Assessing microbial stability and predicting biogas production in full-scale thermophilic dry methane fermentation of municipal solid waste

被引:0
|
作者
Sato, Yuya [1 ]
Hasemi, Kentaro [2 ]
Machikawa, Kazunori [3 ]
Kinjo, Hisato [3 ]
Yashiro, Naohisa [3 ]
Iimura, Yosuke [1 ]
Aoki, Hiroshi [1 ]
Habe, Hiroshi [1 ]
机构
[1] Natl Inst Adv Ind Sci & Technol, Environm Management Res Inst, 16-1 Onogawa, Tsukuba, Ibaraki 3058569, Japan
[2] Kagawa Prefectural Ind Technol Ctr, 587-1 Goto Cho, Takamatsu, Kagawa 7618031, Japan
[3] Fuji Clean Co Ltd, 2994-1 Yamadashimo,Ayagawacho, Ayagawa, Kagawa 7612204, Japan
关键词
Gradient boosting decision tree; High-throughput sequencing; Machine learning; Microbial community analysis; Statistical analysis; ANAEROBIC-DIGESTION;
D O I
10.1016/j.biortech.2024.130766
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
Compared to typical anaerobic digestion processes, little is known about both sludge microbial compositions and biogas production models for full-scale dry methane fermentation treating municipal solid waste (MSW). The anaerobic sludge composed of one major hydrogenotrophic methanogen (Methanoculleus) and syntrophic acetate oxidizing bacteria (e.g., Caldicoprobacter), besides enrichment of MSW degraders such as Clostridia. The core population remained phylogenetically unchanged during the fermentation process, regardless of amounts of MSW supplied (similar to 35 ton/d) or biogas produced (similar to 12000 Nm(3)/d). Based on the correlations observed between feed amounts of MSW from 6 days in advance to the current day and biogas output (the strongest correlation: r = 0.77), the best multiple linear regression (MLR) model incorporating the temperature factor was developed with a good prediction for validation data (R-2 = 0.975). The proposed simple MLR method with only data on the feedstock amounts will help decision-making processes to prevent low-efficient biogas production.
引用
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页数:10
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