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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.

2023

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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.

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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.

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利用可再生能源发电获得单细胞蛋白质,即“电转蛋白质(Power-to-Protein,PtP)”,是生产单细胞蛋白质的 绿色可持续途径. 厌氧消化(anaerobic digestion,AD)可提供“电转蛋白质”过程中所需要的碳源以及部分氮源、微 量元素等,通过厌氧消化与“电转蛋白质”技术相结合构建了一种可持续的蛋白质生产工艺,“厌氧消化-电转蛋白质 (anaerobic digestion-Power-to-Protein,ADPtP)”. 本文介绍现有利用AD出料培养微生物获得单细胞蛋白质的工艺, 分析引入PtP技术后的工艺效率与能耗,重点介绍ADPtP工艺的基础模式、过程中资源整合与利用. 在电化学法还原模 式中将沼气升级后培养甲烷氧化细菌操作简便易行且环境效益高,而以Wood-Ljungdahl碳转化途径设计的ADPtP工艺 在安全性方面占优势. ADPtP工艺中,降低爆炸风险和提高转化效率是亟须解决的关键问题. 另外,工艺中资源整合方 式为电化学沼气升级、电化学消化液提氨、电解水产氢. 未来,我国沼气产量与潜在储备量以及可再生能源发电量将为 ADPtP提供发展基础,同时具有良好的政策环境和巨大的潜在蛋白质市场需求,因此该工艺在我国作为新型绿色蛋白质 生产工艺而施行具有良好基础与应用前景. (图1 表2 参64)

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Soils are the foundation of agricultural production, ecosystem functioning and human well-being. Bridging soil knowledge gaps and improving the knowledge system is crucial to meet the growing EU soil policy ambitions in the face of climate change and the ongoing trend in soil degradation. The objective of this article is to assess the current state of knowledge, knowledge use and knowledge gaps concerning sustainable soil management in Europe. This study is based on interviews with 791 stakeholders and 254 researchers and on a comprehensive review of >1800 documents carried out under the European Joint Programme on agricultural soils. Despite differences in stakeholder groups, the conclusions are rather consistent and complementary. We identified major knowledge gaps with respect to (1) soil carbon stocks, (2) soil degradation and fertility and (3) strategies for improved soil management. Transcending these three areas, particularly the loss of soil organic carbon, peatland degradation and soil compaction, are most critical, thus, we stress the urgency of developing more models and monitoring programmes on soils. Stakeholders further report that insufficient transfer of existing soil research findings to practitioners is a hindrance to the adoption of sustainable soil management practices. In addition to knowledge production, soil knowledge gaps may be addressed by considering seven recommendations from the stakeholders: (1) raising awareness, (2) strengthening knowledge brokers, (3) improving relevance of research activities and resource allocation for land users, (4) peer-to-peer communication, (5) targeting advice and information, (6) improving knowledge access, and (7) providing incentives. We argue that filling and bridging knowledge gaps should be a priority for policymakers and the insights provided in the article may help prioritise research and dissemination needs enabling a transition to more sustainable soil management in Europe.