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

美国的奶牛养殖业是一个价值数十亿美元的产业，它提供了基本的食品。与此同时，该行业监管的数百万只动物产生了巨大的环境足迹，影响了空气、土地和水质。具体来说，牲畜粪便是一种富含碳和营养（氮和磷化合物）的废物流，通常用作肥料。这种做法促进了养分的循环，但也导致了甲烷和一氧化二氮等强效温室气体的排放，并且由于粪肥养分往往与作物需求不平衡而导致土壤中的养分积累。养分积累反过来促进径流流入地表水和地下水，导致富营养化和藻类大量繁殖，从而影响财产价值、娱乐和旅游业。以可扩展的方式回收肥料营养仍然是一个巨大的社会挑战；主要的困难是粪便是一种巨大的、稀释的、分散的废物流。让我们从一个角度来看，威斯康星州有120万头奶牛分布在9000个奶牛场；该州每年总共产生2400万吨粪肥，这些废物流含有32000吨磷。该项目将寻求开发低成本、模块化和灵活的粪便处理技术来应对这一挑战。这些过程将利用光合微生物（蓝藻）从粪便中捕获营养物质，这些微生物将利用合成生物学技术进行改造，以适应这种应用。我们将结合实验、计算模型和机器学习技术来研究使用蓝藻作为可持续生物肥料的潜力，这可以帮助减少合成肥料的使用，减轻水体的营养污染。我们设想的过程为更可持续的农业迈出了一步，并有可能激活生物经济，帮助农民获得新技术和收入来源。该项目还为K-12、本科生和研究生在STEM领域提供了令人兴奋的机会。该项目的总体目标是开发利用蓝藻（CB）从粪便中生产农场生物肥料的光合作用过程。这些多功能过程旨在：(i)以湿/干CB生物质和营养平衡的CB-粪肥混合物的形式生产一系列有价值的生物肥料，（ii）回收粪肥营养物质进行再分配，以及（iii）在生物肥料生产中实现水、碳和能源的可持续管理。这些目标将通过模块化袋式光生物反应器与粪肥厌氧消化装置、沼气净化系统、CB生物质分离装置和发电机的集成来实现。这一整合的推动因素将是经过设计的CB菌株：(i)最大限度地从粪便中回收养分，（ii）促进作物养分吸收，（iii）最大限度地从粪便中产生沼气，以及（iv）促进沼气净化。CB培养、共消化和土壤实验将使用机器学习算法进行指导；这些算法的目标是战略性地收集数据，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，并评估部署我们的工艺（例如，地理营养平衡）所带来的基础设施层面的效益。同样，技术经济建模工作将用于比较当前氮和磷遏制战略的经济成本与向环境释放经过改造的氯化碳的潜在风险相关的成本。我们在威斯康星大学麦迪逊分校组建了一个多学科的团队，他们在系统工程、合成生物学、农业可持续性和土壤科学方面具有专业知识。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.