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Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics

Author

Listed:
  • Jinming Liu

    (College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China)

  • Changhao Zeng

    (College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China)

  • Na Wang

    (College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China)

  • Jianfei Shi

    (College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China)

  • Bo Zhang

    (College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
    Key Laboratory of Technology and Model for Cyclic Utilization from Agricultural Resources, Ministry of Agriculture and Rural, Beijing 100125, China)

  • Changyu Liu

    (College of Architecture and Civil Engineering, Northeast Petroleum University, Daqing 163318, China)

  • Yong Sun

    (College of Engineering, Northeast Agricultural University, Harbin 150030, China)

Abstract

Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering.

Suggested Citation

  • Jinming Liu & Changhao Zeng & Na Wang & Jianfei Shi & Bo Zhang & Changyu Liu & Yong Sun, 2021. "Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics," Energies, MDPI, vol. 14(5), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1460-:d:512345
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    References listed on IDEAS

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    1. Peng, Wei & Beggio, Giovanni & Pivato, Alberto & Zhang, Hua & Lü, Fan & He, Pinjing, 2022. "Applications of near infrared spectroscopy and hyperspectral imaging techniques in anaerobic digestion of bio-wastes: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    2. 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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