Model for optimizing control of the steel heat treatment in the continuous hot-dip galvanizing

The paper proposes a model to optimize control of a steel strip temperature in manufacture of a galvanized sheet metal product to reduce the fuel costs. Based on reviewing proposals for the strip temperature control, it shows that optimization is hindered by complexity in obtaining a suitable model and guaranteeing stability. The model should forecast accurately not only the strip temperature, but also the effect of various control actions on the heat losses. However, a relatively low effect of certain control actions on the signals available for monitoring is difficult to detect against the background of errors caused by simplified description of the heat exchange and action of the unknown disturbances. As a result, the effect of such actions as air consumption in the fuel combustion and the number of burners on the fuel costs is uncertain. To solve the problem, a method of model synthesis is applied based on determining its structure and settings by testing according to the technological process data for a significant time interval. The method involves segmenting the process data in time based on the product range disturbances and significant alterations in the line speed. For each segment, disturbances are determined separately by the alteration rate in the signal values that are unchanged during a time segment. The goal is to obtain such a structure and settings of the model, where segments with the abnormally low accuracy in forecasting the working space temperature, as well as the exhaust flue gases temperature, are missing. As a result, a model is obtained that operates with signals in the form of increments relative to the initial time for a segment, which allows for control optimization to reduce heat losses with the exhaust flue gases. Assessment of the possibility of accounting for the heat losses through the walls during optimization shows that this requires introduction of additional means in monitoring temperature distribution in the wall or the models operation with the absolute signal values. The proposed model does not require significant computing resources and allows the use of a simple method proposed by A.A. Krasovsky in monitoring the optimization, which makes it possible to implement it directly in the process controllers.
EDN: UCLCJJ, https://elibrary/uclcjj

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