Load　parameters　inside　ball　mill　are　dif　ficulty-to-measure　key　process　variables　relative　to　production　quality　and　quantity　of　the　whole　grinding　process.There　are　complex　nonlinear　mapping　relationships　between　mill　load　parameters(MLPs)and　multi-source　mechanical　frequency　spectral　data.Kernel　project　to　latent　structure(KPLS)algorithm　is　suitable　to　build　mill　load　parameter　forecasting(MLPF)model　based　on　such　frequency　spectral　data.Aim　to　these　problems,　a　new　adaptive　multi-kernel　projection　to　latent　structure　selective　ensemble(SEN)model　for　MLPF　is　proposed.At　first,　candidate　sub-signals＇frequency　spectral　data　with　different　time　scales　are　obtained　by　using　ensemble　empirical　model　decomposition(EEMD)and　time/frequency　transformation　techniques　from　multi-source　mechanical　signals.Then,　candidate　sub-sub-models　and　SEN-sub-models　are　constructed　based　on　different　frequency　spectral　data　by　using　KPLS　and　branch&bound　SEN(BBSEN)algorithms.Finally,　the　candidate　sub-signal　models　are　optimal　selected　from　these　candidate　sub-sub-models　and　SEN-sub-models;BBSEN　is　used　again　to　select　ensemble　sub-signal　models　from　these　can　didateones　and　to　weight　them.Therefore,　ch　reque　MLPF　model　is　constructed.Simulation　results　of　a　laboratory-scale　ball　mill　show　effectiveness　of　the　proposed　approach.　©　2019,　Editorial　Department　of　Control　Theory　&　Applications　South　China　University　of　Technology.　All　right　reserved.