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Rapid characterization of SAF based on ATR-FTIR and machine learning models

Author

Listed:
  • Tao, Junyu
  • Zhu, Jingyu
  • Chen, Chao
  • Zhu, Xiaochao
  • Yan, Beibei
  • Cheng, Zhanjun
  • Chen, Guanyi

Abstract

Air transportation plays an emerging role in carbon emission of transportation section, leading to a worldwide attention about the development of SAF. Due to the heterogeneous feedstock supply of SAF, it is necessary to characterize SAF properties for quality control. The study proposes a rapid characterization method for SAF by integrating Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) with machine learning models. The input to the model consists of high-dimensional infrared spectral data. Key performance indicators for SAF include flash point, H/C ratio, LHV, C content, and H content. Model parameters were optimized based on prediction accuracy and correlation coefficients. A Support Vector Regression (SVR) model, an Artificial Neural Network (ANN) model, and a Random Forest (RF) model were established using Principal Component Analysis (PCA) and compared. The results showed that, under optimal parameters, the lowest Mean Relative Error (MRE) for flash point, H/C ratio, LHV, C content, and H content were 1.01 %, 3.34 %, 1.65 %, 0.57 %, and 2.97 %, respectively. The average R2 was 0.94. Additionally, the analysis of the optimal principal components (PC) in the infrared spectra revealed that PC10 and PC19 had the greatest impact on prediction performance.

Suggested Citation

  • Tao, Junyu & Zhu, Jingyu & Chen, Chao & Zhu, Xiaochao & Yan, Beibei & Cheng, Zhanjun & Chen, Guanyi, 2025. "Rapid characterization of SAF based on ATR-FTIR and machine learning models," Renewable Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:renene:v:253:y:2025:i:c:s0960148125011619
    DOI: 10.1016/j.renene.2025.123499
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    References listed on IDEAS

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