Missing　values　in　wastewater　treatment　process　(WWTP)　data　hinder　the　monitoring　and　prediction　of　operational　status.　Therefore,　various　online　imputation　methods　have　been　proposed　to　recover　missing　values　from　streaming　data　collected　in　WWTP　in　real　time.　However,　existing　methods　tend　to　ignore　previous　learned　knowledge.　In　this　paper,　an　online　aware　synapse　weighted　autoencoder　imputation　method　(OASI)　is　proposed　to　impute　random　missing　values.　First,　an　online　stacked　autoencoder　(OSAE)　framework　is　constructed　to　capture　the　nonlinear　structure　of　the　recently　collected　data.　The　framework　decreases　the　computational　and　storage　consumption　of　the　model　training.　Second,　an　aware　synapses　weighted　parameter　regularization　strategy　is　presented　to　guide　the　update　of　model　parameters　and　alleviate　the　forgetting　of　historical　information　in　an　online　continual　setup.　In　this　way,　the　learned　features　offer　a　more　comprehensive　representation　of　the　overall　information　and　help　enhance　imputation　performance.　Third,　two　real　WWTP　datasets　with　strong　non-stationarity,　high-noise　level　and　high-dimensionality　are　used　to　validate　the　performance　of　the　proposed　OASI.　Experimental　results　show　that　the　proposed　OASI　achieves　superior　performances　over　the　existing　methods　even　in　the　presence　of　random　missing　values　with　different　missing　ratios,　and　only　costs　a　short　running　time.　IEEE