To　achieve　the　excellent　operational　performance　of　the　wastewater　treatment　process,　optimal　control　has　been　considered　a　reliable　method.　However,　there　is　a　time-delay　response　of　the　operation　performances　to　the　process　variables,　leading　to　uncertainties　of　operational　optimal　objectives.　It　is　difficult　to　obtain　the　optimal　set-points　due　to　the　uncertain　operational　optimal　objectives.　Therefore,　a　kernel-density-estimation-based　robust　optimal　control　(KDE-ROC)　method　is　proposed.　First,　a　data-driven　prediction　strategy　is　developed　to　construct　the　uncertain　operational　optimal　objectives.　Based　on　the　time-delay　intervals,　the　uncertainties　between　process　variables　and　operational　optimal　objectives　are　expressed.　Second,　a　kernel-density-estimation-based　robust　optimization　algorithm　is　designed　to　solve　the　uncertain　operational　optimal　objectives.　Then,　the　optimal　set-points　of　process　variables　are　obtained　depending　on　the　robustness　index　to　reduce　the　influence　of　uncertainties.　Third,　an　adaptive　neural　network　controller　is　developed　to　track　the　optimal　set-points　of　process　variables.　Finally,　the　proposed　KDE-ROC　is　applied　in　benchmark　simulation　model　No.1.　In　the　experimental　results,　the　optimal　control　performance　of　KDE-ROC　is　compared　with　some　effective　optimal　control　strategies　to　demonstrate　its　effectiveness.