Details

Autor(en) / Beteiligte

• Xi, Xuejie
• Wang, Zhan
• Zhang, Jing
• Zhou, Yuenan
• Chen, Na
• Shi, Longyue
• Wenyue, Dong
• Cheng, Lina
• Yang, Wentao

Titel

Prediction of impacts of fabrication conditions on the filtration performance of homemade VC-co-VAc-OH microfiltration membrane by Support Vector Machine (SVM)

Ist Teil von

• Desalination and water treatment, 2013-05, Vol.51 (19-21), p.3970-3978

Ort / Verlag

L'Aquila: Desalination Publications

Erscheinungsjahr

2013

Link zum Volltext

Quelle

Taylor & Francis Journals Auto-Holdings Collection

Beschreibungen/Notizen

• In this study, the support vector machine (SVM) model which was based on restricted data sets (the size of the training set is small or small training sample) was applied to predict the permeate flux and rejection of Bovine serum albumin (BSA) of homemade VC-co-VAc-OH microfiltration membrane as the function of fabrication conditions. The membrane preparation conditions (the solid content, the additive content, environmental temperature, the relative humidity, evaporation time of a volatile solvent, precipitation temperature, and precipitation time) were input variables; pure water flux and rejection of BSA were output variables. The results showed that the detailed relationships between fabrication conditions and filtration performance of the membranes could be established. Excellent agreements between the prediction of SVM model and the experiments validate that SVM model has sufficient accuracy. Furthermore, the results predicted by SVM model were compared with those predicted by artificial neural network (ANN) model which was widely used in the optimization of nonlinear relationships. It is found that the deviations of both the training and the predicting data obtained by SVM model are much smaller than those by ANN models. Hence, SVM model can be used as an efficient approach to optimize fabrication conditions of homemade VC-co-VAc-OH microfiltration membrane.