PDF《博士学位论文公示材料》

s the development trend of strip production process. Restricted to the current conditions such as fluctuation of coming material, equipment capacity and detection devices, there are many problems to be solved to achieve accurate strip heat treatment by process optimization and control. Therefore, the nation has included it in the thirteenth five year plan as the research focus. This thesis aims at study of the process optimization and control during strip heat treatment by applying mathematical and intelligent model-based methodology, which is of utmost importance to realize green and intelligent metallurgical industry. As for the strip heat treatment, this research was carried out in the following aspects: first, the annealing temperature optimization method, strip temperature control in heating and cooling section were systematically studied during hot dip galvanizing continuous annealing process;

second, further to the relatively unrepresentative annealing cooling process, strip temperature control in laminar cooling process after hot rolling was investigated. This thesis focuses on the mathematical models, temperature observer design, as well as optimization and control strategies. The detailed research work is as follows: (1) The annealing temperature setting strategy based on data mining methods and its accuracy online assessment method were established. In the proposed method, the initial annealing process of a typical steel grade was derived by the hot dip simulator. Then based on the real-time data in real production and test data in laboratory, the IBK algorithm was used to optimize annealing temperature, and neural network algorithm was used to evaluate the optimized annealing temperature online. The practical application and numeric results show that, the algorithm can set a suitable annealing temperature for the processed strip, considering its targeted mechanical properties and other slight differences. It'

s worth mentioning that this kind of methodologies can not only be used for setting the annealing temperature but also extrapolated to optimize other heat treatment processes, such as cooling rate, final cooling temperature, etc. (2) The optimization and control strategy of annealing temperature based on temperature observer was developed. Firstly, one dimensional unsteady heat conduction model ignoring the phase transformation was established, coupling the heat transfer model through boundary conditions. In PH-NOF, the convection flux between the strip and furnace gas was calculated by experience criterion equation, while the strip radiation flux was obtain by equivalent circuit. In RTF, the imaginary plane equivalent emissivity method combined with equivalent circuit was used. Secondly, the temperature observer was established on the basis of the physical models, and parameters of the observer were modified based on the nonlinear least square optimization method. At last, based on the observer, the optimization strategy employed the fuel gas flow in PH-NOF and furnace temperature in RTF to control strip temperature during the annealing process. Actual industrial experiment and simulation results show that temperature observer is able to track the strip temperature, based on which, the optimization and control strategy can control the strip annealing temperature accurately. (3) The cooling path control method based ........