In　order　to　solve　the　problem　of　difficult　mathematical　modeling　of　complex　industrial　processes,　this　thesis　proposes　a　data-driven　modeling　method　that　performs　dynamic　linearization　near　the　working　point.　Since　all　real　industrial　processes　have　a　certain　working　range,　they　can　be　regarded　as　linear　objects　containing　unmodeled　dynamics　within　this　range.　Therefore,　a　dynamic　modeling　algorithm　based　on　incremental　least　squares　is　designed　in　this　thesis.　This　algorithm　is　able　to　compute　a　linearized　model　of　the　dynamics　in　the　vicinity　of　the　operating　point　based　on　actual　production　data.　Considering　the　controller　implementation　problems　in　the　industrial　field,　the　model　is　computed　up　to　the　2nd　order,　i.e.,　the　higher-order　characteristics　are　also　regarded　as　part　of　the　unmodeled　dynamics.　Finally,　on　the　basis　of　the　dynamic　model,　Smith　predictor　is　used　to　simulate　the　control.　The　simulation　results　show　that　control　based　on　the　model　derived　from　this　algorithm　results　in　better　dynamic　and　steady　state　characteristics.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2024.