The　drastic　fluctuations　of　influent　pollutant　load　are　inevitable　in　wastewater　treatment　process,　which　makes　it　difficult　for　nitrification　to　regulate　dissolved　oxygen　concentrations　with　minimal　effort　to　ensure　the　effluent　quality.　To　solve　this　problem,　a　multispatial-scale　optimal　control　with　multisource　information　(MSI-MSSOC)　is　developed　in　this　article.　First,　a　multispatial-scale　optimization　model,　making　use　of　mechanism　knowledge　and　process　data,　is　designed　to　construct　reasonable　objectives　and　constraints.　Then,　the　performance　indexes　can　be　described　to　evaluate　the　comprehensive　adjustment　effect　of　dissolved　oxygen　concentrations　in　different　areas.　Second,　an　adaptive　knowledge　acquisition　strategy　is　employed　to　extract　the　interactivity　between　feasible　and　infeasible　solutions.　Then,　the　proposed　strategy　can　assist　in　the　optimal　control　to　search　for　the　optimal　solutions.　Third,　a　knowledge-aided　optimization　algorithm　is　introduced　to　update　the　feasible　region　to　calculate　the　optimal　solutions.　Then,　the　optimal　dissolved　oxygen　concentrations　in　different　areas　can　be　obtained　to　guarantee　the　effluent　quality　and　reduce　the　energy　consumption.　Finally,　the　proposed　MSI-MSSOC　is　applied　to　Benchmark　Simulation　Model　No.　1　to　verify　its　effectiveness.　The　experimental　results　demonstrate　that　MSI-MSSOC　can　achieve　the　desirable　operation　performance　of　nitrification.　©2005-2012　IEEE.