Wastewater　treatment　process　(WWTP)　has　long　been　a　challenging　industrial　issue　due　to　its　built-in　uncertainties　and　discontinuous　measurement　of　system　states.　To　solve　this　problem,　in　this　paper,　a　data-based　predictive　control　(DPC)　strategy.　based　on　the　available　sensing　measurements,　is　proposed　to　control　the　dissolved　oxygen　(DO)　concentration　in　WWTP.　First,　a　self-organizing　fuzzy　neural　network,　which　can　adjust　both　the　structure　and　parameters　simultaneously,　is　developed　to　identify　the　real-time　states　of　WWTP.　Second,　an　improved　nonlinear　predictive　control　method　is　designed　to　reduce　the　online　computation　complexity　by　transforming　the　constrained　conditions　into　an　unconstrained　nonlinear　programming　problem.　Then,　an　adaptive　second　order　Levenberg-Marquardt　algorithm　is　developed　to　derive　the　control　law　of　DPC.　Third,　the　theoretical　analysis　on　the　stability　is　also　given　to　confirm　the　prerequisite　of　any　successful　application　of　DPC,　Finally,　the　proposed　DPC　strategy　is　applied　to　the　Benchmark　Simulation　Model　No.　1.　Experimental　results　demonstrate　that　the　control　performance　of　the　proposed　DPC　is　better　than　some　existing　methods.