Flue　gas　emission　is　an　inevitable　procedure　in　the　course　of　electricity　generation,　which　would　pose　a　severe　threat　to　human　health,　and　has　an　adverse　effect　on　our　environment.　Due　to　the　fact　that　the　environment　in　practical　flue　gas　desulfurization　system　fluctuates　frequently,　system　parameters　tend　to　vary　constantly　during　the　operating　process,　thus　control　performance　with　traditional　strategies　tends　to　be　suboptimal　in　most　cases.　To　address　this　problem,　some　insight　into　operating　conditions　must　be　gained　prior　to　taking　proper　control　strategy.　Therefore,　in　this　article,　based　on　actual　measurements　in　1000　MW　Unit　Wet　Limestone　FGD　System　for　a　coal-fired　power　plant,　a　kind　of　intelligent　operating　condition　partition　method　is　combined　with　the　multi-model　adaptive　control　strategy.　Specifically,　analysis　and　partition　of　operating　condition　is　carried　out　in　the　first　place,　then　adaptive　multi-model　control　is　implemented　with　the　combination　of　parallel　dynamic　neural　network　and　partition　results.　Additionally,　the　applicability　of　proposed　control　mode　is　investigated　through　different　simulation　examples.　At　the　same　time,　to　further　enhance　the　flexibility　of　multi-model　control　structure,　some　possible　improvements　on　it　is　also　discussed.