Biochemical methane potential prediction for mixed feedstocks of straw and manure in anaerobic co-digestion

To rapidly estimate the biochemical methane potential (BMP) of feedstocks, different multivariate regression models were established between BMP and the physicochemical indexes or near-infrared spectroscopy (NIRS). Mixed fermentation feedstocks of corn stover and livestock manure were rapidly detected BMP in anaerobic codigestion (co-AD). The results showed that the predicted accuracy of NIRS model based on characteristic wavelengths selected by multiple competitive adaptive reweighted sampling outperformed all regression models based on the physicochemical indexes. For the NIRS regression model, coefficient of determination, root mean squares error, relative root mean squares error, mean relative error and residual predictive deviation of the validation set were 0.982, 6.599, 2.713%, 2.333% and 7.605. The results reveal that the predicted accuracy of NIRS model is very high, and meet the requirements of rapid prediction of BMP for co-AD feedstocks in practical biogas engineering.

Automated summary

The study found that the regression model based on characteristic wavelengths selected by NIRS showed superior performance in predicting biochemical methane potential. The predicted accuracy of the NIRS model was very high, meeting the requirements for rapid prediction of BMP for co-AD feedstocks in practical biogas engineering.