Incineration　is　one　of　the　main　techniques　for　municipal　solid　waste　(MSW)　treatment,　which　is　widely　used　in　most　countries.　The　stable　operation　of　the　MSW　incineration　(MSWI)　process　depends　on　three　key　controlled　variables,　namely　furnace　temperature,　flue　gas　oxygen　concentration,　and　boiler　steam　flow.　The　first　problem　to　develop　the　intelligent　control　and　optimization　technology　of　the　MSWI　process　is　to　establish　the　controlled　object　model.　Thus,　a　data-driven　model　based　on　ensemble　decision　tree　algorithm　is　constructed　for　the　above-mentioned　three　controlled　objects　in　this　paper.　It　is　an　ensemble　combination　of　random　forest　(RF)　and　gradient　boosting　decision　tree　(GBDT).　First,　the　random　sampling　strategy　is　used　to　pre-process　a　large　amount　of　MSWI　process　data　to　obtain　modeling　sub　datasets.　Second,　the　training　subsets　are　used　to　build　the　RF　model.　Furthermore,　a　GBDT　model　with　a　serial　structure　is　constructed　by　using　the　gradient　iterative　RF　model.　Finally,　the　predicted　values　are　produced　by　the　simple　weighted　average　of　the　predicted　output　of　the　RF　and　GBDT　sub-model.　The　results　show　the　effectiveness　of　the　proposed　method　on　the　basis　of　the　real-time　process　data　obtained　in　the　actual　MSWI　process.　©　2021　IEEE