Continuous　catalytic　reforming　(CCR)　is　an　important　process　in　hydrocarbon　processing　to　convert　low-octane　gasoline　blending　components　to　high-octane　components　for　use　in　high-performance　gasoline　fuels　or　as　source　of　aromatics.　Carbon　deposition　rate　is　a　critical　performance　factor　of　reforming　catalyst　and　carbon　content　of　spent　catalyst　would　directly　influence　the　subsequent　catalyst　regeneration;　thus　it　is　imperative　to　monitor　the　carbon　content　of　spent　catalyst　in　real　time.　In　this　paper　a　soft　sensor　is　proposed　using　least　squares　support　vector　machine　(LSSVM)　with　genetic　algorithm　(GA)　to　solve　the　industrial　problem　for　online　estimating　the　carbon　content　of　spent　catalyst　in　an　existing　CCR　plant,　wherein　the　GA　is　used　to　select　the　free　parameters　of　the　LSSVM　model.　The　LSSVM　with　traditional　grid　algorithm　and　artificial　neural　network　(ANN)　are　also　applied　to　model　two　soft　sensors　using　the　same　data　sets　for　comparison.　The　simulation　results　show　that　GA　shows　outstanding　performance　than　traditional　grid　algorithm　for　selecting　free　parameters　of　LSSVM;　the　proposed　LSSVMGA　soft　sensor　can　achieve　smallest　errors　and　shortest　computing　comparing　with　LSSVM　and　ANN.　Then　the　proposed　soft　sensor　is　applied　to　the　existing　CCR　plant;　the　predictive　values　are　satisfactory.