To　achieve　the　real-time　dynamic　prediction　of　effluent　biochemical　oxygen　demand　(BOD)　in　the　wastewater　treatment　process　(WWTP),　an　online　prediction　method　based　on　LSTM　neural　network　(OPLSTM)　is　proposed　in　this　paper.　The　online　prediction　is　realized　by　sliding　window　strategy　using　the　LSTM　model,　with　the　sliding　window　size　adaptively　adjusted　by　an　adaptive　method　based　on　model　performance　and　data　distribution,　and　BPTT　learning　algorithm　is　used　to　update　the　parameters　of　the　LSTM.　The　OPLSTM　is　first　tested　on　two　benchmark　datasets　and　then　applied　to　BOD　prediction　in　WWTP.　The　experimental　results　show　that　the　method　can　realize　online　prediction　and　obtain　satisfactory　prediction　accuracy,　and　the　adaptive　method　for　sliding　window　size　would　improve　the　generalization　performance　of　OPLSTM.　©　2023　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.