Prediction of Flooding Velocity in Packed Towers Using Least Squares Support Vector Machine

The flooding velocity is an important but difficult to accurately predict parameter for the packed column design. With the appearance of new packing shapes, traditional empirical models are insufficient to satisfy the requirement of engineering applications. In this paper, a novel approach using least squares-support vector machine (LS-SVM) is proposed to predict the flooding velocity in the randomly dumped packed towers. To evaluate the performance of the LS-SVM model applied to predict the flooding velocity, it is compared with the traditional empirical models and the neural network models. It is found that the LS-SVM model can provide the best performance of all, with an average absolute relative error less than 8%. The results demonstrate that LS-SVM offers an alternative approach to model and predict the flooding velocity in the randomly dumped packed towers.