With　the　industrialization　of　modern　society,　the　pollution　of　water　resources　becomes　more　and　more　serious.　Although　purifying　urban　sewage　through　the　wastewater　treatment　plants　eases　the　burden　of　fragile　ecosystems,　the　nonlinearities　and　uncertainties　of　biochemical　reactions　are　difficult　to　address.　In　this　article,　a　dynamic　prioritized　policy　gradient　adaptive　dynamic　programming　(ADP)　method　is　developed　to　solve　the　optimal　control　problem　of　nonaffine　nonlinear　discrete-time　systems,　along　with　convergence　analysis　of　the　algorithm.　To　the　best　of　our　knowledge,　it　is　indispensable　to　conduct　system　modeling　during　the　previous　ADP　research　on　wastewater　treatment　process　control.　By　introducing　the　dynamic　prioritized　replay　buffer　and　neural　networks,　the　proposed　ADP　controller　can　track　the　setpoints　of　the　wastewater　treatment　plant　and　alleviate　the　effects　of　disturbance　without　system　modeling.　The　test　results　verify　that　the　devised　control　method　outperforms　the　proportional-integral-derivative　strategy　with　less　oscillation　when　unknown　interference　occurred.