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

Jinming Liu, Changhao Zeng, Na Wang, Jianfei Shi, Bo Zhang, Changyu Liu and Yong Sun
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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
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

Energies, 2021, vol. 14, issue 5, 1-17

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.

Keywords: anaerobic co-digestion; biochemical methane potential; near infrared spectroscopy; characteristic wavelengths; partial least squares (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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