Wastewater　treatment　must　satisfy　discharge　requirements　under　specified　constraints　and　have　minimal　operating　costs　(OC).　The　operating　results　of　wastewater　treatment　processes　(WWTPs)　have　significantly　focused　on　both　the　energy　consumption　(EC)　and　effluent　quality　(EQ).　To　reflect　the　relationship　between　the　EC　and　EQ　of　WWTPs　directly,　an　extended　Elman　neural　networkbased　energy　consumption　model　(EENN-ECM)　was　studied　for　WWTP　control　in　this　paper.　The　proposed　EENN-ECM　was　capable　of　predicting　EC　values　in　the　treatment　process.　Moreover,　the　self-adaptive　characteristic　of　the　EENN　ensured　the　modeling　accuracy.　A　performance　demonstration　was　carried　out　through　a　comparison　of　the　EC　between　the　benchmark　simulation　model　No.　1　(BSM1)　and　the　EENN-ECM.　The　experimental　results　demonstrate　that　this　EENN-ECM　is　more　effective　to　model　the　EC　of　WWTPs.