Collaborative Research: An Integrated Approach to Modeling, Decision-Making and Control for Energy Efficient Manufacturing

该项目将支持在制造业中提高能源效率和促进健康室内环境的基础研究。为了实现美国积极的脱碳目标，除了当前的能源发电组合外，还需要对需求侧能源管理进行重大转变。在典型的制造设施中，最重要的能源消耗来源是制造系统和环境控制系统，如加热，通风和空调。这两个系统在生产运营、动态能源需求/消耗和室内条件方面密切相关。然而，目前的制造系统控制策略缺乏与设施能源管理和室内环境控制的有效整合，阻碍了整体能源效率的提高。该补助金促进多学科研究，以全面了解智能制造设施的能源效率，减少能源浪费，提高整体制造效率，降低制造成本，并促进行业工人的福祉。这项研究的成果将为环境、社会和美国能源格局带来长期利益。此外，本研究符合工业需求，促进多样性，鼓励参与研究的代表性不足的群体，并有助于工程教育的进步。本研究致力于开发复杂系统的集成建模，多智能体决策和分布式控制的创新技术。该研究团队旨在构建制造系统和环境控制系统的动态模型，全面了解制造设施各组件之间的动态相互作用。此外，一个集成的工厂能源模型将建立使用图形神经网络，弥合差距之间的传统分离管理的环境控制和制造系统。此外，将设计一个分层控制框架，以整合监督决策和自适应控制方案，同时考虑生产操作和设施能源管理。该团队将开发一种多智能体强化学习算法，以支持图形神经网络描述的复杂系统的在线决策。数据驱动的自适应控制算法将被用来处理系统的不确定性和环境干扰使用学习为基础的方法。这项基础研究有可能克服传统稳态分析应用于单独制造系统和环境控制的局限性，将能源和生产效率提升到新的水平。此外，该方法的通用性将有助于更广泛的工程系统建模和控制领域。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This project will support fundamental research in improving energy efficiency and promoting a healthy indoor environment in the manufacturing industry. To achieve the aggressive decarbonization goal of the United States, a significant transformation in demand-side energy management is needed alongside the current energy generation mix. In typical manufacturing facilities, the most significant sources of energy consumption are the manufacturing systems and the environmental control systems, such as heating, ventilation, and air-conditioning. These two systems are closely interconnected in terms of production operations, dynamic energy demand/consumption, and indoor conditions. However, the current control strategy of manufacturing systems lacks effective integration with facility energy management and indoor environmental control, hindering overall energy efficiency improvements. This grant promotes multi-disciplinary research to establish a comprehensive understanding of energy efficiency in smart manufacturing facilities to reduce energy waste, enhance overall manufacturing efficiency, lower manufacturing costs, and promote the well-being of industry workers. The outcomes of this research will yield long-term benefits for the environment, society, and the U.S. energy landscape. Moreover, this research aligns with industrial needs, fosters diversity, encourages the involvement of underrepresented groups in research, and contributes to the advancement of engineering education.This research endeavors to develop innovative technologies for integrated modeling of complex systems, multi-agent decision-making, and distributed control. The research team aims to construct dynamic models of manufacturing systems and environmental control systems, gaining comprehensive insights into the dynamic interactions among various components of the manufacturing facility. Furthermore, an integrated factory energy model will be established using a graph neural network, bridging the gap between traditionally separate management of environmental control and manufacturing systems. Additionally, a hierarchical control framework will be designed to integrate supervisory decision-making and adaptive control schemes, considering both production operations and facility energy management. The team will develop a multi-agent reinforcement learning algorithm to support online decision-making for the complex system described by the graph neural network. Data-driven adaptive control algorithms will be employed to handle system uncertainties and ambient disturbances using a learning-based approach. This fundamental research has the potential to overcome the limitations of traditional steady-state analysis applied to separate manufacturing systems and environmental control, elevating energy and production efficiency to new levels. Furthermore, the generic nature of the methods will contribute to the broader field of engineering system modeling and control.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.