Data-driven　methods　are　widely　used　in　wastewater　treatment　processes　(WWTPs).　However,　disturbances　are　not　fully　considered　during　the　control　process,　which　may　affect　the　stable　operation　of　WWTPs.　To　address　this　issue,　a　data-driven　robust　model　predictive　control　(DRMPC)　method　is　proposed　for　WWTPs　with　disturbances.　First,　an　interval　type-2　fuzzy　neural　network　(IT2FNN),　which　has　high　robust　performance　to　disturbances,　is　used　to　identify　dynamic　behavior　of　WWTPs.　Second,　based　on　IT2FNN,　a　robust　stabilizing　controller　is　developed　to　weaken　the　influence　of　disturbances　on　control　performance　and　stability.　Third,　the　proposed　controller　ensures　constraint　satisfaction　and　input-to-state　stability　(ISS).　Finally,　the　feasibility　of　DRMPC　strategy　is　verified　on　the　benchmark　simulation　model　No.　1　(BSM1).　The　experimental　studies　indicate　that　the　proposed　DRMPC　is　capable　of　running　stably　and　tracking　accurately.　(C)　2022　Published　by　Elsevier　Ltd.