Because　of　their　complex　behavior,　wastewater　treatment　processes　(WWTPs)　are　very　difficult　to　control.　In　this　paper,　the　design　and　implementation　of　a　nonlinear　model-predictive　control　(NMPC)　system　are　discussed.　The　proposed　NMPC　comprises　a　self-organizing　radial　basis　function　neural　network　(SORBFNN)　identifier　and　a　multiobjective　optimization　method.　The　SORBFNN　with　concurrent　structure　and　parameter　learning　is　developed　as　a　model　identifier　for　approximating　the　online　states　of　dynamic　systems.　Then,　the　solution　of　the　multiobjective　optimization　is　obtained　by　a　gradient　method　which　can　shorten　the　solution　time　of　optimal　control　problems.　Moreover,　the　conditions　for　the　stability　analysis　of　NMPC　are　presented.　Experiments　reveal　that　the　proposed　control　technique　gives　satisfactory　tracking　and　disturbance　rejection　performance　for　WWTPs.　Experimental　results　on　a　real　WWTP　show　the　efficacy　of　the　proposed　NMPC　for　industrial　processes　in　many　applications.　Index