Purpose:　The　purpose　of　this　paper　is　to　present　an　on-line　modeling　and　controlling　scheme　based　on　the　dynamic　recurrent　neural　network　for　wastewater　treatment　system.　Design/methodology/approach:　A　control　strategy　based　on　rule　adaptive　recurrent　neural　network　(RARFNN)　is　proposed　in　this　paper　to　control　the　dissolved　oxygen　(DO)　concentration　and　nitrate　nitrogen　(SNo)　concentration.　The　structure　of　the　RARFNN　is　self-organized　by　a　rule　adaptive　algorithm,　and　the　rule　adaptive　algorithm　considers　the　overall　information　processing　ability　of　neural　network.　Furthermore,　a　stability　analysis　method　is　given　to　prove　the　convergence　of　the　proposed　RARFNN.　Findings:　By　application　in　the　control　problem　of　wastewater　treatment　process　(WWTP),　results　show　that　the　proposed　control　method　achieves　better　performance　compared　to　other　methods.　Originality/value:　The　proposed　on-line　modeling　and　controlling　method　uses　the　RARFNN　to　model　and　control　the　dynamic　WWTP.　The　RARFNN　can　adjust　its　structure　and　parameters　according　to　the　changes　of　biochemical　reactions　and　pollutant　concentrations.　And,　the　rule　adaptive　mechanism　considers　the　overall　information　processing　ability　judgment　of　the　neural　network,　which　can　ensure　that　the　neural　network　contains　the　information　of　the　biochemical　reactions.　©　2017,　©　Emerald　Publishing　Limited.