Application of a self-learning function to an expert system for blast furnace heat control.

Abstract

A self-learning function, in which the statistical methods and production rules were effectively combined, was applied to an expert system for blast furnace heat control to improve controllability of temperature and chemical composition of hot metal and to improve mentenance of the system.This self-learning function consists of a short term self-learning function and a long term self-learning function. The former has been in operation since the expert system for blast furnace heat control, which was named BAISYS, was started and the latter has been utilized since March, 1988.In the short term self-learning function, the reference values of sensor data, hot metal temperature differences among tapholes and rising patterns of hot metal temperature in the casting are periodically judged and automatically processed.In the long term self-learning function, the guidance to modify the three-dimensional membership functions and the weight coefficients of all sensors is performed. Through the test application of the self-learning function, the followings were confirmed.(1) Guidance by the self-learning function is very effective for the evaluation of weight coefficients of various sensors.(2) The three-dimensional membership functions for all sensor data, which are made by self-learning function, are applicable.(3) The standard deviations of hot metal temperature and silicon content in hot metal have been decreased and the application ratio of the expert system has been kept at high level by the introduction of the self-learning function.