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Fast Quantitative Modelling Method for Infrared Spectrum Gas Logging Based on Adaptive Step Sliding Partial Least Squares

Author

Listed:
  • Zhongbing Li

    (School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China)

  • Wei Pang

    (School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China)

  • Haibo Liang

    (School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Guihui Chen

    (School of Engineering, Southwest Petroleum University, Nanchong 637000, China)

  • Hongming Duan

    (School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China)

  • Chuandong Jiang

    (School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China)

Abstract

Infrared spectroscopy (IR) quantitative analysis technology has shown excellent development potential in the field of oil and gas logging. However, due to the high overlap of the IR absorption peaks of alkane molecules and the offset of the absorption peaks in complex environments, the quantitative analysis of IR spectroscopy applied in the field puts forward higher requirements for modelling speed and accuracy. In this paper, a new type of fast IR spectroscopy quantitative analysis method based on adaptive step-sliding partial least squares (ASS-PLS) is designed. A sliding step control function is designed to change the position of the local PLS analysis model in the full spectrum band adaptively based on the relative change of the current root mean square error and the global minimum root-mean-square error for rapid modelling. The study in this paper reveals the influence of the position and width of the local modelling window on the performance, and how to quickly determine the optimal modelling window in an uncertain sample environment. The performance of the proposed algorithm has been compared with three typical quantitative analysis methods by experiments on an IR spectrum dataset of 400 alkane samples. The results show that this method has a fast quantitative modelling speed with high analysis accuracy and stability. It has important practical value for promoting IR spectroscopy gas-logging technology.

Suggested Citation

  • Zhongbing Li & Wei Pang & Haibo Liang & Guihui Chen & Hongming Duan & Chuandong Jiang, 2022. "Fast Quantitative Modelling Method for Infrared Spectrum Gas Logging Based on Adaptive Step Sliding Partial Least Squares," Energies, MDPI, vol. 15(4), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1325-:d:747632
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    Cited by:

    1. Baishao Zhan & Peng Li & Ming Li & Wei Luo & Hailiang Zhang, 2023. "Detection of Soluble Solids Content (SSC) in Pears Using Near-Infrared Spectroscopy Combined with LASSO–GWF–PLS Model," Agriculture, MDPI, vol. 13(8), pages 1-15, July.

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