Wastewater　treatment　plays　a　crucial　role　in　urban　society,　requiring　efficient　control　strategies　to　optimize　its　performance.　In　this　paper,　we　propose　an　enhanced　offline　reinforcement　learning　(RL)　approach　for　wastewater　treatment.　Our　algorithm　improves　the　learning　process.　It　uses　a　transition　filter　to　sort　out　low-　performance　transitions　and　employs　prioritized　approximation　loss　to　achieve　prioritized　experience　replay　with　uniformly　sampled　loss.　Additionally,　the　variational　autoencoder　is　introduced　to　address　the　problem　of　distribution　shift　in　offline　RL.　The　proposed　approach　is　evaluated　on　a　nonlinear　system　and　wastewater　treatment　simulation　platform,　demonstrating　its　effectiveness　in　achieving　optimal　control.　The　contributions　of　this　paper　include　the　development　of　an　improved　offline　RL　algorithm　for　wastewater　treatment　and　the　integration　of　transition　filtering　and　prioritized　approximation　loss.　Evaluation　results　demonstrate　that　the　proposed　algorithm　achieves　lower　tracking　error　and　cost.