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A Study on Sustainable Consumption of Fuel—An Estimation Method of Aircraft

Author

Listed:
  • Lisha Li

    (CAAC Academy, Civil Aviation Flight University of China, Guanghan 618307, China)

  • Shuming Yuan

    (School of Management, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an 710072, China)

  • Yue Teng

    (China Eastern Technology Application Research and Development Center Co., LTD, Shanghai 201707, China)

  • Jing Shao

    (School of Management, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an 710072, China)

Abstract

Though the development of China’s civil aviation and the improvement of control ability have strengthened the safety operation and support ability effectively, the airlines are under the pressure of operation costs due to the increase of aircraft fuel price. With the development of optimization controlling methods in flight management systems, it becomes increasingly challenging to cut down flight fuel consumption by control the flight status of the aircraft. Therefore, the airlines both at home and abroad mainly rely on the accurate estimation of aircraft fuel to reduce fuel consumption, and further reduce its carbon emission. The airlines have to take various potential factors into consideration and load more fuel to cope with possible negative situation during the flight. Therefore, the fuel for emergency use is called PBCF (Performance-Based Contingency Fuel). The existing PBCF forecasting method used by China Airlines is not accurate, which fails to take into account various influencing factors. This paper aims to find a method that could predict PBCF more accurately than the existing methods for China Airlines.This paper takes China Eastern Airlines as an example. The experimental data of flight fuel of China Eastern Airlines Co, Ltd. were collected to find out the relevant parameters affecting the fuel consumption, which is followed by the establishment of the LSTM neural network through the parameters and collected data. Finally, through the established neural network model, the PBCF addition required by the airline with different influencing factors is output. It can be seen from the results that the all the four models are available for the accurate prediction of fuel consumption. The amount of data of A319 is much larger than that of A320 and A330, which leads to higher accuracy of the model trained by A319. The study contributes to the calculation methods in the fuel-saving project, and helps the practitioners to learn about a particular fuel calculation method. The study brought insights for practitioners to achieve the goal of low carbon emission and further contributed to their progress towards circular economy.

Suggested Citation

  • Lisha Li & Shuming Yuan & Yue Teng & Jing Shao, 2021. "A Study on Sustainable Consumption of Fuel—An Estimation Method of Aircraft," Energies, MDPI, vol. 14(22), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7559-:d:677647
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    References listed on IDEAS

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    1. Mohamed, Zaid & Bodger, Pat, 2005. "Forecasting electricity consumption in New Zealand using economic and demographic variables," Energy, Elsevier, vol. 30(10), pages 1833-1843.
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