Prediction of methane yield and pretreatment efficiency of lignocellulosic biomass based on composition

•Regression models predict pretreatment efficiency based on raw biomass composition.•Best regressors for predicting methane yield and hydrolysis rate increase differed.•Ammonia treatment for maximizing methane yield is best for biomass poor in lipids.•Ammonia treatment for increasing hydrolysis rate...

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Veröffentlicht in:Waste management (Elmsford) 2023-01, Vol.155, p.302-310
Hauptverfasser: Lymperatou, Anna, Engelsen, Thor K., Skiadas, Ioannis V., Gavala, Hariklia N.
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Sprache:eng
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Zusammenfassung:•Regression models predict pretreatment efficiency based on raw biomass composition.•Best regressors for predicting methane yield and hydrolysis rate increase differed.•Ammonia treatment for maximizing methane yield is best for biomass poor in lipids.•Ammonia treatment for increasing hydrolysis rate is best for biomass rich in ash.•The methodology followed assisted on understanding global pretreatment mechanism. Lignocellulosic biomass is considered a key resource for the future expansion of biogas production through anaerobic digestion (AD), and research on the development of pretreatment technologies for improving biomass conversion is an intensive and fast-growing field. Consequently, there is a need for creating tools able to predict the efficiency of a certain pretreatment on different biomass types, fast and accurately, and to assist in selecting a pretreatment technology for a specific biomass. In this study, seven different types of raw lignocellulosic biomass of industrial relevance were systematically analyzed regarding their composition (carbohydrates, lignin, lipids, ash, extractives, etc.) and subjected to a common pretreatment. The aim of the study was to identify the most important characteristics that make a biomass good receptor of the specific pretreatment prior to AD. A simple ammonia pretreatment was chosen as a case study and partial least squares regression (PLS-R) was used for modeling initially the ultimate methane yield of raw and pretreated biomass. In the sequel, PLS-R was used for modeling the efficiency of the pretreatment on increasing the ultimate methane yield and hydrolysis rate as a function of the biomass composition. The fit of the models was satisfactory, ranging from R2 = 0.89 to R2 = 0.97. The results showed that the most decisive characteristics for predicting the efficiency of the pretreatment were the lipid (r = −0.88), ash (r = +0.79), protein (r = −0.61), and hemicellulose/lignin (r = −0.53) content of raw biomass. Finally, the approach followed in this study facilitated an improved understanding of the mechanism of the pretreatment and presented a methodology to be followed for developing tools for the prediction of pretreatment efficiency in the field of lignocellulosic biomass valorization.
ISSN:0956-053X
1879-2456
DOI:10.1016/j.wasman.2022.10.040