The　operating　performance　of　the　expander,　as　the　core　component　of　the　ORC　system,　directly　determines　the　comprehensive　performance　of　the　system.　Single　screw　expander　(SSE)　has　received　widespread　attention　due　to　its　advantages　such　as　large　single-stage　expansion　pressure　ratio,　insensitivity　to　liquid,　wide　working　range,　and　balanced　forces.　This　study　fully　considers　the　strong　coupling,　high　nonlinearity,　large　time　variability　and　uncertainty　between　SSE　performance　evaluation　indexes　and　operating　parameters.　On　this　basis,　a　scatter-bilinear　interpolation　hybrid　feature　selection　approach　is　proposed,　and　the　feature　selection　and　analysis　of　the　correlation　degree　among　SSE　performance　evaluation　indexes　are　conducted.　In　the　high-dimensional　space,　the　influence　of　operating　parameters　on　different　performance　evaluation　indexes　is　analyzed.　Then,　this　study　proposes　the　ensemble　learning-driven　non-dominated　sorting　genetic　algorithm-III　(NSGA-III)　approach.　An　ensemble　learning-driven　NSGA-III　model　for　SSE　performance　limit　optimization　is　constructed　on　the　basis　of　the　scatter-bilinear　interpolation　hybrid　feature　selection　method　and　the　ensemble　learningdriven　NSGA-III　approach.　In　the　hyperdimensional　space,　the　performance　limits　of　SSE　are　obtained.　When　the　comprehensive　performance　of　SSE　reaches　its　limit,　the　shaft　efficiency,　expansion　pressure　ratio,　and　power　output　are　53.71%,　7.76,　and　10.83　kW,　respectively.　This　research　provides　a　reliable　reference　for　analyzing,　designing,　and　optimizing　the　SSE　performance　limits,　and　it　also　provides　a　direct　guidance　for　obtaining　the　SSE　performance　limits　in　the　ORC　system.　(c)　2022　Elsevier　Ltd.　All　rights　reserved.