The　conventional　data-driven　soft　sensor　methods　such　as　multiway　partial　least　squares　have　been　encountering　nonlinear　problems　in　predictions　of　batch　processes,　and　kernel　methods　have　been　used　to　deal　with　these　problems.　In　this　work,　a　new　data-driven　soft　sensor　method　is　proposed　by　developing　a　Reduced　Dual　Kernel　multiway　partial　least　squares　algorithm.　First,　the　number　of　kernel　vectors　is　reduced　by　the　feature　vector　selection　method.　Then,　by.　projecting　both　input　data　and　the　output　data　into　two　reduced　kernel　spaces,　dual　kernel　matrices　are　established.　These　two　matrices　can　be　used　to　build　PLS　models.　Finally,　the　predicted　data　in　the　kernel　space　can　be　reversely　projected　onto　its　original　space　during　online　prediction.　Comparisons　were　made　among　the　proposed　method　and　some　pervious　algorithms　through　a　numerical　example　and　an　Escherichia　coli　fermentation　batch　process.　(C)　2016　Elsevier　B.V.　All　rights　reserved.