Mechanical pretreatment for increased biogas production from lignocellulosic biomass; predicting the methane yield from structural plant components

被引:61
|
作者
Tsapekos, Panagiotis [1 ]
Kougias, Panagiotis G. [1 ]
Angelidaki, Irini [1 ]
机构
[1] Tech Univ Denmark, Dept Environm Engn, Bld 113, DK-2800 Lyngby, Denmark
关键词
Biogas; Mechanical pretreatment; Methane yield; BMP prediction model; Principal component analysis; ANAEROBIC-DIGESTION; CHEMICAL-COMPOSITION; RAPID-DETERMINATION; CROP ROTATIONS; POTENTIAL BMP; ENERGY CROPS; WASTE; BIODEGRADABILITY; GRASS; SPECTROSCOPY;
D O I
10.1016/j.wasman.2018.07.017
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Lignocellulosic substrates are associated with limited biodegradability due to the structural complexity. For that reason, a pretreatment step is mandatory for efficient biomass transformation which will lead to increased bioenergy output. The aim of the present study was to assess the efficiency of two pretreatment machines to enhance the methane yield of meadow grass. Specifically, the application of shearing forces with a rotated plastic sweeping brush against a steel roller significantly increased biomass biodegradability by 20% under relatively gentle operation conditions (600 rpm). The more intense operation (1200 rpm) was not associated with higher methane yield enhancement. Regarding an alternative machine, in which the brush was replaced with a coarse steel roller resulted in a more distinct effect (+27%) despite the lower rotating speed (similar to 400 rpm). Moreover, the association of the substrate's individual chemical components and the practical methane yield was assessed, establishing single and multiple linear regression models. However, the estimation accuracy was rather low with either single (regressor: lignin, R-2: 0.50) or multiple linear regression analyses (regressors: arabinan-lignin-protein, R-2: 0.61). Results showed that poorly lignified plant tissue containing relatively high fractions of protein and arabinan is more susceptible to anaerobic digestion. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:903 / 910
页数:8
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