This　paper　tackles　the　challenge　of　effectively　regulating　furnace　temperature　in　the　municipal　solid　waste　incineration　(MSWI)　process.　It　proposes　an　approach　that　integrates　reinforcement　learning　with　traditional　PID　control.　Firstly,　an　analysis　of　the　characteristics　of　furnace　temperature　control　is　conducted　to　identify　the　key　manipulated　variables　affecting　it.　Subsequently,　a　control　strategy　specifically　tailored　for　furnace　temperature　is　developed,　employing　online　adaptive　PID　with　a　BPNN-fitted　actor-critic　network　(BPNN-ACN-PID).　Finally,　the　effectiveness　of　the　proposed　method　is　validated　through　control　experiments　using　actual　process　data　from　a　MSWI　plant　in　Beijing.　©　2024　IEEE.