Air　pollution　control　poses　a　major　problem　in　the　implementation　of　municipal　solid　waste　incineration　(MSWI).　Accurate　prediction　of　nitrogen　oxides　(NOX)　concentration　plays　an　important　role　in　efficient　NOX　emission　controlling.　In　this　study,　a　modular　long　short-term　memory　(M-LSTM)　network　is　devel-oped　to　design　an　efficient　prediction　model　for　NOX　concentration.　First,　the　fuzzy　C　means　(FCM)　algo-rithm　is　utilized　to　divide　the　task　into　several　sub-tasks,　aiming　to　realize　the　divide-and-conquer　ability　for　complex　task.　Second,　long　short-term　memory　(LSTM)　neural　networks　are　applied　to　tackle　corre-sponding　sub-tasks,　which　can　improve　the　prediction　accuracy　of　the　sub-networks.　Third,　a　cooperative　decision　strategy　is　designed　to　guarantee　the　generalization　performance　during　the　testing　or　applica-tion　stage.　Finally,　after　being　evaluated　by　a　benchmark　simulation,　the　proposed　method　is　applied　to　a　real　MSWI　process.　And　the　experimental　results　demonstrate　the　considerable　prediction　ability　of　the　M-LSTM　network.(c)　2022　The　Chemical　Industry　and　Engineering　Society　of　China,　and　Chemical　Industry　Press　Co.,　Ltd.　All　rights　reserved.