基于GA-SVM的近红外光谱法预测有机废弃物生化甲烷潜力

doi:10.3964/j.issn.1000-0593(2020)06-1857-05

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Abstract Anaerobic fermentation technology is one of the most promising technologies for the utilization of organic waste resources. Its research and utilization have been widely carried out at home and abroad. Usually, biochemical methane potential (BMP) is used to represent the anaerobic degradation of the material in the anaerobic degradation technology of organic waste. The traditional measuring methods of BMP, are usually expensive and time-consuming. Therefore, near-infrared spectroscopy is proposed to rapid predict the biochemical methane potential (BMP) of organic waste in this paper. And genetic algorithm (GA) combined with support vector machine (SVM) is applied to establish a functional model to predict the biochemical methane potential of organic waste. 64 samples of aquatic plants and algae are collected from the south and east of China. The original BMP data of samples were obtained from the experimental scale digesbers. At the same time, near-infrared spectral data are obtained by Fourier transform near-infrared spectrometer. First of all, the prediction models were developed by the principal component regression, partial least squares, recursive exponential partial least squares (RPLS) on the pre-processed data, respectively. The aim is to connect the original BMP date with the spectral data and realize the rapid prediction of aquatic plants and algae BMP. The results show that the RPLS method on the full spectral can solve the problem of poor robustness and the poor data interference caused by the traditional PLS method. Although this method improves the robustness of the model, it has slow response speed and low computational efficiency. Therefore, we proposed a genetic algorithm (GA) combined with support vector machine (SVM) method, which is suitable for small sample cases, has good global search ability, and also avoids the traditional process from induction to deduction, and eliminates a lot of redundant sample information. In summary, the GA-SVM method is simple, and it has good stability. Combined with the band assignment of the near-infrared spectrum, it could know that the 1 404 characteristic wavelength points were selected，and roughly divided into 3 representative bands by genetic algorithm (GA), so we built the regression model by support vector machines on the selected characteristic bands. According to the results of model evaluation, it is known that the prediction model based on GA-SVM not only simplifies the date scale, but also improves the prediction accuracy. The root mean square error of prediction (RMSEP) is 10.32 mL, the coefficient of determination (R²) is 0.92; the residual prediction deviation (RPD) is 6.56. Compared with the models PLS and RPLS, the RMSEP was decreased by 19.56 and 14.81 mL respectively; the R² increased by 0.06 and 0.04, the RPD increased by 4.31，3.85 respectively. The results show that the NIRS model based on GA-SVM can predict the biochemical methane potential of organic waste rapidly and has higher accuracy, it can replace the traditional BMP determination method to meet the needs of rapid detection.

Key words：Infrared spectroscopy; Organic waste; Biochemical methane polontial; Algorithm; Support vector machine

Received: 2018-11-19 Accepted: 2019-04-08

ZTFLH:

TM571.2

Corresponding Authors: YAO Yan E-mail: yaoyan@cjlu.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)06-1857-05 OR https://www.gpxygpfx.com/EN/Y2020/V40/I06/1857