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.

REGENERATE旨在支持快速增长的废物能源厌氧处理废水行业。为此，我们将率先在废水处理系统中应用能量驱动的工程方法，以提高氨去除能力，减少温室气体排放，并减轻抗生素耐药性。因此，有必要进行范式转换创新，以推动废水处理行业走向更加碳自然、环境健康的未来。鉴于废物转化为能源的厌氧处理后的出水富含氨和抗生素，最先进的节能部分硝化-厌氧氨氧化（PN-A）系统无法解决日益增加的温室气体排放（N2 O排放）和环境健康需求（抗生素耐药性），尽管其高氨去除效率。REGENERATE将应对这一挑战，利用多尺度研究中的能量学基础。微生物能量学驱动代谢途径，确定特定的终产物并调节基因表达。具体而言，证据表明，能量驱动的曝气供应可以调节N2 O减排和改善抗生素的生物降解。工程曝气策略的多种组合是可能的;因此，我们将开发一种耦合的溶解氧水平和曝气设定值能量驱动的方法，以研究PN/A系统中发现的微生物聚生体。在本项目中，将通过实验室、实验室和全尺寸实验来检查使用工业相关指标、分析化学和基因组生物学的通气驱动能量学的影响。因此，REGENERATE的一个关键特征是通过联络学术界的项目学者和水行业的合作伙伴，扩大规模并实现快速工业采用升级后的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作为第一原则引入当前的水工业实践。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、台架和全规模实验）来完成。此外，该数据集将由该项目中开发的机器学习管道进行训练和分析。最后，对PN-A系统在污水厌氧处理能源化工业中的环境效益和经济效益进行综合评价。因此，我们将通过包括实验室，实验室和全面调查进行变革性研究，并应用相互关联的研究领域，包括环境生物技术，药物化学，微生物基因组学和机器学习计算机科学，水基础设施规划和工程，并汇集了一个由9名学者和2名利益相关者组成的跨学科团队。因此，REGENERATE将鼓励部署和迅速接受拟议的PN-A技术，使其成为一种更可持续、更健康的废物转化为能源的模式。