(345b) Energy-Efficient Smart Manufacturing: Application to the Control of Austenitizing Furnaces

Energy constitutes the second largest operating cost in manufacturing industries within the United States [1]. In many manufacturing facilities, significant energy efficiency improvements could be gained from advanced sensing, monitoring, control and optimization techniques. However, the expertise required to implement them is not available in-house, and the associated capital expenditures often exceed the resources available to small and medium-sized manufacturers. The scope of our smart manufacturing work is to democratize access to model-based optimization for a broad class of manufacturing processes by developing a generic modeling and optimization platform, which can be modularized, configured and deployed with minimal expertise in a largely existing communication and control infrastructure.

Within this broader scope, this paper describes new developments concerning the operation and control of an austenitizing furnace. Austenitizing furnaces are high-temperature-high-throughput processes in which steel billets are heated in a semi-batch manner. The billets travel on rollers through the furnace, which is heated by ceiling and floor radiation tubes spanning its entire length. The overall heat input from the burners is on the order of 10 million BTU per hour to achieve furnace temperatures of 1400F.

Many such furnaces are still operated based on heuristics focused on the material properties, rather than overall process operation and efficiency, making this class of systems a prime candidate for optimization-based control. Furthermore, advanced sensing and online process monitoring are required to detect the potential for temperature maldistribution, which is a significant contributor to quality control issues and heat losses.

In our paper, we develop a mathematical model of an industrial austenitizing furnace, which encompasses the burner, hearth and billet domains. We validate the model based on data collected from furnace operations at an industrial partner. Based on this model, we develop a model predictive control scheme aimed at minimizing energy use while following the austenitization program prescribed for the metal products. The control scheme contains an additional feedback element based on the quality control checks that the parts are subjected to after they leave the furnace. Simulation results emphasize the energy savings that can be derived from model-based, smart manufacturing. Finally, we present incipient results concerning implementing these tools and algorithms in a smart manufacturing platform that will facilitate their wide deployment across manufacturing facilities in the United States.

References:

[1] Energy Use, Loss and Opportunities Analysis, US Manufacturing and Mining, US Department of Energy Report, 2004.