Wastewater　treatment　processes　(WWTPs)　are　operated　under　multiple　operating　conditions.　Designing　an　appropriate　optimal　control　strategy　based　on　the　identification　of　operating　conditions　is　crucial　for　ensuring　the　safe　operation　of　WWTPs.　To　effectively　deal　with　the　problem　of　multiple　operating　conditions　in　WWTPs,　a　data-knowledge-driven　multiobjective　adaptive　optimal　control　(DK-MAOC)　strategy　is　proposed.　First,　a　fuzzy　neural　network　(FNN)　is　employed　as　the　prediction　model　to　obtain　the　concentrations　of　nitrate　and　total　nitrogen.　Then,　the　operating　conditions　of　WWTPs　can　be　determined.　Second,　an　adaptive　objective　function　(AOF)　is　proposed　to　dynamically　adjust　the　weights　of　operating　indices　to　meet　the　operational　requirements　of　each　operating　condition.　In　particular,　the　AOF　integrates　operating　requirements　and　tracking　errors　to　simultaneously　consider　the　feasibility　of　the　controller　when　solving　setpoints.　Third,　due　to　the　differences　in　data　distribution　under　each　operating　condition,　real-time　data　during　condition　changing　is　insufficient　to　accurately　predict.　A　data-knowledge-driven　model,　incorporating　operational　knowledge　into　the　FNN-based　predictive　model,　is　established　to　predict　the　future　dynamics　of　WWTPs.　Finally,　a　collaborative　gradient　descent　algorithm　is　proposed　to　simultaneously　solve　for　setpoints　and　control　laws.　The　effectiveness　of　the　proposed　DK-MAOC　is　tested　on　the　Benchmark　Simulation　Model　No.　1.　The　experimental　results　indicate　that　DK-MAOC　can　effectively　avoid　the　situation　of　effluent　nitrate　nitrogen　and　total　nitrogen　exceeding　the　standards　while　reducing　energy　consumption　of　WWTPs.　Therefore,　the　proposed　DK-MAOC　can　guarantee　optimal　operation　of　WWTPs.