The　optimization　control　method　for　wastewater　treatment　process　(WWTP)　is　the　key　to　improving　its　operating　efficiency　and　improving　its　operating　effect.　However,　due　to　the　variable　influent　pollutant　load　in　WWTP,　there　are　multiple　operating　conditions,　and　the　changes　are　frequent,　making　it　difficult　for　the　optimal　control　system　to　achieve　optimizing　operation.　Therefore,　how　to　design　an　optimal　control　method　to　cope　with　changes　in　various　operating　conditions　and　ensure　that　water　quality　indicators　such　as　total　nitrogen　and　total　phosphorus　are　up　to　standard　is　a　challenging　problem　that　needs　to　be　solved　in　WWTP.　This　study　designs　a　multi-operating　optimization　control　method　with　domain　adaptive　(MOOC-DA)　for　WWTP.　First,　a　multi-operating　optimization　objective　model　of　WWTP　is　established　to　capture　the　time　series　characteristics　of　operation　energy　consumption　and　effluent　quality,　and　achieve　accurate　prediction　of　operation　indicators.　Second,　a　multi-operating　optimization　setting　method　based　on　the　multi-task　domain　adaptive　is　designed　to　ensure　that　the　effluent　quality　meets　the　standard　in　multiple　operating　conditions.　Finally,　an　optimal　setting　tracking　control　method　based　on　multi-task　fuzzy　neural　network　is　designed　to　realize　multi-operating　optimization　operation　of　WWTP.　At　the　same　time,　the　proposed　MOOC-DA　is　compared　with　other　optimization　control　methods　based　on　the　activated　sludge　model　No.　2d　simulation　platform　to　verify　the　effectiveness.　The　results　indicate　that　MOOC-DA　can　realize　the　optimal　operation　of　multiple　operating　conditions　in　WWTP.　©　2024　Chinese　Academy　of　Sciences.　All　rights　reserved.