Prediction of spunbonded nonwoven filtration performance from structural parameter reducts using rough set theory and support vector machine

Abstract

A novel approach to predicting the filtration performance of spunbonded nonwoven using a rough set theory- and support vector machine-based model is presented. The meso-structure of spunbonded nonwoven was characterized using a structural parameter set (O_s) containing nine parameters. Four reducts were extracted from O_s using rough set theory. Twenty models, each based on either a support vector machine (SVM) or a back-propagation artificial neural network (BP-ANN), were established to predict the filtration performance (under varying filtration velocity and particle size) of spunbonded nonwoven by taking the parameters of each reduct and O_s as inputs. The results show that the prediction accuracy of the model that takes thickness, fiber diameter, and pore size as its input parameters is higher than that of any other model, regardless of the model type. Moreover, the predictive power of the SVM-based model was found to exceed that of the BP-ANN-based model.