Search

Performance of controlled laminar cooling is usually poor because of the difficulty in continuous online temperature measurement and the complex nature of the laminar cooling process (e.g., highly nonlinear, time varying). This paper developed a hybrid control strategy for the laminar cooling process that integrates Radial Basis Function (RBF) networks and Case-Based Reasoning (CBR). The spraying pattern and the first activated headers are found by a case-based reasoner, while the number of activated headers is calculated in real time by RBF networks. Experimental studies using production data from a hot strip mill show the superior performance of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Due to the complex nature (e.g., highly nonlinear, time varying, and spatially varying) of the laminar cooling process, accurate mathematical modeling of the process is difficult. This paper developed a hybrid model of the laminar cooling process by integrating Radial Basis Function (RBF) networks into the first principles dynamical model. The heat transfer coefficients of water cooling in the dynamical model were found by RBF networks. The developed model is capable of predicting the through-thickness temperature evolutions of the moving strip during the laminar cooling process. Experimental studies using real data from a hot strip mill show the superiority of the proposed model. [ABSTRACT FROM AUTHOR]