Data-driven　model　plays　an　important　role　in　monitoring　wastewater　treatment　processes　(WWTPs).　However,　since　online　data-driven　models　only　focus　on　modeling　the　current　operational　status,　it　is　a　challenge　to　determine　an　appropriate　model　to　guarantee　the　prediction　performance　for　WWTPs　with　multiple　operational　conditions.　Therefore,　an　interval　type-2　fuzzy　neural　network　with　multi-gradient　learning　(MGL-IT2FNN)　is　proposed　for　modeling　WWTPs　in　this　paper.　First,　a　condition　detection　mechanism　is　designed　to　detect　the　switching　points　of　operational　conditions　of　WWTPs.　This　mechanism　can　guide　the　learning　process　of　IT2FNN.　Second,　when　operational　condition　switching　occurs,　a　multi-gradient　optimization　algorithm　is　developed　to　adjust　the　parameters　of　IT2FNN,　which　is　able　to　balance　the　previous　knowledge　retention　ability　and　the　learning　ability　of　new　operational　conditions.　Third,　MGL-IT2FNN　is　employed　to　capture　the　operational　features　under　different　conditions.　Finally,　the　presented　MGL-IT2FNN　is　applied　to　monitor　the　total　nitrogen　removal　of　WWTPs　with　multiple　operational　conditions.　The　experiment　signifies　that　the　accuracy　and　effectiveness　of　MGL-IT2FNN　are　greatly　outstanding　in　WWTP　modeling.　©　2023　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.