Due　to　sensor　malfunctions　and　communication　faults,　multiple　missing　patterns　frequently　happen　in　wastewater　treatment　process　(WWTP).　Nevertheless,　the　existing　missing　data　imputation　works　cannot　stand　multiple　missing　patterns　because　they　have　not　sufficiently　utilized　of　data　information.　In　this　article,　a　double-cycle　weighted　imputation　(DCWI)　method　is　proposed　to　deal　with　multiple　missing　patterns　by　maximizing　the　utilization　of　the　available　information　in　variables　and　instances.　The　proposed　DCWI　is　comprised　of　two　components:　a　double-cycle-based　imputation　sorting　and　a　weighted　K　nearest　neighbor-based　imputation　estimator.　First,　the　double-cycle　mechanism,　associated　with　missing　variable　sorting　and　missing　instance　sorting,　is　applied　to　direct　the　missing　values　imputation.　Second,　the　weighted　K　nearest　neighbor-based　imputation　estimator　is　used　to　acquire　the　global　similar　instances　and　capture　the　volatility　in　the　local　region.　The　estimator　preserves　the　original　data　characteristics　as　much　as　possible　and　enhances　the　imputation　accuracy.　Finally,　experimental　results　on　simulated　and　real　WWTP　datasets　with　non-stationarity　and　nonlinearity　demonstrate　that　the　proposed　DCWI　produces　more　accurate　imputation　results　than　comparison　methods　under　different　missing　patterns　and　missing　ratios.