Wastewater　treatment　process　(WWTP)　is　operated　with　multiple　conditions.　Accurately　identify　these　conditions　and　satisfy　the　different　requirements　are　the　keys　to　guarantee　the　optimal　operation　of　WWTP.　To　solve　this　problem,　a　dynamic　optimal　control　(DOC)　strategy　is　designed　in　this　paper.　First,　with　the　aid　of　the　predicting　models　by　adaptive　fuzzy　neural　network　for　effluent　nitrate　and　total　nitrogen,　different　operating　conditions　can　be　identified.　Corresponding　to　each　condition,　changing　number　of　operating　objectives　are　formulated　to　match　the　different　requirements.　Second,　due　to　the　changeable　operating　objectives　can　cause　the　expanded/contracted　dimension　of　Pareto-optimal　set,　a　dynamic　multi-objective　particle　swarm　optimization　algorithm　is　developed.　In　this　algorithm,　self-adjusting　mechanism　of　population　size　and　global　optimal　solution　are　designed　to　derive　the　optimal　solutions　of　control　variables.　Third,　fuzzy　controllers,　matched　with　the　different　conditions,　are　designed　to　trace　these　optimal　solutions.　Finally,　the　effectiveness　of　DOC　strategy　is　tested　on　a　real　WWTP.　The　results　demonstrate　that　this　proposed　DOC　strategy　enables　to　achieve　promising　operating　performance.　Note　to　Practitioners-This　article　aims　to　develop　an　optimal　control　strategy　to　match　the　multiple　conditions　and　improve　the　operating　performance.　To　achieve　this　goal,　a　dynamic　optimal　control　(DOC)　strategy,　with　the　consideration　of　different　operating　conditions,　is　designed.　Two　key　points　are　contained,　the　identification　of　multiple　operating　conditions　and　the　satisfaction　of　different　requirements.　Due　to　each　condition　confronts　with　different　requirements,　it　is　important　to　accurately　identify　the　conditions　and　construct　the　corresponding　operating　objectives.　Based　on　the　obtained　operating　data,　changeable　operating　objectives　are　established　to　describe　the　different　conditions.　In　addition,　to　satisfy　the　operating　requirements,　it　is　necessary　to　optimize　the　changeable　objectives,　but　the　changes　in　the　number　of　objectives　can　cause　the　expanded/　contracted　of　Pareto-optimal　set.　A　dynamic　multi-objective　particle　swarm　optimization　algorithm,　with　self-adjusting　mechanism　of　population　size　and　global　optimal　solution,　is　designed　to　deal　with　this　kind　of　optimization　problem.　The　effectiveness　and　applicability　of　the　proposed　DOC　strategy　are　evaluated　on　a　pilot　platform　of　a　real　WWTP,　showing　the　improvement　on　the　operating　performance.　These　advantages　can　be　valuable　for　transplanting　this　strategy　to　other　real　wastewater　treatment　plants　to　realize　the　optimal　operation.