Hybrid　modelling　utilizes　advantageous　aspects　of　both　mechanistic　(white　box)　and　data-driven　(black　box)　modelling.　Combining　the　physical　interpretability　of　kinetic　modelling　with　the　power　of　a　data-driven　Artificial　Neural　Network　(ANN)　yields　a　hybrid　(grey　box)　model　with　superior　accuracy　when　compared　to　a　traditional　mechanistic　model,　while　requiring　less　data　than　a　purely　data-driven　model.　This　study　aims　to　construct　a　hybrid　model　for　the　predictive　modelling　of　a　high-cell-density　microalgal　fermentation　process　for　lutein　production　under　uncertainty.　In　addition,　transfer　learning　is　combined　with　the　hybrid　model　to　simulate　new　fed-batches　utilizing　alternative　substrates　operated　under　a　different　reactor　scale.　By　comparing　with　experimental　data,　the　hybrid　transfer　model　was　found　to　be　able　to　simulate　the　new　fed-batch　processes　that　achieve　heightened　cell　densities　and　higher　product　quantities.　Overall,　this　work　presents　a　novel　digital　model　construction　strategy　that　can　be　easily　adapted　to　general　bioprocesses　for　model　predictive　control　and　process　optimization　under　uncertainty.　Copyright　©　2025　The　Authors.