Influence of chemical composition on biochemical methane potential of fruit and vegetable waste. (January 2018)

• Record Type: Journal Article
• Authors: Edwiges, Thiago; Frare, Laercio; Mayer, Bruna; Lins, Leonardo; Mi Triolo, Jin; Flotats, Xavier; de Mendonça Costa, Mônica Sarolli Silva
• Is Part Of: Waste management. Volume 71(2018)
• Page Start: 618
• Page End: 625
• Publication Date: 2018-01
• Subjects: Anaerobic digestion -- Biodegradability -- Biogas -- BMP model -- Lignocellulose; Hazardous wastes -- Periodicals; Refuse and refuse disposal -- Periodicals; 363.728
• DOI: 10.1016/j.wasman.2017.05.030
• Languages: English
• ISSNs: 0956-053X

Abstract

Highlights: Different mixtures of fruit and vegetable waste collected along one year were analysed. Chemical composition showed great variation over time. BMP ranged from 288 to 516 LN CH4 kg VS −1 with 79% biodegradability average. Lipid and high calorific value is well correlated to BMP. Multiple regression models show good ability to predict methane potential. Abstract: This study investigates the influence of chemical composition on the biochemical methane potential (BMP) of twelve different batches of fruit and vegetable waste (FVW) with different compositions collected over one year. BMP ranged from 288 to 516 LN CH4 kg VS −1, with significant statistical differences between means, which was explained by variations in the chemical composition over time. BMP was most strongly correlated to lipid content and high calorific values. Multiple linear regression was performed to develop statistical models to more rapidly predict methane potential. Models were analysed that considered chemical compounds and that considered only high calorific value as a single parameter. The best BMP prediction was obtained using the statistical model that included lipid, protein, cellulose, lignin, and high calorific value (HCV), with R 2 of 92.5%; lignin was negatively correlated to methane production. Because HCV and lipids are strongly correlated, and because HCV can be determined more rapidly than overall chemical composition, HCV may be useful for predicting BMP.