The　timely　and　accurate　measurement　of　nitrogen　oxides　(NOx)　emissions　is　important　for　efficient　pollution　controlling　of　municipal　solid　waste　incineration　plants.　With　the　aim　to　design　an　efficient　and　effective　prediction　model　for　NOx　concentrations,　a　brain-inspired　modular　neural　network　(BIMNN)　is　developed　in　this　article.　First,　a　biologically　inspired　modularization　technique　is　proposed　in　which　the　topological　modularity　gives　rise　to　functional　modularity.　Consequently,　different　modules　correspond　to　different　tasks,　improving　the　network　efficiency　by　performing　task　decomposition.　Subsequently,　an　adaptive　task-oriented　radial　basis　function　(ATO-RBF)　neural　network　is　applied　to　construct　each　module　based　on　assigned　subtasks.　The　ATO-RBF　neural　network　is　comprised　of　a　structure　self-organizing　mechanism　and　an　adaptive　second-order　learning　algorithm,　providing　basis　for　learning　performance　and　generalization　ability　of　BIMNN.　Finally,　during　the　testing　or　application　stages,　a　competitive　strategy　is　utilized　to　select　the　modules　which　can　be　adapted　to　the　current　task,　aiming　to　enhance　the　efficiency　of　BIMNN.　The　proposed　prediction　methodology　is　verified　using　industrial　data,　and　the　experimental　results　demonstrate　the　advantages　of　the　BIMNN-based　prediction　model　on　speed　and　accuracy.