Hierarchical nonlinear optimization-based controller of a continuous strip annealing furnace. (April 2018)

• Record Type: Journal Article
• Authors: Strommer, S.; Niederer, M.; Steinboeck, A.; Kugi, A.
• Is Part Of: Control engineering practice. Volume 73(2018)
• Page Start: 40
• Page End: 55
• Publication Date: 2018-04
• Subjects: Nonlinear model predictive control -- Direct- and indirect-fired strip annealing furnace -- Reheating and heat treatment of metal strips -- Nonlinear MIMO system -- Unconstrained optimization -- Gauss–Newton method; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2017.12.005
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Continuous strip annealing furnaces are complex multi-input multi-output nonlinear distributed-parameter systems. They are used in industry for heat treatment of steel strips. The product portfolio and different materials to be heat-treated is steadily increasing and the demands on high throughput, minimum energy consumption, and minimum waste have gained importance over the last years. Designing a furnace control concept that ensures accurate temperature tracking under consideration of all input and state constraints in transient operations is a challenging task, in particular in view of the large thermal inertia of the furnace compared to the strip. The control problem at hand becomes even more complicated because the burners in the different heating zones of the considered furnace can be individually switched on and off. In this paper, a real-time capable optimization-based hierarchical control concept is developed, which consists of a static optimization for the selection of an operating point for each strip, a trajectory generator for the strip velocity, a dynamic optimization routine using a long prediction horizon to plan reference trajectories for the strip temperature as well as switching times for heating zones, and a nonlinear model predictive controller with a short prediction horizon for temperature tracking. The mass flows of fuel and the strip velocity are the basic control inputs. The underlying optimization problems are transformed to unconstrained problems and solved by the Gauss–Newton method. The performance of the proposed control concept is demonstrated by an experimentally validated simulation model of a continuous strip annealing furnace at voestalpine Stahl GmbH, Linz, Austria. Graphical abstract: