Biochemical methane potential prediction for mixed feedstocks of straw and manure in anaerobic co-digestion. (April 2021)

• Record Type: Journal Article
• Authors: Yang, Gaixiu; Li, Ying; Zhen, Feng; Xu, Yonghua; Liu, Jinming; Li, Nan; Sun, Yong; Luo, Lina; Wang, Ming; Zhang, Lingling
• Is Part Of: Bioresource technology. Volume 326(2021)
• Publication Date: 2021-04
• Subjects: Anaerobic co-digestion -- Biochemical methane potential -- Multivariate linear regression -- Partial least squares -- Near-infrared spectroscopy; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2021.124745
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: Biochemical methane potential was rapidly estimated in anaerobic co-digestion. Mean relative errors of models based on physicochemical indexes were under 3.1%. Mean relative error of model based on near infrared spectroscopy was 2.333%. Model based on near-infrared spectroscopy had the highest predicted accuracy. Abstract: 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 co-digestion (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.