Predictive Temperature Control of Steel Strip During Hot-Dip Galvanizing Using Thermal Process Model

The paper proposes a system for model predictive temperature control of steel strip in the galvanized sheet metal production. Based on a review of proposals for predictive strip temperature control, we show that one of the main problems is the lack of reliable information about the current temperature state of the heat treatment section, since the temperature of the workspace is controlled locally at individual points. Taking this into account, the proposed system is based on the use of generalized estimates of the temperature of the workspace. This allows for precise predictive control in the absence of complete information about the current temperature state of the section. A generalized temperature estimate is given using a simplified interpretable model based on strip temperature values at the inlet and outlet of the section. To determine the feed forward impact on the power of heating and cooling systems of sections when compensating for disturbances in the product range and line speed, we propose a hybrid model. The model consists of an interpretable and an empirical component based on an artificial neural network. Using the example of a closed cooling section of a continuous hot-dip galvanizing unit, we studied the performance of a generalized temperature estimation stabilization system with a standard PID controller. We identified difficulties due to the complexity of simultaneous effective processing of disturbances according to the task, as well as disturbances caused by errors in the feed forward compensation of disturbances according to the model. To quickly stabilize the generalized temperature estimate in the event of modeling errors, the structure of the control system with two degrees of freedom was chosen. The closed-loop PID controller is configured to handle disturbances caused by modeling errors during predictive control. The direct open control loop is configured to handle disturbances based on a given value of a generalized estimate of the workspace temperature. The performance of the proposed system is demonstrated using the example of a closed strip cooling section. It is shown that the system is able to guarantee control quality even with the maximum possible errors of the hybrid model. The developed hybrid model can also be used to plan the dynamics of temperature changes in the workspace in the long term. The proposed model structures and control systems can also be used for heating sections.