There　has　recently　been　increasing　attention　paid　to　sulphur　dioxide　(SO2)　pollution　owing　to　its　hazardous　effect　on　both　human　health　and　atmospheric　environment.　To　handle　this　problem,　the　wet　flue　gas　desulphurization　(FGD)　system　has　found　wide　applications　in　SO2　emitting　industries.　Accurate　prediction　of　SO2　emissions　in　treated　flue　gas　serves　the　purpose　of　providing　timely　operating　guidance　for　the　FGD　system.　However,　the　wet　FGD　process　is　characterized　by　highly　nonlinear　dynamics　and　non-stationarity,　which　poses　significant　difficulties　and　limitations　for　traditional　modeling　methods.　To　address　above　issues,　in　this　article,　an　integrated　model　is　proposed　to　perform　SO2　emission　forecasting　for　an　FGD　process.　Our　integrated　model　comprises　a　multiplicity　of　techniques,　including　complete　ensemble　empirical　mode　decomposition　(EMD)　with　adaptive　noise　CEEMDAN　stacking　ensemble　learning　(SEL)　and　permutation-based　entropy　(PEN).　The　CEEMDAN　serves　as　decomposing　SO2　emission　signal,　then　the　complexity　of　each　decomposed　sub-series　is　analyzed　by　PEN　and　ones　with　similar　scores　are　combined,　finally　a　stacking-based　ensemble　learning　model　which　incorporates　different　types　of　member　models　are　developed　for　modeling　purposes.　The　proposed　method　was　validated　and　evaluated　by　measurements　of　a　real　FGD　system　in　a　600MW　coal-fired　unit,　and　experimental　results　illustrate　the　superiority　of　our　method.　©　2024,　Kauno　Technologijos　Universitetas.　All　rights　reserved.