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增长的垃圾转化为能源的厌氧处理污水行业。为此，我们将率先在污水处理系统中应用能源学驱动的工程方法，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，转变模式创新是必要的，以推动废水处理行业走向更碳、更自然、更环保的健康未来。考虑到垃圾转化能源厌氧处理出水富含氨氮和抗生素，最先进的节能部分硝化-厌氧氨氧化(PN-A)系统虽然具有很高的氨氮去除效率，但无法满足日益增长的温室气体排放(N2O排放)和环境健康要求(抗生素耐药性)。再生将对这一挑战作出回应，利用多尺度调查中的能量学基础。微生物能量学驱动新陈代谢途径，确定特定的最终产物，并调节基因表达。具体地说，证据表明，能量驱动的曝气供应可以调节N2O的减排，并改善抗生素的生物降解性。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点能量驱动的方法来研究在PN/A系统中发现的微生物群落。在这个项目中，将通过实验室、工作台和全尺寸实验来检验使用工业相关指标以及分析化学和基因组生物学的充气驱动的能量学的效果。因此，Regate的一个主要特点是通过联系学术界的项目学者和水务行业的合作伙伴来实施运营地点的研究成果，从而扩大和实现升级后的PN-A技术的行业快速采用。另一项创新是将微生物能量学作为当前水行业实践的首要原则。这将通过使用高通量的化学和基因组分析来收集史无前例的工程和基因组数据集来完成，包括实验室、工作台和全尺寸实验。此外，数据集将通过项目中开发的机器学习管道进行训练和分析。最后，我们将对垃圾转化能源厌氧处理污水行业的PN-A系统的环境和经济效益进行全面评估。因此，我们将通过包括实验台、实验室和全面调查在内的变革性研究，应用环境生物技术、药物化学、微生物基因组学、机器学习计算机科学、水利基础设施规划和工程等相互关联的研究领域，并汇聚一个由9名学者和2名利益相关者组成的跨学科团队。因此，再生将鼓励部署和迅速接受拟议的PN-A技术，使之成为更可持续、更健康的废物转化能源范例。