The　accurate　prediction　of　the　nitrogen　oxides　(NOx)　emissions　is　extremely　important　for　pollutant　control　in　municipal　solid　waste　incineration　(MSWI)　process.　Modular　neural　network　(MNN)　provides　a　support　for　predicting　NOx　emissions　to　overcome　the　limitations　of　a　single　model　in　nonlinear　processes　and　different　climatic　conditions.　However,　the　design　of　sub　model　for　MNN　is　a　challenge.　We　propose　a　method　based　on　MNN　and　adaptive　ensemble　stochastic　configuration　network　(m-AESCN)　in　this　work.　First,　fuzzy　C-means　algorithm　decomposes　the　task　into　several　sub　datasets　with　similar　characteristics,　and　an　evaluation　function　is　used　to　guarantee　the　optimal　decomposition　result.　Second,　for　each　sub　dataset,　an　adaptive　ensemble　stochastic　configuration　network　as　the　sub　model　of　modular　neural　network.　The　optimal　output　for　sub　model　can　be　obtained　by　an　adaptive　weighting　method　based　on　variance.　Third,　in　testing　phase,　a　sub　model　activation　method　based　on　cluster　centroid　is　proposed　to　select　a　suitable　sub　model.　Besides,　the　m-AESCN　method　is　validated　by　the　real　data　of　an　MSWI　plant　and　shows　considerable　performance　in　two　different　datasets.　And　the　proposed　method　is　compared　with　several　modeling　methods,　such　as　stochastic　configuration　network,　support　vector　regression,　random　forest　and　so　on.　The　experimental　results　under　two　datasets　prove　that　the　maximum　improvements　in　accuracy　of　m-AESCN　are　39.74%　and　31.67%,　respectively.　©　2023　Elsevier　Ltd