Accurate　prediction　of　nitrogen　oxides　(NOx)　is　crucial　for　improving　the　efficiency　of　denitrification　systems　in　municipal　solid　waste　incineration　(MSWI)　process.　Due　to　the　change　of　feed　composition　and　operation　mode,　it　is　difficult　to　predict　NOx　emissions　with　complex　dynamics.　For　this　reason,　a　dynamic　modular　neural　network　(DMNN)　is　proposed　for　NOx　emissions　prediction　in　MSWI　process.　First,　a　principal　component　analysis　(PCA)-based　dynamic　task　decomposition　method　is　proposed,　and　then　the　original　task　with　time　varying　characteristic　is　divided　into　several　sub-tasks　for　effectual　handing.　Next,　an　adaptive　long　short-term　memory　(ALSTM)　network　is　designed　driven　by　the　corresponding　sub-task.　Then,　the　nonlinearity　between　dominant　variables　and　NOx　value　is　learned　to　guarantee　the　prediction　accuracy.　Finally,　the　merits　of　proposed　DMNN　are　confirmed　on　a　benchmark　and　real　industrial　data　of　a　MSWI　process.　The　experimental　results　further　demonstrate　the　superiority　and　potential　of　DMNN　for　industrial　applications.