EFRI DCheM: Distributed Photosynthetic Recovery of Livestock Waste Nutrients for Sustainable Production of Fertilizers

EFRI DCheM：畜牧废物养分的分布式光合回收用于肥料的可持续生产

• 批准号: 2132036
• 负责人: Victor Zavala Tejeda
• 金额: $ 200万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2132036&HistoricalAwards=false
• 关键词: EFRI; DCheM; Distributed; Photosynthetic; Recovery

Dairy farming in the U.S. is a multi-billion-dollar industry that provides essential food products. At the same time, the millions of animals that this industry oversees generate a massive environmental footprint affecting air, land, and water quality. Specifically, livestock manure is a carbon- and nutrient-rich (in nitrogen and phosphorus compounds) waste stream that is routinely used as fertilizer. This practice enables nutrient recycling but also leads to emissions of potent greenhouse gases such as methane and nitrous oxide, and to nutrient accumulation in soils due to manure nutrients often being imbalanced with respect to crop needs. Nutrient accumulation in turn promotes runoff to surface and groundwaters and leads to eutrophication and algae blooms that impact property values, recreation, and tourism. Recovering manure nutrients in a scalable manner remains a grand societal challenge; the main difficulty is that manure is a vast, diluted, and distributed waste stream. To give some perspective, there are 1.2 million dairy cows in Wisconsin distributed across 9,000 dairy farms; a total of 24 million tons of manure are generated in the state annually and this waste stream contains 32,000 tons of phosphorous. This project will seek to develop low-cost, modular, and flexible manure processing technologies to tackle this challenge. These processes will capture nutrients from manure using photosynthetic microorganisms (cyanobacteria) that will be engineered using synthetic biology techniques to tailor their performance for this application. We will combine experiments, computational models, and machine learning techniques to investigate the potential of using the cyanobacteria as sustainable biofertilizers that can help reduce the use of synthetic fertilizers and mitigate nutrient pollution of waterbodies. The processes that we envision provide a step towards more sustainable farming and can potentially activate a bioeconomy that helps farmers access new technologies and revenue sources. This project also provides exciting opportunities to engage K-12, undergraduate, and graduate students in STEM fields.The overall goal of this project is to develop photosynthetic processes for on-farm biofertilizer production from manure using cyanobacteria (CB). These multi-functional processes aim to: (i) produce a range of valuable biofertilizers in the form of wet/dry CB biomass and of nutrient-balanced CB-manure blends, (ii) recover manure nutrients for redistribution, and (iii) enable sustainable management of water, carbon, and energy in biofertilizer production. These objectives will be achieved via integration of modular bag photobioreactors with manure anaerobic digestion units, biogas purification systems, CB biomass separation units, and power generators. The enablers of this integration will be engineered CB strains that: (i) maximize nutrient recovery from manure, (ii) facilitate crop nutrient uptake, (iii) maximize biogas production from manure, and (iv) facilitate biogas purification. CB culture, co-digestion, and soil experiments will be guided using machine learning algorithms; these algorithms will aim to strategically collect data to create and refine process models. We will use our models to conduct techno-economic and life-cycle studies and to assess infrastructure-level benefits that result from the deployment of our processes (e.g., geographical nutrient balancing). Likewise, the techno-economic modeling work will be used to compare the economic costs of current nitrogen and phosphorous containment strategies to the costs associated with potential risks of releasing the engineered CB to the environment. We have assembled a multi-disciplinary team at UW-Madison with expertise in systems engineering, synthetic biology, agricultural sustainability, and soil science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

美国的奶牛农业是一个数十亿美元的行业，提供必不可少的食品。同时，该行业监督的数百万只动物产生了影响空气，土地和水质的巨大环境足迹。具体而言，牲畜粪便是一种富含碳和养分的废物（在氮和磷化合物中），通常用作肥料。这种做法可以使营养回收利用，但也导致有效的温室气体（如甲烷和一氧化二氮）的排放，以及由于肥料养分而在土壤中的养分积累，通常会因农作物需求而失衡。养分的积累反过来促进了地面和地下水的径流，并导致富营养化和藻类开花，影响财产价值，娱乐和旅游业。以可扩展的方式恢复肥料营养仍然是一个巨大的社会挑战。主要困难是肥料是广阔，稀释和分布的废物流。为了提供一些观点，威斯康星州有120万奶牛分布在9,000个奶牛场上。该州每年共有2400万吨肥料，该废物流含32,000吨磷。该项目将寻求开发低成本，模块化和灵活的肥料加工技术来应对这一挑战。这些过程将使用光合微生物（蓝细菌）从肥料中捕获营养，这些生物将使用合成生物学技术进行设计，从而为此应用定制其性能。我们将结合实验，计算模型和机器学习技术，以研究使用蓝细菌作为可持续生物肥料的潜力，这些生物量化剂可以帮助减少合成肥料的使用并减轻水体的养分污染。我们设想的过程为更可持续的农业提供了一步，并可以激活一种生物经济，以帮助农民获得新的技术和收入来源。该项目还提供了激动人心的机会，可以使K-12，本科生和研究生参与STEM领域。该项目的总体目标是开发光合作用过程，用于使用蓝细菌（CB）从肥料中产生农场生物纤维化剂的生产。这些多功能过程的目的是：（i）以湿/干的CB生物量和营养平衡的CB-Manure混合物的形式生产一系列有价值的生物肥料，（ii）恢复用于再分配的肥料营养素，以及（iii）启用（iii）生物肥料生产中水，碳和能源的可持续管理。这些目标将通过将模块化袋光生反应器与肥料厌氧消化单元，沼气纯化系统，CB生物量分离单元和发电机的整合进行整合。这种整合的推动因素将是设计的CB菌株：（i）最大化肥料中的营养恢复，（ii）促进农作物的营养摄取，（iii）最大化肥料中的沼气产生，（iv）促进沼气净化。 CB文化，共同消化和土壤实验将使用机器学习算法进行指导；这些算法将旨在战略性地收集数据以创建和完善过程模型。我们将使用我们的模型进行技术经济和生命周期研究，并评估由于我们的流程部署（例如，地理营养平衡）而造成的基础设施级别的收益。同样，技术经济建模工作将用于比较当前的氮和磷遏制策略的经济成本与将工程CB释放到环境的潜在风险相关的成本。我们已经在UW-Madison组建了一个多学科团队，并拥有系统工程，合成生物学，农业可持续性和土壤科学方面的专业知识。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛的评估来支持的。影响审查标准。