To　achieve　efficient　operation　of　the　wastewater　treatment　plant(WWTP),　it　is　necessary　to　establish　a　model　that　accurately　describes　the　behavior　of　the　plan.　In　this　paper,　the　radial　basis　function　neural　network　(RBFNN)　is　utilized　in　the　modeling　of　the　WWTP　basing　on　the　available　influent　and　effluent　data.　Considering　the　bounded　modeling　error,　linear-in-parameters　set　membership　identification　algorithm　is　used　to　describe　an　uncertain　set　of　each　vector　representing　the　weights　of　the　links　between　all　the　hidden　neurons　and　one　output　neuron.　Comparing　with　the　existing　methods　which　are　all　proposed　for　a　single　effluent　variable,　the　method　here　builds　a　predictor　model　which　can　compute　confidence　intervals　for　multiple　effluent　variables　simultaneously　according　to　the　values　of　the　influent　variables.　The　confidence　intervals　can　characterize　the　existence　ranges　of　the　effluent　variables,　such　that　reliable　estimates　of　them　are　obtained.　By　the　estimates,　the　effluent　quality　or　the　WWTP　performance　can　be　evaluated.　Besides,　the　interval　predictor　model　is　also　applied　to　the　fault　detection　and　isolation　of　the　WWTP　to　realize　reliable　operation.　The　experiment　results　show　the　satisfying　performance　of　the　proposed　method.　©　All　Right　Reserved.