To　tackle　the　challenge　of　controlling　furnace　temperature　in　municipal　solid　waste　incineration　(MSWI),　which　is　critical　for　incineration　efficiency　and　pollutant　reduction,　this　paper　introduces　a　model　predictive　control　(MPC)　strategy　utilizing　a　back　propagation　neural　network　(BPNN).　Traditional　PID　struggles　with　the　nonlinearity　and　disturbances　of　process.　Our　approach　involves　constructing　a　BPNN-based　prediction　model　to　forecast　future　furnace　temperatures.　Using　gradient　descent,　we　optimize　the　objective　function　within　a　predetermined　period,　allowing　for　real-time　adjustment　of　control　variables.　This　effectiveness　of　method　is　validated　through　experiments　with　data　from　a　Beijing　MSWI　plant,　demonstrating　enhanced　tracking　and　disturbance　management　capabilities.　©　2024　IEEE.