Comparative analysis of prediction models for methane potential based on spent edible fungus substrate. (December 2020)

• Record Type: Journal Article
• Authors: Xu, Yonghua; Awasthi, Mukesh Kumar; Li, Pengfei; Meng, Xianghui; Wang, Zhi
• Is Part Of: Bioresource technology. Volume 317(2020)
• Publication Date: 2020-12
• Subjects: Prediction model -- Spent edible fungus -- Anaerobic digestion -- Methane potential -- Lignocellulose; 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.2020.124052
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: The BMP of SEF substrates with different characteristics was evaluated. The regression model with more chemical compositions had good correlation for P0. The minimum digestive testing time (14 days) was determined in all kinetic models. The lignocellulosic prediction model based on SEF substrates was more convincing. Abstract: In this study, ten spent edible fungus (SEF) with different compositional features were used for the maximum methanogenic potential ( P 0 ) evaluation, and the prediction models including regression and kinetics based on this were developed separately. The results showed that the regression model with more chemical components had a good correlation with the P 0, and at least three chemical compositions could reach the threshold of sensitivity. The Cone model showed the best fitting effect on P 0 in all kinetic models, which had higher R-square (>0.994) and lower error (1.004–5.672%). Meanwhile, the minimum digestive testing time (14 days) was determined by the evaluation of sensitivity via statistical indicators. It is concluded that the determination of the prediction model of P 0 should be evaluated with the combination of statistical indicators and specific requirements.