Computer simulations of the air-mist cooling of steel flat products based on an analytic predictive control model

A computer software was created to realize the previously developed analytic model for predictive control of the air-mist cooling for a steel flat product. The analytic control model comprises an analytic model of the control object and a novel method for the cooling predictive control. The control object model in turn is based on the previously deduced differential equation analytically defining target time dependence of the water-air volume relation providing meeting the «additivity» rule requirements to the volumes and consumptions of the mixture components. Further, the metal temperature, the heat transfer coefficient, heat fluxes and other cooling process characteristics are determined analytically together with their quantitative relations with the continuous casting technology parameters: casting speed, liquid metal initial temperature etc. High adequacy of the object model was shown by the metal target cooling trajectory predictions. The predictive control method proposed is based on the on-line temperature and the water-air volume relation measurements with further quantitative predictions of above values for each next time of their measurement with use of the object analytic model. The corresponding target levels of the above parameters in turn are also specified by the same model use. The control action at a time is generated based on comparing the predicted and the target levels of the control parameters for the nearest measurement time. A corresponding computer JavaScript based software was developed. The software was applied to control a simulated air-mist secondary cooling of continuous cast slabs. The real values of the continuous casting technology parameters and their scattering ranges were used as input simulation characteristics. Over twice reduction in simulated slab temperature scattering ranges was obtained by the pro-posed model application to control the secondary cooling comparing the uncontrolled process. High workability of the proposed control modeling approach was demonstrated