Dissolved　oxygen　(DO)　concentration　is　a　key　variable　in　the　operation　of　wastewater　treatment　processes　(WWTPs).　In　this　paper,　an　adaptive　fuzzy　neural　network-based　model　predictive　control　(AFNN-MPC)　is　proposed　for　the　control　problem　of　DO　concentration.　The　main　contributions　of　AFNN-MPC　are　threefolds:　First,　an　AFNN,　based　on　a　novel　learning　method　with　adaptive　learning　rate,　is　designed　to　model　the　unknown　nonlinearities　of　WWTPs　with　high　predicting　performance.　Second,　a　gradient　method　is　used　to　solve　the　optimal　control　problem　of　AFNN-MPC　to　reduce　the　computational　cost.　Third,　the　convergence　of　AFNN,　as　well　as　the　stability　analysis　of　AFNN-MPC,　has　been　given　in　detail.　Finally,　the　proposed　AFNN-MPC　is　applied　to　the　benchmark　simulation　model　No.　2.　The　promising　results　indicate　that　the　proposed　AFNN-MPC　is　a　suitable　solution　to　control　DO　concentration.　Moreover,　the　comprehensive　experiments　clearly　show　the　superiority　and　efficacy　of　the　proposed　AFNN-MPC.