Understanding microbial community dynamics in food waste anaerobic digestion

厌氧消化（AD）利用复杂的微生物群落以沼气和氮氧化物的形式从有机废物中回收资源。沼气可以在热电联合发动机中燃烧，产生供当地使用的热量和可以输出到电网的“绿色”电力。该项目将考虑微生物群落在商业系统中的食物垃圾AD期间如何改变，其中主要考虑因素是将垃圾转化为高质量沼气的持续高水平。样本将从D&F Solutions工厂收集，考虑生物和技术复制以及不同地点操作之间的工艺差异。该项目还可能考虑容器清洗后沼气池启动的动态。初始工作将考虑D&F现场的历史数据，寻找原料成分和沼气生产之间的相关性。这项工作还将比较至少两种不同的方法，用于16S rRNA扩增子宏基因组学的绝对定量，作为跟踪微生物群落组成变化的一种方式。其他方法，可以应用于获得更多的了解详细的反应的微生物群落的原料和工艺的变化可能包括鸟枪宏基因组学和代谢omics.Variables，可能被认为是一种方式来了解这些AD过程是否可以增强将包括废物流的组成，喂养制度和添加的过程增强剂，如微量元素。在从数据收集和分析中产生可检验假设的情况下，可以使用基于实验室的AD系统来检验这些假设，以推断这些变化可能如何影响工艺规模操作。

Anaerobic digestion (AD) uses complex microbial communities to recover resources from organic waste in the form of biogas and a nitrogen digestate. Biogas can be burned in a combined heat and power engine to produce heat for local use and "green" electricity that can be exported to the grid. This project will consider how microbial communities alter during the AD of food waste in commercial systems, where key considerations are generating a consistently high level of conversion of waste to high quality biogas. Samples will be collected from D&F Solutions facilities, with consideration of biological and technical replication and, process differences between operations at different sites. The project may also consider the dynamics of digester start-up following vessel cleaning.Initial work will consider historical data from D&F sites, looking for correlations between feedstock composition and biogas production. The work will also compare at least two different methods for the absolute quantification of 16S rRNA amplicon metagenomics as a way of tracking microbial community compositional changes. Other methods that may be applied to gaining more insight into the detailed responses of the microbial communities to feedstock and process changes might include shotgun metagenomics and metabolomics.Variables that might be considered as a way of understanding whether these AD processes can be enhanced will include waste stream composition, feeding regimes and additions of process enhancers such as trace elements. Where testable hypotheses are generated from data collection and analysis, these may be tested using lab-based AD systems to infer how these changes might impact on process scale operations.