In　wastewater　treatment　processes　(WWTPs),　the　effluent　ammonia　nitrogen　(NH4-N)　concentration　is　a　significant　index　to　measure　effluent　quality.　Recently,　the　soft　computing　methods　have　been　widely　used　to　measure　effluent　NH4-N　concentration.　However,　the　performance　of　soft　computing　method　is　closely　related　with　its　input　variables,　which　is　difficult　to　choose.　As　an　alternative,　the　time　series　prediction　method　PSR-PRWNN　is　proposed,　which　combines　phase　space　reconstruction　(PSR)　technique　and　pipelined　recurrent　wavelet　neural　network　(PRWNN).　Different　from　soft　computing　methods,　the　time　series　prediction　method　is　a　method　which　predicts　the　effluent　NH4-N　concentration　by　using　its　history　data　rather　than　other　variables　data.　Firstly,　the　chaotic　characteristics　of　effluent　NH4-N　time　series　is　proved　by　using　the　correlation　dimension　method.　Based　on　chaotic　characteristics,　the　phase　space　of　effluent　NH4-N　concentration　is　reconstructed　by　PSR　technique.　Then,　the　relationship　model　between　inputs　and　output　of　the　reconstructed　phase　space　is　established　by　PRWNN.　Thirdly,　the　parameters　of　PRWNN　are　trained　by　an　online　gradient　algorithm　with　adaptive　learning　rates.　Finally,　the　experimental　results　indicate　that　the　PSR-PRWNN　can　obtain　better　training　results　and　prediction　accuracy　than　other　algorithms.　(c)　2022　Elsevier　B.V.　All　rights　reserved.