Municipal　solid　waste　incineration　(MSWI)　is　a　dynamic　industrial　process　involving　complex　physical　and　chemical　reactions.　Due　to　the　uncertain　municipal　solid　waste　(MSW)　composition　and　dynamic　operation　conditions,　it　is　difficult　to　guarantee　optimal　operation　for　the　MSWI　process.　To　solve　this　problem,　a　data-driven　optimal　control　scheme　is　proposed　with　a　hierarchical　control　structure.　In　the　set　points　optimization　stage,　an　online　adaptive　fuzzy　neural　network　(OAFNN)　is　designed　to　construct　the　objective　functions,　including　combustion　efficiency　and　NOx　emission　concentration.　An　adaptive　mutation　particle　swarm　optimization　(AMPSO)　algorithm　is　developed　to　obtain　the　optimal　set　points　of　oxygen　content　in　flue　gas.　In　the　control　stage,　a　double　long　short-term　memory　neural　networks-based　model　predictive　control　(DLSTM-MPC)　strategy　is　exploited.　A　self-organizing　long　short-term　memory　(SOLSTM)　network　is　employed　to　predict　the　oxygen　content　in　flue　gas　with　a　compact　structure.　To　reduce　the　influences　from　dynamic　disturbances　and　further　improve　the　tracking　control　performance,　another　long　short-term　memory　(LSTM)　neural　network　is　established　to　correct　the　prediction　results.　Finally,　the　set　points　optimization　method　and　DLSTM-MPC　strategy　are　combined　to　realize　the　optimal　operation　of　the　MSWI　process.　The　effectiveness　of　the　proposed　optimal　control　scheme　is　verified　by　real　industrial　data.　The　experimental　results　demonstrate　that　the　optimal　control　scheme　can　achieve　promising　tracking　control　performance　of　oxygen　content　in　flue　gas　and　improve　the　operational　performance　of　the　MSWI　process.　IEEE