IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v271y2023ics0360544223004206.html
   My bibliography  Save this article

Machine learning based approach for forecasting emission parameters of mixed flow turbofan engine at high power modes

Author

Listed:
  • Aygun, Hakan
  • Dursun, Omer Osman
  • Toraman, Suat

Abstract

To predict aircraft emissions from their own features has become more important as the usage field of aviation engines is extended to different sectors for different purposes. In the present study, thanks to by-pass ratio, pressure ratio and rated thrust of the one hundred sixty Mixed-Flow Turbofan (MFT) engines, NOx and CO emission indices as well as fuel flow for take-off and climb-out phases are predicted by using conventional Long-Short Term Memory (LSTM) at initial stage. After that, to improve the obtained estimations, hybrid Convolutional Neural Network (CNN-LSTM) model is employed to these engine dataset. The differences between two methods are presented by several types of error for each parameter. When considering the correlations, the relation between three variables and CO emission index is opposite whereas it is positively for NOx index. Moreover, NOx emission index of the MFT engines is predicted with 0.8166 of R2 by LSTM whereas its R2 increases to 0.8991 by means of hybrid CNN-LSTM approach. Amongst the models, the fuel flow is predicted the highest more than 0.95 of R2 whereas hybrid model makes it predictable with the higher accuracy, which has more than 0.99 of R2 for both phases. Moreover, these enhancements are observed in predicting of the other parameters regarding MFTs. It is thought that the proposed CNN-LSTM model could help in estimation of important parameters of gas turbine engines with higher level of accuracy.

Suggested Citation

  • Aygun, Hakan & Dursun, Omer Osman & Toraman, Suat, 2023. "Machine learning based approach for forecasting emission parameters of mixed flow turbofan engine at high power modes," Energy, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:energy:v:271:y:2023:i:c:s0360544223004206
    DOI: 10.1016/j.energy.2023.127026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223004206
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127026?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. Zha, Wenshu & Liu, Yuping & Wan, Yujin & Luo, Ruilan & Li, Daolun & Yang, Shan & Xu, Yanmei, 2022. "Forecasting monthly gas field production based on the CNN-LSTM model," Energy, Elsevier, vol. 260(C).
    2. Mingliang Bai & Jinfu Liu & Yujia Ma & Xinyu Zhao & Zhenhua Long & Daren Yu, 2020. "Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine," Energies, MDPI, vol. 14(1), pages 1-22, December.
    3. Aygun, Hakan & Turan, Onder, 2023. "Analysis of cruise conditions on energy, exergy and NOx emission parameters of a turbofan engine for middle-range aircraft," Energy, Elsevier, vol. 267(C).
    4. Park, Yeseul & Choi, Minsung & Kim, Kibeom & Li, Xinzhuo & Jung, Chanho & Na, Sangkyung & Choi, Gyungmin, 2020. "Prediction of operating characteristics for industrial gas turbine combustor using an optimized artificial neural network," Energy, Elsevier, vol. 213(C).
    5. Chung, Won Hee & Gu, Yeong Hyeon & Yoo, Seong Joon, 2022. "District heater load forecasting based on machine learning and parallel CNN-LSTM attention," Energy, Elsevier, vol. 246(C).
    6. Tokuslu, Aydin, 2020. "Estimation of aircraft emissions at Georgian international airport," Energy, Elsevier, vol. 206(C).
    7. 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.
    8. Nicola Menga & Akhila Mothakani & Maria Grazia De Giorgi & Radoslaw Przysowa & Antonio Ficarella, 2022. "Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine," Energies, MDPI, vol. 15(19), pages 1-22, October.
    9. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(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. Tang, Zhenhao & Sui, Mengxuan & Wang, Xu & Xue, Wenyuan & Yang, Yuan & Wang, Zhi & Ouyang, Tinghui, 2024. "Theory-guided deep neural network for boiler 3-D NOx concentration distribution prediction," Energy, Elsevier, vol. 299(C).
    2. Kirmizi, Mehmet & Aygun, Hakan & Turan, Onder, 2024. "Energetic and exergetic metrics of a cargo aircraft turboprop propulsion system by using regression method for dynamic flight," Energy, Elsevier, vol. 296(C).
    3. Kirmizi, Mehmet & Aygun, Hakan & Turan, Onder, 2023. "Performance and energy analysis of turboprop engine for air freighter aircraft with the aid of multiple regression," Energy, Elsevier, vol. 283(C).
    4. Wang, Xin & Liu, Xiang & Bai, Yun, 2024. "Prediction of the temperature of diesel engine oil in railroad locomotives using compressed information-based data fusion method with attention-enhanced CNN-LSTM," Applied Energy, Elsevier, vol. 367(C).
    5. Ekici, Selcuk & Ayar, Murat & Orhan, Ilkay & Karakoc, Tahir Hikmet, 2024. "Cruise altitude patterns for minimizing fuel consumption and emission: A detailed analysis of five prominent aircraft," Energy, Elsevier, vol. 295(C).
    6. Ekici, Selcuk & Ayar, Murat & Hikmet Karakoc, T., 2023. "Fuel-saving and emission accounting: An airliner case study for green engine selection," Energy, Elsevier, vol. 282(C).
    7. Chen, Guisheng & Sun, Min & Li, Junda & Wang, Jiguang & Shen, Yinggang & Liang, Daping & Xiao, Renxin, 2024. "Study on high-altitude ceiling strategy of compression ignition aviation piston engines based on BP-NSGA II algorithm optimization," Energy, Elsevier, vol. 294(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. He, Honglin & Yang, Xiaojun & Li, Chunyang & Teng, Jinfang, 2024. "Numerical analysis of the landing and take-off cycle standard for supersonic engines based on pollutant emission characteristics," Energy, Elsevier, vol. 299(C).
    2. Wang, Yuqi & Liu, Tianyuan & Meng, Yue & Zhang, Di & Xie, Yonghui, 2022. "Integrated optimization for design and operation of turbomachinery in a solar-based Brayton cycle based on deep learning techniques," Energy, Elsevier, vol. 252(C).
    3. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    4. Chen Zhang & Tao Yang, 2023. "Anomaly Detection for Wind Turbines Using Long Short-Term Memory-Based Variational Autoencoder Wasserstein Generation Adversarial Network under Semi-Supervised Training," Energies, MDPI, vol. 16(19), pages 1-18, October.
    5. Bahri, Bahram & Shahbakhti, Mahdi & Aziz, Azhar Abdul, 2017. "Real-time modeling of ringing in HCCI engines using artificial neural networks," Energy, Elsevier, vol. 125(C), pages 509-518.
    6. Fan, Siyuan & Wang, Yu & Cao, Shengxian & Zhao, Bo & Sun, Tianyi & Liu, Peng, 2022. "A deep residual neural network identification method for uneven dust accumulation on photovoltaic (PV) panels," Energy, Elsevier, vol. 239(PD).
    7. Xiurui Guo & Chunxiao Ning & Yaqian Shen & Chang Yao & Dongsheng Chen & Shuiyuan Cheng, 2023. "Projection of the Co-Reduced Emissions of CO 2 and Air Pollutants from Civil Aviation in China," Sustainability, MDPI, vol. 15(9), pages 1-23, April.
    8. Feng, Zhanyu & Zhang, Jian & Jiang, Han & Yao, Xuejian & Qian, Yu & Zhang, Haiyan, 2024. "Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework," Energy, Elsevier, vol. 302(C).
    9. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    10. Mingliang Bai & Jinfu Liu & Yujia Ma & Xinyu Zhao & Zhenhua Long & Daren Yu, 2020. "Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine," Energies, MDPI, vol. 14(1), pages 1-22, December.
    11. Aygun, Hakan & Cilgin, Mehmet Emin & Ekmekci, Ismail & Turan, Onder, 2020. "Energy and performance optimization of an adaptive cycle engine for next generation combat aircraft," Energy, Elsevier, vol. 209(C).
    12. Wang, Pengfei & Zhang, Jiaxuan & Wan, Jiashuang & Wu, Shifa, 2022. "A fault diagnosis method for small pressurized water reactors based on long short-term memory networks," Energy, Elsevier, vol. 239(PC).
    13. Chen, Yu-Zhi & Tsoutsanis, Elias & Xiang, Heng-Chao & Li, Yi-Guang & Zhao, Jun-Jie, 2022. "A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions," Applied Energy, Elsevier, vol. 317(C).
    14. Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).
    15. Lyu, Chen & Liu, Xiaoman & Wang, Zhen & Yang, Lu & Liu, Hao & Yang, Nan & Xu, Shaodong & Cao, Libin & Zhang, Zhe & Pang, Lingyun & Zhang, Li & Cai, Bofeng, 2023. "An emissions inventory using flight information reveals the long-term changes of aviation CO2 emissions in China," Energy, Elsevier, vol. 262(PB).
    16. Wang, Yuqi & Du, Qiuwan & Li, Yunzhu & Zhang, Di & Xie, Yonghui, 2022. "Field reconstruction and off-design performance prediction of turbomachinery in energy systems based on deep learning techniques," Energy, Elsevier, vol. 238(PB).
    17. Huang, Guizao & Wu, Guangning & Yang, Zefeng & Chen, Xing & Wei, Wenfu, 2023. "Development of surrogate models for evaluating energy transfer quality of high-speed railway pantograph-catenary system using physics-based model and machine learning," Applied Energy, Elsevier, vol. 333(C).
    18. Ekici, Selcuk & Ayar, Murat & Orhan, Ilkay & Karakoc, Tahir Hikmet, 2024. "Cruise altitude patterns for minimizing fuel consumption and emission: A detailed analysis of five prominent aircraft," Energy, Elsevier, vol. 295(C).
    19. Wang, Chang & Zheng, Jianqin & Liang, Yongtu & Wang, Bohong & Klemeš, Jiří Jaromír & Zhu, Zhu & Liao, Qi, 2022. "Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines," Energy, Elsevier, vol. 261(PB).
    20. Wen, Jie & Wan, Chenxi & Xu, Guoqiang & Zhuang, Laihe & Dong, Bensi & Chen, Junjie, 2024. "Optimization of thermal management system architecture in hydrogen engine employing improved genetic algorithm," Energy, Elsevier, vol. 297(C).

    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:271:y:2023:i:c:s0360544223004206. 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.