To　realize　the　stable　control　of　furnace　temperature　in　a　municipal　solid　waste　incineration　(MSWI)　process,　a　nonlinear　model　predictive　control　(NMPC)　method　for　furnace　temperature　is　proposed　in　this　paper.　First,　using　the　grate　temperature　and　primary　air　temperature　as　the　intermediate　variables,　a　new　MSWI　furnace　temperature　control　structure　is　obtained　by　integrating　the　cascade　control　strategy　into　NMPC.　Then,　the　stochastic　configuration　network　(SCN)　is　used　to　establish　the　furnace　temperature　static　nonlinear　prediction　model　offline,　and　the　output　weights　of　the　hidden　layer　neurons　of　the　SCN　are　updated　online　through　the　recursive　least　square　method,　so　the　furnace　temperature　dynamic　nonlinear　prediction　model　is　established.　Finally,　an　improved　rolling　optimization　strategy　is　obtained　by　integrating　the　improved　seagull　optimization　algorithm　with　the　set　value　evaluation　and　learning　model,　which　is　used　to　improve　the　solution　accuracy　and　efficiency　of　NMPC　rolling　optimization.The　experimental　results　show　that　the　dynamic　nonlinear　prediction　model　of　furnace　temperature　can　predict　the　furnace　temperature　accurately.　The　proposed　control　method　has　good　adaptability　and　robustness,　and　can　realize　the　stable　control　of　furnace　temperature　in　the　MSWI　process.　©　2024　South　China　University　of　Technology.　All　rights　reserved.