In　this　paper,　a　new　learning　algorithm　named　OEM-ELM　(Online　Error　Minimized-ELM)　is　proposed　based　on　ELM　(Extreme　Learning　Machine)　neural　network　algorithm　and　the　spreading　of　its　main　structure.　The　core　idea　of　this　OEM-ELM　algorithm　is:　online　learning,　evaluation　of　network　performance,　and　increasing　of　the　number　of　hidden　nodes.　It　combines　the　advantages　of　OS-ELM　and　EM-ELM,　which　can　improve　the　capability　of　identification　and　avoid　the　redundancy　of　networks.　The　adaptive　control　based　on　the　proposed　algorithm　OEM-ELM　is　set　up　which　has　stronger　adaptive　capability　to　the　change　of　environment.　The　adaptive　control　of　chemical　process　Continuous　Stirred　Tank　Reactor　(CSTR)　is　also　given　for　application.　The　simulation　results　show　that　the　proposed　algorithm　with　respect　to　the　traditional　ELM　algorithm　can　avoid　network　redundancy　and　improve　the　control　performance　greatly.　(C)　2016　ISA.　Published　by　Elsevier　Ltd.　All　rights　reserved.