Modeling　of　energy　consumption　(EC)　and　effluent　quality　(EQ)　are　very　essential　problems　that　need　to　be　solved　for　the　multiobjective　optimal　control　in　the　wastewater　treatment　process　(WWTP).　To　address　this　issue,　a　density　peaks-based　adaptive　fuzzy　neural　network　(DP-AFNN)　is　proposed　in　this　study.　To　obtain　suitable　fuzzy　rules,　a　DP-based　clustering　method　is　applied　to　fit　the　cluster　centers　to　process　nonlinearity.　The　parameters　of　the　extracted　fuzzy　rules　are　fine-tuned　based　on　the　improved　Levenberg-Marquardt　algorithm　during　the　training　process.　Furthermore,　the　analysis　of　convergence　is　performed　to　guarantee　the　successful　application　of　the　DP-AFNN.　Finally,　the　proposed　DP-AFNN　is　utilized　to　develop　the　models　of　EC　and　EQ　in　the　WWTP.　The　experimental　results　show　that　the　proposed　DP-AFNN　can　achieve　fast　convergence　speed　and　high　prediction　accuracy　in　comparison　with　some　existing　methods.