Ball　mill　is　a　heavy　mechanical　device　necessary　for　grinding.　Mill　load　parameters　(MLP)　relate　to　production　economic　indices　and　process　safety.　Mechanical　signals　of　the　ball　mill　are　used　to　estimate　MLP　by　domain　experts.　However,　they　can　only　estimate　familiar　mills　effectively　in　certain　time　because　of　human　limitation.　A　new　dual-layer　optimized　selective　information　fusion　is　proposed　based　on　the　analysis　of　the　characteristics　of　mill　mechanical　signals　and　cognitive　behavior　of　the　domain　expert　for　MLP　forecasting　(MLPF).　An　ensemble　construction　strategy　based　on　multi-component　mechanical　signals　adaptive　decomposition　is　employed　to　build　candidate　sub-models　by　using　kernel　partial　least　squares　(KPLS).　The　dual-layer　optimization　strategy　is　proposed　to　build　selective　ensemble　(SEN)　KPLS　(SENKPLS)　with　optimized　ensemble　sub-models　and　their　coefficients,　thus　realizing　the　trade-off　between　prediction　accuracies　and　diversity　implicitly.　The　MLPF　models　based　on　SENKPLS　are　constructed　by　selective　fusion　multi-source　multi-scale　frequency　spectral　information　in　terms　of　the　auditory　perception　process　of　the　simulation　domain　experts.　Results　show　that　the　proposed　strategy　can　obtain　better　forecasting　results　than　other　state-of-the-art　methods.　(C)　2019　Elsevier　Ltd.　All　rights　reserved.