A　nonlinear　multiobjective　model-predictive　control　(NMMPC)　scheme,　consisting　of　self-organizing　radial　basis　function　(SORBF)　neural　network　prediction　and　multiobjective　gradient　optimization,　is　proposed　for　wastewater　treatment　process　(WWTP)　in　this　paper.　The　proposed　NMMPC　comprises　a　SORBF　neural　network　identifier　and　a　multiple　objectives　controller　via　the　multi-gradient　method　(MGM).　The　SORBF　neural　network　with　concurrent　structure　and　parameter　learning　is　developed　as　a　model　identifier　for　approximating　on-line　the　states　of　WWTP.　Then,　this　NMMPC　optimizes　the　multiple　objectives　under　different　operating　functions,　where　all　the　objectives　are　minimized　simultaneously.　The　solution　of　optimal　control　is　based　on　the　MGM　which　can　shorten　the　solution　time.　Moreover,　the　stability　and　control　performance　of　the　closed-loop　control　system　are　well　studied.　Numerical　simulations　reveal　that　the　proposed　control　strategy　gives　satisfactory　tracking　and　disturbance　rejection　performance　for　WWTP.　Experimental　results　show　the　efficacy　of　the　proposed　method.　Crown　Copyright　(C)　2013　Published　by　Elsevier　Ltd.　All　rights　reserved.