Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2022
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Hao Jiang Shuangqing Wang Baochen Li Lu Feng Limei Zhai Hongjun Zhou Yeqing Li Junting PanSammendrag
Det er ikke registrert sammendrag
Forfattere
Lu Feng Mihaela Tanase Opedal Francesca Di Bartolomeo Sidsel Markussen Aniko Varnai Svein Jarle HornSammendrag
Det er ikke registrert sammendrag
Forfattere
Lu FengSammendrag
Det er ikke registrert sammendrag
Sammendrag
Proper treatment of polyvinyl chloride (PVC) waste is challenge as it is not easily degraded and incineration can lead to environmental issue as it will produce toxic chemicals. In this study, a hydrothermal carbonization approach was applied to treat PVC waste. The influence of exogenous additives on dechlorination efficiency of PVC were evaluated. The results showed that, with exogenous additive, substitution, elimination, dehydration and aromatization reaction were enhanced during hydrothermal carbonization. The maximum dechlorination efficiency of 97.50% was achieved with the mass ratio of 1.4% between rice straw and PVC resin at hydrothermal carbonization temperature 240℃ for 120min. The calorific value of hydrothermal charcoal was relatively higher (39.57MJ/kg ± 0.40MJ/kg), indicating a good combustion process. This study presented a novel and sustainable approach, which could convert PVC-waste as a form of solid fuel.
Forfattere
Lu Feng Lise Bonne Guldberg Michael Jørgen Hansen Chun Ma Rikke Vinther Ohrt Henrik Bjarne MøllerSammendrag
Det er ikke registrert sammendrag
Forfattere
Lu Feng Lise Bonne Guldberg Michael Jørgen Hansen Chun Ma Rikke Vinther Ohrt Henrik Bjarne MøllerSammendrag
Anaerobic digestion of animal slurry to produce biogas is the dominated treatment approach and a storage period is normally applied prior to digestion. Pre-storage, however, contributes to CH4 emissions and results in loss of biogas potential. Manure management was found to be an efficient approach to reduce not only the on-site CH4 emission but may also have extended influence on CH4 emission/losses for storage and subsequent biogas process, while the connection remains unclear. The objective of this study was therefore to evaluate the impact of slurry management (e.g. removal frequency) on CH4 emission (both on-site and storage process prior to biogas) and biogas yield. An experimental pig house for growing-finishing pigs (30–110 kg) and the relevant CH4 emission was monitored for one year. In addition, the specific CH4 activity (SMA) test was conducted and used as an alternative indicator to reflect the impact. Results showed that the manure management affected both on-site and subsequent methane emission; with increased manure removal frequencies, the methane emission became less dependent on variation of temperatures and the specific methanogenesis activity was significantly lower. The highest SMA (100 mL CH4 gVS-1), for instance, was observed from the slurries with limited emptied times, which was 10 times of that from the slurries being emptied three times a week. These findings could enlighten the development of environmentally friendly strategies for animal slurry management and biogas production.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Yeqing Li Zhangmu Jing Junting Pan Gang Luo Lu Feng Hao Jiang Hongjun Zhou Quan Xu Yanjuan Lu Hongbin LiuSammendrag
Due to the diversity of microbiota and the high complexity of their interactions that mediate biogas production, a detailed understanding of the microbiota is essential for the overall stability and performance of the anaerobic digestion (AD) process. This study evaluated the microbial taxonomy, metabolism, function, and genetic differences in 14 full-scale biogas reactors and laboratory reactors operating under various conditions in China. This is the first known study of the microbial ecology of AD at food waste (FW) at a regional scale based on multi-omics (16S rRNA gene amplicon sequencing, metagenomics, and proteomics). Temperature significantly affected the bacterial and archaeal community structure (R2 = 0.996, P = 0.001; R2 = 0.846, P < 0.002) and total inorganic carbon(TIC) slightly changed the microbial structure (R2 = 0.532, P = 0.005; R2 = 0.349, P = 0.016). The Wood-Ljungdahl coupled with hydrogenotrophic methanogenic pathways were dominant in the thermophilic reactors, where the acs, metF, cooA, mer, mch and ftr genes were 10.1-, 2.8-, 16.2-, 1.74-, 4.15-, 1.04-folds of the mesophilic reactors (P < 0.01). However, acetoclastic and methylotrophic methanogenesis was the primary pathway in the mesophilic reactors, where the ackA, pta, cdh and mta genes were 2.2-, 3.2-, 14.3-, 1.88-folds of the thermophilic group (P < 0.01). Finally, the Wilcoxon rank-sum test was applied to explain the cause of the temperature affecting AD microbial activities. The findings have deepened the understanding of the effect of temperature on AD microbial ecosystems and are expected to guide the construction and management of full-scale FW biogas plants.
Forfattere
Yi Zhang Linhui Li Zhonghao Ren Yating Yu Yeqing Li Junting Pan Yanjuan Lu Lu Feng Weijin Zhang Yongming HanSammendrag
The parameters from full-scale biogas plants are highly nonlinear and imbalanced, resulting in low prediction accuracy when using traditional machine learning algorithms. In this study, a hybrid extreme learning machine (ELM) model was proposed to improve prediction accuracy by solving imbalanced data. The results showed that the best ELM model had a good prediction for validation data (R2 = 0.972), and the model was developed into the software (prediction error of 2.15 %). Furthermore, two parameters within a certain range (feed volume (FV) = 23–45 m3 and total volatile fatty acids of anaerobic digestion (TVFAAD) = 1750–3000 mg/L) were identified as the most important characteristics that positively affected biogas production. This study combines machine learning with data-balancing techniques and optimization algorithms to achieve accurate predictions of plant biogas production at various loads.