In　China,　coal　accounts　for　over　a　half　of　the　total　energy　consumption　and　it　is　primarily　used　in　coal-fired　power　stations.　Undoubtedly,　the　change　of　a　dominant　role　that　coal　played　in　electricity　generation　appears　to　be　impossible　in　the　near　future.　The　operation　of　coal-fired　power　stations　involves　combustion　of　coal,　which　would　generate　a　vast　amount　of　sulphur　dioxide　(SO2),　the　resulting　SO2　in　turn　pose　numbers　of　environmental　and　human-health　hazards.　In　view　of　the　above　considerations,　optimization　and　control　techiniques　should　be　incorporated　in　flue　gas　desulphurization　(FGD)　system,　so　that　further　improvement　in　overall　performance　of　FGD　can　be　made.　In　this　paper,　based　upon　historical　operational　data　from　FGD　system　at　a　1000MW　unit　coal-fired　power　station,　we　formulate　the　set-point　determination　in　FGD　process　into　a　constrained　many-objective　optimization　problem.　First　of　all,　multiple　objective　particle　swarm　optimization　(MOPSO)　algorithm　along　with　extreme　learning　machine　(ELM)　is　utilized　to　yield　Pareto-optimal　front.　Then,　for　control　purpose,　setpoint　is　determined　from　the　obtained　Pareto　front.　Lastly,　with　the　use　of　linear　quadratic　integral　controller　(LQIR),　the　setpoint　regualation　task　is　fulfilled.　By　simulation　studies,　derived　respresentative　point　is　theoretically　justified　and　FGD　process　can　be　regulated　to　it　successfully.