EPSRC-SFI: REducing Greenhouse gas emissions and ENgaging antibactErial Resistance in Anaerobic Treated Effluents (REGENERATE)

EPSRC-SFI：减少温室气体排放并提高厌氧处理废水的抗菌性（再生）

• 批准号: EP/X010260/1
• 负责人: Po-Heng (Henry) Lee
• 金额: $ 64.87万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX010260%2F1
• 关键词: EPSRC; SFI; REducing; Greenhouse; gas

REGENERATE is to support the rapidly growing waste-to-energy anaerobic treated effluent industry. To this end, we will be the first to apply an energetics-driven engineering method in wastewater treatment systems to improve ammonia removal capacity, reduce greenhouse gas emissions, and mitigate antibiotic resistance. Therefore, paradigm-shifting innovation is necessary to advance the wastewater industry to a more carbon natural, environmental healthy future. Given that the waste-to-energy anaerobic treated effluent is rich in ammonia and antibiotics, the state-of-the-art energy-efficient Partial Nitrification-Anammox (PN-A) system is incapable to address the increasing greenhouse gas emission (N2O emission) and environmental health demands (antibiotic resistance), albeit its high ammonia removal efficiency. REGENERATE will respond to this challenge, taking advantage of energetics fundamental in a multiple-scale investigation. Microbial energetics drives metabolic pathways and determinates specific end-products and regulates gene expression. Specifically, evidence shows energetics-driven aeration supply can regulate N2O emission reduction and improve antibiotics biodegradation. Multiple combination of engineered aeration strategy is possible; therefore, we will develop a coupled dissolved oxygen level and aeration setpoint energetics-driven approach to investigate microbial consortia found in the PN/A system. The effects of aeration-driven energetics using industrially relevant metrics and analytical chemistry and genomic biology will be examined crossing a lab-, bench-, and full-scale experimentation in this project. Accordingly, a key feature of REGENERATE is to up-scale and achieve rapid industrial adoption of the upgraded PN-A technology by liaising the Project Scholars from Academia and Partners from the Water Industry to implement the research outcomes for operational sites. The other innovation is to introduce microbial energetics as the first principle to current water industry practices. This will be done by using high throughput chemical and genomics analyses to collect an unprecedented engineering and genomics dataset including the lab-, bench-, and full-scale experiments. Further, the dataset will be trained and analysed by the machine learning pipelines developed in the project. Finally, we will access a comprehensive evaluation of the environmental and economic benefits of the PN-A system for the waste-to-energy anaerobic treated effluent industry. Therefore, we will conduct transformative research by including bench-, lab-, and full-scale investigation and apply interconnected research areas including Environmental Biotechnology, Pharmaceutical Chemistry, Microbial Genomics, and Machine Learning Computer Science, Water Infrastructure Planning and Engineering, and bring together an interdisciplinary team with 9 scholars and 2 stakeholders. REGENERATE will, thus, encourage deployment and speedy acceptance of the proposed PN-A technology into a more sustainable, healthy waste-to-energy paradigm.

再生是为了支持快速生长的废物厌氧处理的废水行业。为此，我们将是第一个在废水处理系统中采用能量驱动的工程方法来改善氨去除能力，减少温室气体排放并减轻抗生素耐药性。因此，为了将废水行业推向更具碳自然，环境健康的未来，范式转移创新是必要的。鉴于经过的废质量处理的废水富含氨和抗生素，因此最先进的能源有效的部分硝化 - 硝化 - 氨基氧（PN-A）系统无法解决增加的温室气体排放（N2O发射）和环境健康需求（抗生素抗性），Albeiaia的效率很高。再生将在多个规模的调查中利用能量学基本来应对这一挑战。微生物能量驱动代谢途径并确定特定的终产物并调节基因表达。具体而言，有证据表明，通过能量驱动的曝气供应可以调节N2O排放的减少并改善抗生素生物降解。工程曝气策略的多重组合是可能的；因此，我们将开发一种耦合的溶解氧水平和曝气设定点驱动的方法，以研究在PN/A系统中发现的微生物伴侣。将检查使用与工业相关的指标以及分析化学和基因组生物学的曝气驱动能量学的影响，并在该项目中跨越实验室，台式和全尺度实验。因此，再生的一个关键特征是通过与学术界和水工业合作伙伴的项目学者联系来提高升级和快速采用升级的PN-A技术，以实施运营地点的研究成果。另一个创新是将微生物能量学引入当前水产行业实践的第一个原则。这将通过使用高通量化学和基因组学分析来收集前所未有的工程和基因组学数据集，包括实验室，台式和全尺度实验。此外，将通过项目中开发的机器学习管道对数据集进行培训和分析。最后，我们将对PN-A系统的环境和经济益处进行全面评估，以对经过的厌氧处理的废水产业。因此，我们将通过包括台式，实验室和全尺度研究来进行变革性研究，并应用互连的研究领域，包括环境生物技术，药物化学，微生物基因组学以及机器学习计算机科学，水基础设施计划和工程学，并将9个学科和2家学科的跨学科团队召集在一起。因此，将重新培养意志，鼓励将拟议的PN-A技术的部署和快速接受为更可持续，健康的废物到能量范式。