This　work　aims　to　establishing　a　multi-scale　and　multi-objective　optimization　strategy　for　the　catalytic　distillation　process.　Firstly,　the　catalyst　layer　efficiency　factor　correlation　that　embraces　the　influence　of　the　catalyst　layer　structure　was　obtained　and　then　coupled　with　the　process　simulation　to　establish　a　multi-scale　model　for　the　catalytic　distillation　process.　On　this　basis,　genetic　algorithm　was　employed　to　realize　the　multi-objective　optimization.　The　hydrolysis　process　of　methyl　acetate　in　the　reactive　dividing　wall　column　was　investigated.　The　equipment　parameters　and　operational　conditions　of　the　catalytic　distillation　column,　and　the　structure　parameters　of　the　catalyst　layer　are　optimized　simultaneously.　The　results　indicated　that　the　minima　of　the　total　annual　cost,　the　gas　emission　cost　and　the　exergy　loss　decrease　by　19.26%,　23.11%　and　67.27%,　respectively,　by　comparing　with　the　counterparts　obtained　from　the　single-factor　sensitivity　analysis.　Furthermore,　the　catalyst　cost　decreases　by　30.88%　per　year.　©　2022　Elsevier　Ltd