Aromatic　hydrocarbon　yield　is　considered　as　one　of　the　important　product　quality　indicator　in　catalytic　reforming　production　process.　Aiming　at　the　difficulty　of　the　aromatic　hydrocarbon　yield　on-line　measurement,　a　soft　sensor　modeling　method　of　aromatic　hydrocarbon　yield　is　proposed　based　on　mutual　information-improved　gravitational　search　algorithm　(MI-IGSA)　optimized　extreme　learning　machine　(ELM).　Firstly,　the　MI　method　is　used　to　extract　the　most　relevant　process　feature　quantities　and　perform　dimension　reduction　processing,　and　the　auxiliary　variables　of　the　soft　sensor　model　are　determined.　Secondly,　through　introducing　the　successive　quadratic　programming　(SQP)　method　and　chaos　mutation　strategy,　the　IGSA　with　good　global　optimization　performance　is　constructed.　The　IGSA　algorithm　is　then　applied　to　optimize　the　hidden　layer　threshold　parameters　and　input　weight　parameters　of　ELM,　and　the　optimization　target　considers　the　minimization　of　both　the　root　mean　squared　error　(RMSE)　of　the　model　output　and　the　number　of　conditions　of　the　hidden　layer　output　matrix.　Finally,　the　aromatic　hydrocarbon　yield　soft　sensor　model　is　established　based　on　the　IGSA　optimized　ELM　method.　The　proposed　model　was　applied　in　the　prediction　study　of　the　aromatic　hydrocarbon　yield　of　the　catalytic　reforming　equipment　in　a　certain　refinery　plant,　the　simulation　results　show　that　the　proposed　soft　sensor　model　possesses　promising　prediction　accuracy　and　reliability.　©　2019,　Science　Press.　All　right　reserved.