Furnace　temperature　(FT)　is　a　key　variable　in　municipal　solid　waste　incineration　(MSWI)　processes,　influenced　by　many　manipulated　variables　and　directly　impacting　pollutant　concentrations　in　exhaust　gas.　Domain　experts　cannot　achieve　the　optimal　FT　setpoint　value　under　manual　control,　leading　to　abnormal　pollutant　emission　concentrations.　To　address　this,　we　propose　an　intelligent　optimal　control　framework　for　FT　aiming　to　minimize　pollutant　emission　concentration.　First,　the　FT　controlled　object　model　is　established　using　the　Tikhonov　regularization-least　regression　decision　tree　(TR-LRDT)　algorithm.　Then,　based　on　the　experience　of　domain　experts,　a　multi-loop　controller　is　developed　using　an　improved　single　neuron　adaptive　PID　(ISNA-PID)　algorithm　to　stabilize　FT.　Next,　after　establishing　NOx　and　CO2　indicator　models,　the　particle　swarm　optimization　(PSO)　algorithm　is　employed　to　determine　the　FT　setpoint　value　in　terms　of　minimum　pollutant　emission　concentration.　Finally,　the　FT　intelligent　optimal　control　framework　is　verified.　Experimental　results　indicate　that　the　optimal　FT　setpoint　value　can　reduce　NOx　and　CO2　emission　concentrations　by　19.93　%　and　6.99　%,　respectively.