Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2022
Authors
Lu Feng Lise Bonne Guldberg Michael Jørgen Hansen Chun Ma Rikke Vinther Ohrt Henrik Bjarne MøllerAbstract
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.
Abstract
No abstract has been registered
Authors
Yeqing Li Zhangmu Jing Junting Pan Gang Luo Lu Feng Hao Jiang Hongjun Zhou Quan Xu Yanjuan Lu Hongbin LiuAbstract
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.
Authors
Yi Zhang Linhui Li Zhonghao Ren Yating Yu Yeqing Li Junting Pan Yanjuan Lu Lu Feng Weijin Zhang Yongming HanAbstract
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.
Authors
Yeqing Li Xingru Yang Mingyu Zhu Liming Dong Hao Jiang Quan Xu Hongjun Zhou Yongming Han Lu Feng Chengfei LiAbstract
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.
Authors
Lu FengAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Zhanjiang Pei Shujun Liu Zhangmu Jing Yi Zhang Jingtian Wang Jie Liu Yajing Wang Wenyang Guo Yeqing Li Lu Feng Hongjun Zhou Guihua Li Yongming Han Di Liu Junting PanAbstract
No abstract has been registered
Authors
Michel Bechtold Bjørn Kløve Annalea Lohila Massimo Lupascu Line Rochefort Hanna Marika SilvennoinenAbstract
No abstract has been registered
Authors
Marzieh Hasanzadeh Saray Aziza Baubekova Alireza Gohari Seyed Saeid Eslamian Bjørn Kløve Ali Torabi HaghighiAbstract
Water-Energy-Food (WEF) Nexus and CO2 emissions for a farm in northwest Iran were analyzed to provide data support for decision-makers formulating national strategies in response to climate change. In the analysis, input–output energy in the production of seven crop species (alfalfa, barley, silage corn, potato, rapeseed, sugar beet, and wheat) was determined using six indicators, water, and energy consumption, mass productivity, and economic productivity. WEF Nexus index (WEFNI), calculated based on these indicators, showed the highest (best) value for silage corn and the lowest for potato. Nitrogen fertilizer and diesel fuel with an average of 36.8% and 30.6% of total input energy were the greatest contributors to energy demand. Because of the direct relationship between energy consumption and CO2 emissions, potato cropping, with the highest energy consumption, had the highest CO2 emissions with a value of 5166 kg CO2eq ha−1. A comparison of energy inputs and CO2 emissions revealed a direct relationship between input energy and global warming potential. A 1 MJ increase in input energy increased CO2 emissions by 0.047, 0.049, 0.047, 0.054, 0.046, 0.046, and 0.047 kg ha−1 for alfalfa, barley, silage corn, potato, rapeseed, sugar beet, and wheat, respectively. Optimization assessments to identify the optimal cultivation pattern, with emphasis on maximized WEFNI and minimized CO2 emissions, showed that barley, rapeseed, silage corn, and wheat performed best under the conditions studied.