongsheng & Xu, Yuwang, 2022. "The mean wake model and its novel characteristic parameter of H-rotor VAWTs based on random forest method," Energy, Elsevier, vol. 239(PE).
    3. 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).
    4. Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Liu, Jiao & Yu, Daren, 2021. "Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers," Applied Energy, Elsevier, vol. 302(C).
    5. Nakamura, Kotaro & Muramatsu, Takehiko & Ogawa, Takashi & Nakagaki, Takao, 2021. "Prediction of de-NOx performance using monolithic SCR catalyst under load following operation of natural gas-fired combined cycle power plants," Energy, Elsevier, vol. 227(C).
    6. Wen, Yifan & Wu, Ruoxi & Zhou, Zihang & Zhang, Shaojun & Yang, Shengge & Wallington, Timothy J. & Shen, Wei & Tan, Qinwen & Deng, Ye & Wu, Ye, 2022. "A data-driven method of traffic emissions mapping with land use random forest models," Applied Energy, Elsevier, vol. 305(C).
    7. Li, Jianzhong & Chen, Jian & Jin, Wu & Yuan, Li & Hu, Ge, 2020. "The design and performance of a RP-3 fueled high temperature rise combustor based on RQL staged combustion," Energy, Elsevier, vol. 209(C).
    8. Jiang, Yu & Lee, Byoung-Hwa & Oh, Dong-Hun & Jeon, Chung-Hwan, 2022. "Influence of various air-staging on combustion and NOX emission characteristics in a tangentially fired boiler under the 50% load condition," Energy, Elsevier, vol. 244(PB).
    9. Park, Yeseul & Choi, Minsung & Kim, Dongmin & Lee, Joongsung & Choi, Gyungmin, 2021. "Performance analysis of large-scale industrial gas turbine considering stable combustor operation using novel blended fuel," Energy, Elsevier, vol. 236(C).
    10. Grochowalski, Jaroslaw & Jachymek, Piotr & Andrzejczyk, Marek & Klajny, Marcin & Widuch, Agata & Morkisz, Pawel & Hernik, Bartłomiej & Zdeb, Janusz & Adamczyk, Wojciech, 2021. "Towards application of machine learning algorithms for prediction temperature distribution within CFB boiler based on specified operating conditions," Energy, Elsevier, vol. 237(C).
    11. Cai, Jianchao & Xu, Kai & Zhu, Yanhui & Hu, Fang & Li, Liuhuan, 2020. "Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest," Applied Energy, Elsevier, vol. 262(C).
    12. Roy, Rishi & Gupta, Ashwani K., 2022. "Data-driven prediction of flame temperature and pollutant emission in distributed combustion," Applied Energy, Elsevier, vol. 310(C).
    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. Okeleye, Samuel Adeola & Thiruvengadam, Arvind & Perhinschi, Mario G. & Carder, Daniel, 2024. "Data-driven machine learning model of a Selective Catalytic Reduction on Filter (SCRF) in a heavy-duty diesel engine: A comparison of Artificial Neural Network with Tree-based algorithms," Energy, Elsevier, vol. 290(C).
    2. Qiu, Tao & Li, Ning & Lei, Yan & Sang, Hailang & Ma, Xuejian & Liu, Zedu, 2024. "Research on the method of diesel particulate filters carbon load recognition based on deep learning," Energy, Elsevier, vol. 292(C).
    3. Alruqi, Mansoor & Sharma, Prabhakar & Ağbulut, Ümit, 2023. "Investigations on biomass gasification derived producer gas and algal biodiesel to power a dual-fuel engines: Application of neural networks optimized with Bayesian approach and K-cross fold," Energy, Elsevier, vol. 282(C).
    4. Wang, Jianguo & Han, Lincheng & Zhang, Xiuyu & Wang, Yingzhou & Zhang, Shude, 2023. "Electrical load forecasting based on variable T-distribution and dual attention mechanism," Energy, Elsevier, vol. 283(C).
    5. Zhang, Hua & Li, Zongkun & Ge, Wei & Zhang, Yadong & Wang, Te & Sun, Heqiang & Jiao, Yutie, 2024. "An extended Bayesian network model for calculating dam failure probability based on fuzzy sets and dynamic evidential reasoning," Energy, Elsevier, vol. 301(C).

    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. Cheng, Xianda & Zheng, Haoran & Yang, Qian & Zheng, Peiying & Dong, Wei, 2023. "Surrogate model-based real-time gas path fault diagnosis for gas turbines under transient conditions," Energy, Elsevier, vol. 278(PA).
    2. Zhang, Xin & Chen, Zhichao & Hou, Jian & Liu, Zheng & Zeng, Lingyan & Li, Zhengqi, 2022. "Evaluation of wide-range coal combustion performance of a novel down-fired combustion technology based on gas–solid two-phase flow characteristics," Energy, Elsevier, vol. 248(C).
    3. Zhou, P. & Zhang, H. & Zhang, L.P., 2022. "The drivers of energy intensity changes in Chinese cities: A production-theoretical decomposition analysis," Applied Energy, Elsevier, vol. 307(C).
    4. Xiaodong Chen & Anda Guo & Jiahao Zhu & Fang Wang & Yanqiu He, 2022. "Accessing performance of transport sector considering risks of climate change and traffic accidents: joint bounded-adjusted measure and Luenberger decomposition," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(1), pages 115-138, March.
    5. Liu, Jianmiao & Li, Junyi & Chen, Yong & Lian, Song & Zeng, Jiaqi & Geng, Maosi & Zheng, Sijing & Dong, Yinan & He, Yan & Huang, Pei & Zhao, Zhijian & Yan, Xiaoyu & Hu, Qinru & Wang, Lei & Yang, Di & , 2023. "Multi-scale urban passenger transportation CO2 emission calculation platform for smart mobility management," Applied Energy, Elsevier, vol. 331(C).
    6. Sungur, Bilal & Basar, Cem & Kaleli, Alirıza, 2023. "Multi-objective optimisation of the emission parameters and efficiency of pellet stove at different supply airflow positions based on machine learning approach," Energy, Elsevier, vol. 278(PA).
    7. Kuznetsov, G.V. & Syrodoy, S.V. & Purin, M.V. & Karelin, V.A. & Nigay, N.A. & Yankovsky, S.A. & Isaev, S.A., 2024. "Analysis of the possibility of solid-phase ignition of coal fuel," Energy, Elsevier, vol. 288(C).
    8. Zhang, Tao & Li, Yiteng & Chen, Yin & Feng, Xiaoyu & Zhu, Xingyu & Chen, Zhangxing & Yao, Jun & Zheng, Yongchun & Cai, Jianchao & Song, Hongqing & Sun, Shuyu, 2021. "Review on space energy," Applied Energy, Elsevier, vol. 292(C).
    9. Malin Song & Weiliang Tao, 2022. "Coupling and coordination analysis of China's regional urban‐rural integration and land‐use efficiency," Growth and Change, Wiley Blackwell, vol. 53(3), pages 1384-1413, September.
    10. Zhang, Yagang & Zhang, Jinghui & Yu, Leyi & Pan, Zhiya & Feng, Changyou & Sun, Yiqian & Wang, Fei, 2022. "A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique," Energy, Elsevier, vol. 254(PC).
    11. Jiming Lin & Haozhen Li & Yong Zhang & Jianhong Yang, 2022. "Experimental and Numerical Study of a Two-Stage Swirl Burner," Energies, MDPI, vol. 15(3), pages 1-19, February.
    12. Yuhong Zhao & Ruirui Liu & Zhansheng Liu & Liang Liu & Jingjing Wang & Wenxiang Liu, 2023. "A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    13. Chu, Junfei & Shao, Caifeng & Emrouznejad, Ali & Wu, Jie & Yuan, Zhe, 2021. "Performance evaluation of organizations considering economic incentives for emission reduction: A carbon emission permit trading approach," Energy Economics, Elsevier, vol. 101(C).
    14. Wang, Yanhong & Li, Xiaoyu & Mao, Tianqin & Hu, Pengfei & Li, Xingcan & GuanWang,, 2022. "Mechanism modeling of optimal excess air coefficient for operating in coal fired boiler," Energy, Elsevier, vol. 261(PA).
    15. Simin Kheradmand & Nima Heidarzadeh & Seyed Hossein Kia, 2023. "Prediction of the CO2 emission across grassland and cropland using tower-based eddy covariance flux measurements: a machine learning approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(6), pages 5495-5509, June.
    16. Kaiyuan Zheng & Ying Zhang, 2023. "Prediction and Urban Adaptivity Evaluation Model Based on Carbon Emissions: A Case Study of Six Coastal City Clusters in China," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    17. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    18. Juan Luis Martín-Ortega & Javier Chornet & Ioannis Sebos & Sander Akkermans & María José López Blanco, 2024. "Enhancing Transparency of Climate Efforts: MITICA’s Integrated Approach to Greenhouse Gas Mitigation," Sustainability, MDPI, vol. 16(10), pages 1-35, May.
    19. Huang, Yufeng & Tao, Jun & Zhao, Junyi & Sun, Gang & Yin, Kai & Zhai, Junyi, 2023. "Graph structure embedded with physical constraints-based information fusion network for interpretable fault diagnosis of aero-engine," Energy, Elsevier, vol. 283(C).
    20. Saeed Salah & Husain R. Alsamamra & Jawad H. Shoqeir, 2022. "Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms," Energies, MDPI, vol. 15(7), pages 1-16, April.

    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:273:y:2023:i:c:s0360544223006217. 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.