Batch　processes　inherently　have　multiple　operation　phases;　different　phases　exhibit　different　characteristics.　Hence,　it　is　reasonable　to　partition　the　process　into　phases　and　build　sub-phase　models　for　online　quality　prediction.　To　this　end,　a　sequential　phase　partition　method　based　on　the　information　increment　is　proposed.　To　address　the　multiphase　behaviours　in　batch　processes,　this　work　utilizes　a　new　information　increment　index　to　capture　the　dynamic　characteristics　of　batch　processes　along　a　time　direction　and　divides　the　process　into　sub-phases.　Next,　phase-based　multiway　partial　least　squares　(MPLS)　models　are　built　to　model　within-phase　characteristics　and　predict　the　quality-related　variables　online.　Information　increment　is　able　to　exploit　the　process　evolution　by　focusing　on　the　changing　variable　correlations　derived　from　two　adjacent　extend　time　slice.　It　directly　utilizes　the　available　process　measurements　of　successful　history　batch　processes　without　data　transformation　or　dimensionality　reduction.　The　method　is　sequential　and　can　overcome　the　limits　of　some　phase　partition　methods　that　may　divide　the　samples　with　discontinuous　time　sequence　but　similar　characteristics　into　the　same　phase.　In　addition,　the　information　increment　is　capable　of　reflecting　the　change　of　the　process　intuitively　with　high　computation　efficiency.　Advantages　of　the　proposed　method　are　illustrated　by　two　case　studies,　a　penicillin　simulation　platform　and　an　industrial　application　of　Escherichia　coli　(E.　coli)　fermentation,　respectively.