Nonlinear dynamic simulation and control of large-scale reheating furnace operations using a zone method based model. (5th May 2018) - Hu, Yukun; Tan, C.K.; Broughton, Jonathan; Roach, Paul Alun; Varga, Liz
Highlights: New method for the simulation of nonlinear dynamic operations of reheating furnaces. Detailed radiation heat transfer in a time-varying computational domain. Feedback and feedforward combined self-adapting predictive control scheme. The model was verified by using radiometric imaging camera and SCADA data. A fuel saving of about 6% can be achieved by the control scheme. Abstract: Modern reheating furnaces are complex nonlinear dynamic systems having heat transfer performances which may be greatly influenced by operating conditions such as stock material properties, furnace scheduling and throughput rate. Commonly, each furnace is equipped with a tailored model predictive control system to ensure consistent heated product quality such as final discharge temperature and temperature uniformity within the stock pieces. Those furnace models normally perform well for a designed operating condition but cannot usually cope with a variety of transient furnace operations such as non-uniform batch scheduling and production delay from downstream processes. Under these conditions, manual interventions that rely on past experience are often used to assist the process until the next stable furnace operation has been attained. Therefore, more advanced furnace control systems are useful to meet the challenge of adapting to those circumstances whilst also being able to predict the dynamic thermal behaviour of the furnace. In view of the above, this paper describes in detail an episode of actual transient furnace operation, and demonstrates a nonlinear dynamic simulation of this furnace operation using a zone method based model with a self-adapting predictive control scheme. The proposed furnace model was found to be capable of dynamically responding to the changes that occurred in the furnace operation, achieving about ±10 °C discrepancies with respect to measured discharge temperature, and the self-adapting predictive control scheme is shown to outperform the existing scheme used for furnace control in terms of stability and fuel consumption (fuel saving of about 6%).