Nowadays　flue　gas　desulphurization　(FGD)　technologies　have　been　extensively　applied　in　the　coal-fired　electricity-generating　plants.　As　emission　standards　for　sulfur　dioxide　(SO2)　have　become　more　stringent　in　recent　years,　there　is　a　real　need　to　develop　more　advanced　modeling　techniques　such　that　the　FGD　process　can　be　identified　accurately,　which　in　turn　provides　a　reliable　foundation　for　FGD　process　control　and　optimization.　However,　FGD　process　has　characteristics　of　large　time　delay,　strongly　nonlinear　dynamics,　making　traditional　identification　models　become　ineffectual.　To　address　this　issue,　this　paper　proposes　a　hybrid　model　consisting　of　information-theoretic　technique　and　convolution　neural　network　(CNN).　To　verify　the　proposed　identification　approach,　a　real　FGD　system　of　a　600MW　coal-fired　power　station　is　selected　as　case　analysis.　Experimental　results　indicate　that　our　model　achieves　satisfactory　identification　result　and　performs　better　than　other　popular　FGD　models　appeared　in　previous　studies,　demonstrating　the　effectiveness　and　superiority　of　the　proposed　MI-CNN　model.　©　2024　IEEE.