Sulphur　dioxide,　as　one　of　the　most　common　air　pollutant　gases,　brings　considerable　numbers　of　hazards　on　human　health　and　environment.　For　the　purpose　of　reducing　the　detrimental　effect　it　brings,　it　is　of　urgent　necessity　to　control　emissions　of　flue　gas　in　power　plants,　since　a　substantial　proportion　of　sulphur　dioxide　in　the　atmosphere　stems　from　flue　gas　generated　in　the　whole　process　of　electricity　generation.　However,　the　complexity　and　nondeterminism　of　the　environment　increase　the　occurrences　of　anomalies　in　practical　flue　gas　desulphurization　system.　Anomalies　in　industrial　desulphurization　system　would　induce　severe　consequences　and　pose　challenges　for　high-performance　control　with　classical　control　strategies.　In　this　article,　based　on　process　data　sampled　from　1000　MW　unit　flue　gas　desulphurization　system　in　a　coal-fired　power　plant,　a　multimodel　control　strategy　with　multilayer　parallel　dynamic　neural　network　(MPDNN)　is　utilized　to　address　the　control　problem　in　the　context　of　different　anomalies.　In　addition,　simulation　results　indicate　the　applicability　and　effectiveness　of　the　proposed　control　method　by　comparing　with　different　cases.