Wastewater　treatment　process　(WWTP),　consisting　of　a　class　of　physical,　chemical,　and　biological　phenomena,　is　an　important　means　to　reduce　environmental　pollution　and　improve　recycling　efficiency　of　water　resources.　Considering　characteristics　of　the　complexities,　uncertainties,　nonlinearities,　and　multitime　delays　in　WWTPs,　an　adaptive　neural　controller　is　presented　to　achieve　the　satisfying　control　performance　for　WWTPs.　With　the　advantages　of　radial　basis　function　neural　networks　(RBF　NNs),　the　unknown　dynamics　in　WWTPs　are　identified.　Based　on　the　mechanistic　analysis,　the　time-varying　delayed　models　of　the　denitrification　and　aeration　processes　are　established.　Based　on　the　established　delayed　models,　the　Lyapunov-Krasovskii　functional　(LKF)　is　used　to　compensate　for　the　time-varying　delays　caused　by　the　push-flow　and　recycle　flow　phenomenon.　The　barrier　Lyapunov　function　(BLF)　is　used　to　ensure　that　the　dissolved　oxygen　(DO)　and　nitrate　concentrations　are　always　kept　within　the　specified　ranges　though　the　time-varying　delays　and　disturbances　exist.　Using　Lyapunov　theorem,　the　stability　of　the　closed-loop　system　is　proven.　Finally,　the　proposed　control　method　is　carried　out　on　the　benchmark　simulation　model　1　(BSM1)　to　verify　the　effectiveness　and　practicability.