I-Corps: Machine Learning Approach for Microbial Process Control and Management

I-Corps项目的广泛影响/商业潜力将直接影响到美国每年产生的80多立方千米的废水和固体废物的处理和管理。在发达国家和发展中国家，废物处理过程与环境和人类健康直接相关，但由于需要大量能源和维护，其成本很高。在美国和其他发达国家，仅城市污水处理一项就消耗了约3%的电能。该技术将人工智能（AI）集成到现有的废物处理基础设施中，成功实施该技术有可能大大改善与处理过程相关的微生物群落的管理，从而提高整体有效性和可持续性。该解决方案为客户提供了一种新的方式，可以降低能源和运营成本，提高处理过程的有效性，而无需在基础设施上进行大量投资。这项技术的潜在市场除了工业/农业部门的处理设施外，还包括公共、市政设施。拟议技术的发展可能会刺激人工智能系统在其他领域的应用，这些领域以基于微生物群落的工程系统为中心，如生物产品生产、生物传感和生物修复。这个I-Corps项目是基于我们最近开发的一种基于机器学习的方法，用于准确预测废水处理中的微生物群落结构、过程稳定性和反应器性能。与不考虑微生物群落动态的模型相比，这种将基因组数据以及环境和操作参数纳入数据挖掘数据集的新方法在准确预测小规模废水系统的过程稳定性和性能方面有了显著提高。通过构建人工神经网络开发的预测模型有可能为工程决策提供信息，以优化全尺寸系统中环境生物技术的性能和稳定性。这种方法的发展和实施不仅将促进对居住在环境生物技术中的微生物群落的理解和控制，而且可以扩展到其他微生物群落，例如与人类健康和生物地球化学循环有关的微生物群落。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。

The broader impact/commercial potential of this I-Corps project will directly affect the treatment and management of the over 80 km3 of wastewater and solid wastes produced each year in the US. Waste treatment processes are directly tied to environmental and human health in both developed and developing countries but are subject to high costs due to significant energy and maintenance requirements. Municipal wastewater treatment alone accounts for about 3% of electrical energy consumed in the U.S and other developed countries. The successful implementation of the proposed technology, which integrates artificial intelligence (AI) into existing waste treatment infrastructure, has the potential to greatly improve the management of microbial communities associated with treatment processes thereby improving overall effectiveness and sustainability. This solution represents a new way for customers to cut energy and operational costs and improve effectiveness of their treatment processes without large investments in infrastructure. Potential markets for this technology include public, municipal facilities in addition to treatment facilities in the industrial/agricultural sector. Development of the proposed technology will likely spur additional applications of AI systems in other fields centered around microbial community based engineered systems like bioproduct production, biosensing, and bioremediation.This I-Corps project is based on our recent development of a machine learning based approach used to accurately predict microbial community structure, process stability, and reactor performance for wastewater treatment. This novel approach, which incorporates genomic data along with environmental and operational parameters into data-mining datasets has demonstrated significant increases in accurately predicting process stability and performance of small-scale wastewater systems compared to models developed without consideration of microbial community dynamics. The predictive models developed through the construction of artificial neural networks have potential to inform engineering decisions for optimized performance and stability of environmental biotechnologies in full-scale systems. Development and implementation of this approach will not only progress the understanding and control of microbial communities that inhabit environmental biotechnologies but may be expanded to other microbiomes such as those associated with human health and biogeochemical cycles.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.