A suit of online self-adapting control （OSAC） approach has been developed to predict and optimize annealing craft system. The approach consists of three critical parts including prediction module, self-adapting opti...A suit of online self-adapting control （OSAC） approach has been developed to predict and optimize annealing craft system. The approach consists of three critical parts including prediction module, self-adapting optimization module, and self-learning amendment module. Firstly, the prediction module and self- adapting optimization module are based on the modeling methods. The self-adapting optimization module consists of two parts including ＂reappearance of annealed process＂ and ＂optimization of subsequent annealing process＂. Secondly, the self-learning amendment module, based on furnace atmosphere, equipment performance, and compensation coefficients, is designed to improve the accuracy of optimization results. The results obtained from the proposed approach, usually finished in about 3 min, are in good agreement with the test values, such as the deviation of temperature for hot-spot and cold-spot are within 10 K, the relative errors are within 1.1%, and the accuracy of annealing for heating period is increased by using self-learning amendment module.展开更多
基金Supported by the Specialized Research Project of WuhanIron and Steel Corporation (20050038)
文摘A suit of online self-adapting control （OSAC） approach has been developed to predict and optimize annealing craft system. The approach consists of three critical parts including prediction module, self-adapting optimization module, and self-learning amendment module. Firstly, the prediction module and self- adapting optimization module are based on the modeling methods. The self-adapting optimization module consists of two parts including ＂reappearance of annealed process＂ and ＂optimization of subsequent annealing process＂. Secondly, the self-learning amendment module, based on furnace atmosphere, equipment performance, and compensation coefficients, is designed to improve the accuracy of optimization results. The results obtained from the proposed approach, usually finished in about 3 min, are in good agreement with the test values, such as the deviation of temperature for hot-spot and cold-spot are within 10 K, the relative errors are within 1.1%, and the accuracy of annealing for heating period is increased by using self-learning amendment module.
