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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

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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.

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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.

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The amount of lignocellulose biomass and sludge is enormous, so it is of great significance to find a treatment combining the two substances. Co-hydrothermal carbonization (Co-HTC) has emerged as an efficient approach to dispose sludge. However, the improvement of sludge upgrading and combustion performance remains an important challenge during the Co-HTC of sludge. In this work, the Co-HTC of sludge and Fenton's reagent at different mixing ratios was proposed to achieve sludge reduction. Moreover, the addition of two kinds of biomass improved the adsorption capacity and combustion performance of hydrochars. When sludge and sawdust were the Co-HTC at the mass ratio of 1:3, the liquid phase Pb concentration decreased notably to 18.06%. Furthermore, the adsorption capacity of hydrochars was further improved by modification, which was in accordance with pseudo-second-order kinetics. Particularly, the hydrochars derived from the Co-HTC had higher heating value (HHV) and could be used as a clean fuel. This study proposed a new technical route of combining the HTC with Fenton's reagent and lignocellulose biomass, which could be served as a cleaner and eco-friendly treatment of sludge.

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Bioretention cells are popular stormwater management systems for controlling peak runoff and improving runoff water quality. A case study on a functional large-scale bioretention cell and a laboratory column experiment was conducted to evaluate the concentrations and retention efficiency of bioretention cells towards tire wear particles (TWP). The presence of TWP was observed in all soil fractions (<50 µm, 50–100 µm, 100–500 µm, and >500 µm) of the functional bioretention cell. TWP concentrations were higher (30.9 ± 4.1 mg/g) close to the inlet to the bioretention cell than 5 m away (19.8 ± 2.4 mg/g), demonstrating the influence of the bioretention cell design. The column experiment showed a high retention efficiency of TWP (99.6 ± 0.5%) in engineered soil consisting of sand, silty-sand, and garden waste compost. This study confirmed that bioretention cells built with engineered soil effectively retained TWP > 25 µm in size, demonstrating their potential as control measures along roads.