In　this　paper,　by　integrating　neural　network　approximators,　a　data-based　composite　control　technique　is　developed　with　critic　learning　implementation　and　wastewater　treatment　verification.　The　iterative　adaptive　critic　framework　is　established　involving　dual　heuristic　dynamic　programming　(DHP),　so　as　to　obtain　an　intelligent　optimal　controller.　Besides,　a　steady　control　input　is　computed　with　the　help　of　the　neural　identifier.　Then,　by　combining　the　DHP　controller　and　the　steady　control　input,　an　effective　composite　control　strategy　is　derived　and　applied　to　the　proposed　wastewater　treatment　platform.　Through　conducting　experiments,　it　is　observed　that　the　dissolved　oxygen　concentration　and　the　nitrate　level　can　be　maintained　at　setting　points　successfully,　which　results　in　an　intelligent　wastewater　treatment　system.