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.
2023
Authors
Lu FengAbstract
No abstract has been registered
Authors
Chuan Ding Yi Zhang Xindu Li Qiang Liu Yeqing Li Yanjuan Lu Lu Feng Junting Pan Hongjun ZhouAbstract
The anaerobic digestion (AD) of food waste (FW) was easy to acidify and accumulate ammonia nitrogen. Adding exogenous materials to the AD system can enhance its conversion efficiency by alleviating acidification and ammonia nitrogen inhibition. This work investigated the effects of the addition frequency and additive amount on the AD of FW with increasing organic loading rate (OLR). When the OLR was 3.0 g VS per L per day and the concentration of the additives was 0.5 g per L per day, the stable methane yield reached 263 ± 22 mL per g VS, which was higher than that of the group without the additives (189 mL per g VS). Methanosaetaceae was the dominant archaea, with a maximum abundance of 93.25%. Through machine learning analysis, it was found that the optimal daily methane yield could be achieved. When the OLR was within the range of 0–3.0 g VS per L per day, the pH was within the range of 7.6–8.0, and the additive concentration was more than 0.5 g per L per day. This study proposed a novel additive and determined its usage strategy for regulating the AD of FW through experimental and simulation approaches.
Abstract
The anaerobic digestion of organic materials produces biogas; however, optimizing methane (CH4) content within biogas plants by capturing carbon dioxide (CO2) is one of the challenges for sustainable biomethane production. CH4 is separated from biogas, which is called biogas upgrading for biomethane production. In this regard, in-situ CO2 capture and utilization could be an alternative approach that can be achieved using conductive particles, where the conductive particles support the direct intraspecific electron transfer (DIET) to promote CH4 production. In this investigation, a carbon nanotube (CNT) was grown over conductive activated carbon (AC). Then an iron (Fe) nanoparticle was anchored (AC/CNT/Fe), which ultimately supported microbes to build the biofilm matrix, thereby enhancing the DIET for CH4 formation. The biogas production and CH4 content increased by 17.57 % and 15.91 %, respectively, when AC/CNT/Fe was utilized. Additionally, 18S rRNA gene sequencing reveals that Methanosarcinaceae and Methanobacteriaceae families were the most dominant microbes in the reactor when conductive particles (AC/CNT/Fe) were applied. The proposed study supports the stable operation of biogas plants to utilize CO2 for CH4 production by using surface-modified material.
Abstract
No abstract has been registered
Authors
Yi Zhang Yijing Feng Zhonghao Ren Runguo Zuo Tianhui Zhang Yeqing Li Yajing Wang Zhiyang Liu Ziyan Sun Yongming Han Lu Feng Mortaza Aghbashlo Meisam Tabatabaei Junting PanAbstract
The ideal conditions for anaerobic digestion experiments with biochar addition are challenging to thoroughly study due to different experimental purposes. Therefore, three tree-based machine learning models were developed to depict the intricate connection between biochar properties and anaerobic digestion. For the methane yield and maximum methane production rate, the gradient boosting decision tree produced R2 values of 0.84 and 0.69, respectively. According to feature analysis, digestion time and particle size had a substantial impact on the methane yield and production rate, respectively. When particle sizes were in the range of 0.3–0.5 mm and the specific surface area was approximately 290 m2/g, corresponding to a range of O content (>31%) and biochar addition (>20 g/L), the maximum promotion of methane yield and maximum methane production rate were attained. Therefore, this study presents new insights into the effects of biochar on anaerobic digestion through tree-based machine learning.
Authors
Yi Zhang Zhangmu Jing Yijing Feng Shuo Chen Yeqing Li Yongming Han Lu Feng Junting Pan Mahmoud Mazarji Hongjun Zhou Xiaonan Wang Chunming XuAbstract
Exploring key factors has important guidance for understanding complex anaerobic digestion (AD) systems. This study proposed a multi-layer automated machine learning framework to understand the complex interactions in AD systems and explore key factors at the environmental factor, microorganisms and system levels. The first layer of the framework identified hydraulic residence time (HRT) as the most important environmental factor, with an optimal range of 33–45 d. In the second layer of the framework, Methanocelleus (optimal relative abundance (ORA) = 3.0%) and Candidatus_Caldatribacterium (ORA = 1.7%) were found to be the key archaea and bacteria, respectively. Furthermore, the prediction of key microorganisms based on environmental factors and remaining microbial data showed the essential roles of Methanothermobacter and Acetomicrobium. The third layer for finding the optimal combination of data variables for predicting biogas production demonstrated that combined Archaea genera and environmental factors should be achieved for the most accurate prediction (root mean square error (RMSE) = 84.21). GBM had the best model performance and prediction accuracy among all the built-in models. Based on the optimal GBM model, the analysis at the system level showed that HRT was the most important variable. However the most important microorganism, Methanocelleus, within the appropriate survival range is also essential to achieve optimal biogas production. This research explores key parameters at various levels through automated machine learning techniques, which are expected to provide guidance in understanding the complex architecture of industrial and laboratory AD systems.
Abstract
No abstract has been registered
Authors
Junbin ZhaoAbstract
No abstract has been registered
Authors
Junbin ZhaoAbstract
No abstract has been registered
Authors
Junbin ZhaoAbstract
No abstract has been registered