标题： 厌氧消化中微生物群落的时间动态：温度和进料成分对反应器性能和稳定性的影响 显示英文标题

标题： Temporal Dynamics of Microbial Communities in Anaerobic Digestion: Influence of Temperature and Feedstock Composition on Reactor Performance and Stability

摘要： 厌氧消化（AD）提供了一种可持续的生物技术，用于从富含碳的废水（如食品加工废水）中回收资源。 尽管对粗略废水进行了表征，但详细化学指纹对AD的影响仍研究不足。 本研究调查了发酵废水组成和操作参数随时间对AD的影响，以确定影响反应器微生物多样性和性能的关键因素。 使用酵母蛋白发酵废水，在不同操作条件下运行了十八个反应器。 详细的化学分析在AD过程中对发酵废水中的分子进行了指纹识别，包括糖、糖醇和挥发性脂肪酸（VFAs）。 测序揭示了与温度和反应器配置相关的独特微生物组特征，中温条件支持更丰富且连接更紧密的微生物组。 Methanomassiliicoccus的显著增加与高丁酸浓度和生物气产量减少相关，进一步阐明了这种新发现产甲烷菌的作用。 差异分析表明单个分子对微生物组多样性的重要性，突显了在AD微生物趋势研究中进行详细化学指纹分析的必要性。 基于操作参数和微生物分类的机器学习（ML）模型在预测反应器性能方面达到了高准确性。 操作参数对化学需氧量去除的影响最大，而Oscillibacter和两种梭菌属（Clostridium sp.）被突出为生物气生产的关键因素。 通过将详细的化学和生物指纹分析与ML模型相结合，这项研究提出了一种新方法，以推进我们对AD微生物生态学的理解，为可持续废物-能源系统的工业应用提供了见解。 显示英文摘要

摘要： Anaerobic digestion (AD) offers a sustainable biotechnology to recover resources from carbon-rich wastewater, such as food-processing wastewater. Despite crude wastewater characterisation, the impact of detailed chemical fingerprinting on AD remains underexplored. This study investigated the influence of fermentation-wastewater composition and operational parameters on AD over time to identify critical factors influencing reactor biodiversity and performance. Eighteen reactors were operated under various operational conditions using mycoprotein fermentation wastewater. Detailed chemical analysis fingerprinted the molecules in the fermentation wastewater throughout AD including sugars, sugar alcohols and volatile fatty acids (VFAs). Sequencing revealed distinct microbiome profiles linked to temperature and reactor configuration, with mesophilic conditions supporting a more diverse and densely connected microbiome. Significant elevations in Methanomassiliicoccus were correlated to high butyric acid concentrations and decreased biogas production, further elucidating the role of this newly discovered methanogen. Dissimilarity analysis demonstrated the importance of individual molecules on microbiome diversity, highlighting the need for detailed chemical fingerprinting in AD studies of microbial trends. Machine learning (ML) models predicting reactor performance achieved high accuracy based on operational parameters and microbial taxonomy. Operational parameters had the most substantial influence on chemical oxygen demand removal, whilst Oscillibacter and two Clostridium sp. were highlighted as key factors in biogas production. By integrating detailed chemical and biological fingerprinting with ML models this research presents a novel approach to advance our understanding of AD microbial ecology, offering insights for industrial applications of sustainable waste-to-energy systems.