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

Texas A&M Engineering Experiment Station, College Station TX

Investigators

Abstract

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.