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

在美国，奶牛养殖是一个价值数十亿美元的产业，提供基本的食品。与此同时，该行业监管的数百万动物产生了巨大的环境足迹，影响了空气，土地和水质。具体而言，牲畜粪便是一种富含碳和营养素（氮和磷化合物）的废物流，通常用作肥料。这种做法使养分得以再循环，但也导致甲烷和一氧化二氮等强效温室气体的排放，并由于粪肥养分往往与作物需求不平衡而导致土壤养分积累。养分的积累反过来又促进地表水和地下水的径流，导致富营养化和藻类大量繁殖，影响财产价值、娱乐和旅游业。以可扩展的方式回收粪肥养分仍然是一个巨大的社会挑战;主要困难在于粪肥是一种巨大的、稀释的和分散的废物流。为了提供一些观点，威斯康星州有120万头奶牛分布在9,000个奶牛场;该州每年总共产生2400万吨粪便，这些废物流含有32,000吨磷。该项目将寻求开发低成本、模块化和灵活的粪肥处理技术，以应对这一挑战。这些过程将使用光合微生物（蓝细菌）从粪肥中捕获营养物质，这些微生物将使用合成生物学技术进行工程改造，以适应这种应用的性能。我们将结合联合收割机实验、计算模型和机器学习技术，研究利用蓝藻作为可持续生物肥料的潜力，以帮助减少合成肥料的使用，减轻水体的营养污染。我们设想的过程为实现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。该项目还提供了令人兴奋的机会，让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，用于使用蓝藻（CB）从粪肥生产农场生物肥料。这些多功能工艺的目标是：（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）回收粪肥营养素进行再分配，以及（iii）实现可持续管理生物肥料生产中的水、碳和能源。这些目标将通过模块化袋式光生物反应器与粪便厌氧消化装置、沼气净化系统、CB生物质分离装置和发电机的集成来实现。这种整合的推动者将是工程CB菌株，这些菌株：（i）最大限度地从粪肥中回收养分，（ii）促进作物养分吸收，（iii）最大限度地从粪肥中生产沼气，（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，并评估部署我们的流程所带来的基础设施层面的效益（例如，地理营养平衡（Geographical Nutrition Balancing）。同样，技术经济建模工作将用于比较当前氮和磷控制策略的经济成本与将工程CB释放到环境中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团队，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.