This　study　investigates　an　adaptive　fuzzy　neural　network　control　system　for　the　multiobjective　operation　of　wastewater　treatment　process　(WWTP)　with　standard　effluent　quality　(EQ)　as　well　as　low　energy　consumption　(EC).　The　control　system　consists　of　an　optimization　module　with　the　adaptive　multiobjective　differential　evolution　(AMODE)　algorithm　and　a　control　module　with　the　adaptive　fuzzy　neural　network　(AFNN).　First,　an　AMODE　algorithm,　using　the　adaptive　adjustment　strategies　for　selecting　the　suitable　scaling　factor　and　crossover　rate,　is　developed　to　optimize　all　objectives　simultaneously.　Then,　the　optimal　set-points　of　the　dissolved　oxygen　concentration　in　the　fifth　tank　(S-O5)　and　the　nitrogen　nitrate　concentration　in　the　second　anoxic　tank　(S-NO2)　of　WWTP　can　be　obtained　by　the　AMODE　algorithm.　Second,　an　AFNN　controller,　based　on　an　adaptive　second　order　algorithm,　is　employed　to　trace　the　set-points　of　S-O5　and　S-NO2　for　achieving　the　process　performance.　Finally,　the　proposed　control　system　is　applied　on　the　Benchmark　Simulation　Model　1　(BSM1).　The　performance　comparison　with　other　algorithms　indicates　that　the　proposed　control　system　yields　better　effluent　qualities　and　lower　average　operation　consumption.　(C)　2017　Elsevier　B.V.　All　rights　reserved.