The　accurate　and　timely　prediction　of　nitrogen　oxides　(NOx)　emissions　ensures　eco-friendly　and　efficient　operations　for　municipal　solid　waste　incineration　(MSWI)　plants.　Due　to　the　high　nonlinearity　and　uncertainty　in　MSWI　processes,　constructing　an　efficient　prediction　model　remains　challenging.　This　work　proposes　a　comprehensively　improved　interval　type-2　fuzzy　neural　network　(CI-IT2FNN)　for　NOx　emissions　prediction.　First,　the　neighborhood　rough　set　(NRS)　is　introduced　to　determine　the　structure　of　this　fuzzy　neural　network　automatically,　including　the　number　of　fuzzy　rules　and　their　corresponding　consequent　parameters.　Second,　an　adaptive　shape　factor　is　added　to　the　fuzzy　membership　function　to　better　cope　with　the　uncertainty,　which　can　help　to　improve　the　generalization　ability　of　network.　Furthermore,　to　reduce　the　computational　complexity,　the　Begian-Melek-Mendel　(BMM)　method　is　utilized　as　the　defuzzification　method　in　this　study.　Then,　by　integrating　the　linear　least　square　estimation　(LSE)　and　gradient　decent　(GD),　a　hierarchical　learning　algorithm　is　applied　to　adjust　the　network　parameters　to　further　enhance　the　learning　efficiency　and　accuracy.　Finally,　after　being　evaluated　by　a　benchmark　simulation,　the　proposed　CI-IT2FNN　demonstrates　its　effectiveness　and　superiority　on　NOx　emissions　prediction.　IEEE