In　this　paper,　the　structure　optimized　support　vector　machine　(SVM)　model　was　applied　to　predict　the　membrane　permeate　flux　during　dead-end　microfiltration　of　activated　sludge　suspensions　from　sequencing　batch　reactor　(SBR)　with　different　experimental　samples.　The　membrane　permeate　flux　was　considered　as　a　function　of　mixed　liquor　suspended,　temperature,　dissolved　oxygen,　hydraulic　retention　time,　transmembrane　pressure,　and　operating　time.　Excellent　agreements　between　the　predicted　values　of　SVM　model　and　the　experimental　data　demonstrated　that　SVM　model　has　sufficient　prediction　accuracy.　Furthermore,　the　results　showed　that　the　predicted　values　of　SVM　model　agreed　well　with　experimental　data　at　different　experimental　samples　in　comparison　with　back　propagation　artificial　neural　network　(BP-ANN)　model.　From　the　simulation　results,　the　conclusion　can　be　derived　that　SVM　model　outperforms　BP-ANN　model　when　the　experimental　samples　sizes　are　small.