Application of Genetic Algorithm and Least Squares Support Vector Machines in Laminar Cooling Process

In order to improve the accuracy of temperature prediction model of laminar flow cooling system, a new model is introduced to control the temperature of steel plate cooled down by laminar flow. The new prediction model can express the laminar cooling process unambiguously, in which, the least squares support vector machines (LS-SVM) learning machine is introduced to promote the accuracy of the temperature-varying parameters and the genetic algorithm is proposed to identify the system of the temperature-varying parameters. The new method enables the intelligent laminar flow cooling model to monitor the process of a laminar flow cooling process rapidly, and provide it self-study ability and highly accurate results, the temperature of the length and thickness direction of the iron can be calculated exactly. The comparison between real producing data and results of the intelligent model implies that our new method is highly effective.