There　is　a　global　consensus　on　reducing　greenhouse　gas　(GHG)　emissions　to　slow　down　global　warming.　GHG　emissions　from　the　wastewater　treatment　process　(WWTP)　have　also　received　extensive　attention.　Therefore,　how　to　reduce　GHG　emissions　without　increasing　the　operating　cost　and　ensuring　water　quality　standards　for　WWTP　is　a　challenging　problem.　To　cope　with　the　issue,　a　bi-level　optimal　control　strategy　based　on　similarity　clustering　(BLOC-SC)　for　WWTP　with　GHG　emissions　is　proposed.　First,　a　bi-level　optimization　(BLO)　model　for　WWTP　is　established　based　on　the　key　process　variables.　Then,　the　model　can　describe　the　relationship　among　water　quality,　operating　costs,　and　GHG　emissions.　Second,　a　multi-task　optimization　algorithm,　based　on　similarity　clustering,　is　designed　to　solve　the　BLO　problem.　Then,　the　algorithm　can　save　computing　resources　and　quickly　solve　the　BLO　problem　to　obtain　the　optimal　set　values　for　WWTP.　Third,　a　fuzzy　neural　network　controller　is　proposed　to　achieve　accurate　tracking　of　optimal　set　values.　Finally,　the　simulation　results,　based　on　benchmark　simulation　model　2-GHG,　indicate　that　BLOC-SC　can　achieve　energy　saving　and　emission　reduction　in　WWTP.　©　2024　IEEE.