The　dissolved　oxygen　(DO)　concentration　in　activated　sludge　wastewater　treatment　processes　(WWTPs)　is　difficult　to　control　because　of　the　complex　nonlinear　behavior　involved.　In　this　paper,　a　self-organizing　radial　basis　function　(RBF)　neural　network　model　predictive　control　(SORBF-MPC)　method　is　proposed　for　controlling　the　DO　concentration　in　a　WWTP.　The　proposed　SORBF　can　vary　its　structure　dynamically　to　maintain　prediction　accuracy.　The　hidden　nodes　in　the　RBF　neural　network　can　be　added　or　removed　on-line　based　on　node　activity　and　mutual　information　(MI)　to　achieve　the　appropriate　network　complexity　and　the　necessary　dynamism.　Moreover,　the　convergence　of　the　SORBF　is　analyzed　in　both　the　dynamic　process　phase　and　the　phase　following　the　modification　of　the　structure.　Finally,　the　SORBF-MPC　is　applied　to　the　Benchmark　Simulation　Model　1　(BSM1)　WWTP　to　maintain　the　DO　concentration.　The　results　show　that　SORBF-MPC　effectively　provides　process　control.　The　performance　comparison　also　indicates　that　the　proposed　model＇s　predictive　control　strategy　yields　the　most　accurate　for　DO　concentration,　better　effluent　qualities,　and　lower　average　aeration　energy　(AE)　consumption.　(c)　2012　Elsevier　Ltd.　All　rights　reserved.