Detection of Sulfur Content in Vessel Fuel Based on Hyperspectral
Imaging Technology
LIU Gang1, LÜ Jia-ming1, NIU Wen-xing1, LI Qi-feng2, ZHANG Ying-hu2, YANG Yun-peng2, MA Xiang-yun2*
1. Tianjin Enpromi Environmental Protection Technology Co., Ltd., Tianjin 300457, China
2. School of Precision Instrument and Opto-Electronics Engineering, Tianjin University，Tianjin 300072, China
Abstract：In port transportation, the detection of sulfur content in vessel fuel has always been the focus of the maritime sector. However, the existing detection methods are tough to achieve remote monitoring effectively. This paper proposes a remote measuring method of CO2 and SO2 in vessel exhaust based on hyperspectral imaging technology. The Fourier transform interference hyperspectral imaging technology, and hyperspectral three-dimensional low-rank optimization technology is developed, which can effectively solve the low-concentration detection problems in open light path and complex environment. We perform the sulfur content detection tests for the fixed and random vessel day and night at Dongjiang Wharf of Tianjin Port. The Fourier transform interference hyperspectral imaging technology has been developed for remote exhaust detection under the complex open light paths. The hyperspectral data is preprocessed by hyperspectral imaging three-dimensional low-rank optimization technology. Finally, the accurate detection of sulfur content in vessel fuel has been analyzed accurately using partial least square (PLS) stoichiometry technology and a radiation transmission model according to the atmospheric radiation situation. The detection of fixed and random vessels filled with high sulfur fuel in day and night environments is accomplished. The detection distance is more than 1000 meters, and the single detection time is less than 3 s. The results are consistent with the data held by the maritime department, which verifies the feasibility of remote passive measurement of sulfur content in vessel fuel based on hyperspectral imaging technology.
刘 罡，吕佳明，牛文兴，李奇峰，张迎虎，杨云鹏，马翔云. 高光谱成像的船舶燃油硫含量遥测技术[J]. 光谱学与光谱分析, 2023, 43(06): 1697-1702.
LIU Gang, LÜ Jia-ming, NIU Wen-xing, LI Qi-feng, ZHANG Ying-hu, YANG Yun-peng, MA Xiang-yun. Detection of Sulfur Content in Vessel Fuel Based on Hyperspectral
Imaging Technology. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1697-1702.
[1] Van T C, Ramirez J, Rainey T, et al. Transportation Research Part D: Transport and Environment, 2019, 70: 123.
[2] Wan Z, Zhou X, Zhang Q, et al. Marine Policy, 2021, 130(4): 104584.
[3] SHI Hua-jie, AN Bo-wen, PAN Sheng-da, et al(史华杰, 安博文, 潘胜达, 等). Automation & Instrumentation(自动化与仪表), 2018, 33(6): 65.
[4] Che W, Noor M M, Mamat R. Renewable and Sustainable Energy Reviews, 2018, 94: 127.
[5] QIU Rong-chao, LOU Shu-li, LI Ting-jun, et al(仇荣超，娄树理，李廷军，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(3): 698.
[6] YANG Tian-tian, WEN Yuan-qiao, HUANG Liang, et al(杨甜甜, 文元桥, 黄 亮, 等). Navigation of China(中国航海), 2020, 43(1): 109.
[7] Campbell J F, Lin B, Dobler J, et al. Earth and Space Science, 2020, 7(12): 10.1029/2019EA000847.
[8] Nehrir A R, Kiemle C, Lebsock M D, et al. Surveys in Geophysics, 2017, 38(1-2): 1445.
[9] Madkhali N, Alqahtani H R, Al-Terary S, et al. Optical & Quantum Electronics, 2019, 51(7): 227.1.
[10] Ma Y, Fan G, Jin Q, et al. IEEE Geoscience and Remote Sensing Letters, 2020, (99): 1.
[11] Kastek M, Piatkowski T, Dulski R, et al. Photonics Letters of Poland, 2012, 4(4): 146.
[12] Gagnon M A, Gagnon J P, Tremblay P, et al. Proc. SPIE, 2016, 9988: 998806.
[13] Wei C, Li Q, Zhang X, et al. Canadian Journal of Remote Sensing, 2021, 47(4): 588.
[14] Li Q, Wang Y, Ma X, et al. Measurement Science and Technology, 2018, 29(9): 095401.
[15] Zhu Y M, Tang X J, Li T, et al. Ocean Engineering, 2016, 128: 124.
[16] CHEN Hong-zhan, HE Jun-jie, CHEN Jun-wen, et al(陈鸿展, 何俊杰, 陈俊文, 等). Acta Scientiae Circumstantiae(环境科学学报), 2020, 40(6): 15.
[17] Sher F, Iqbal S Z, Liu H, et al. Energy Conversion & Management, 2020, 203: 112266.
