Parameter Estimation by Inverse Solution Methodology Using Genetic Algorithms for Real Time Temperature Prediction Model of Ladle Furnace

摘要

In the process of developing mechanistic dynamic models which faithfully represent characteristics of a process, accurate estimation of parameters is a very crucial step. Inverse solution methodology combined with evolutionary optimization algorithms has been proved to be a very potential technique for offline parameter estimation. Advanced industrial automation systems capable of generating and storing enormous volumes of sensory data have indeed fostered the usage of this approach. In the present work, inverse methodology combined with Genetic Algorithms has been successfully employed for estimating parameter of a dynamic model aimed to predict liquid steel temperature in Ladle Furnace. The parameter evaluated in this study was heat transfer coefficient of ladle refractory walls. The optimal value evaluated was obtained as 10.62 W/m~2.K.