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Efficaciously assessing product quality remains time- and resource-intensive. Online Process Analytical Technologies (PATs), encompassing real-time monitoring tools and soft-sensor models, are indispensable for understanding process effects and real-time product quality. This research study evaluated three modeling approaches for predicting CHO cell growth and production, metabolites (extracellular, nucleotide sugar donors (NSD) and glycan profiles): Mechanistic based on first principle Michaelis-Menten kinetics (MMK), data-driven orthogonal partial least square (OPLS) and neural network machine learning (NN). Our experimental design involved galactose-fed batch cultures. MMK excelled in predicting growth and production, demonstrating its reliability in these aspects and reducing the data burden by requiring fewer inputs. However, it was less precise in simulating glycan profiles and intracellular metabolite trends. In contrast, NN and OPLS performed better for predicting precise glycan compositions but displayed shortcomings in accurately predicting growth and production. We utilized time in the training set to address NN and OPLS extrapolation challenges. OPLS and NN models demanded more extensive inputs with similar intracellular metabolite trend prediction. However, there was a significant reduction in time required to develop these two models. The guidance presented here can provide valuable insight into rapid development and application of soft-sensor models with PATs for ipurposes. Therefore, we examined three model typesmproving real-time product CHO therapeutic product quality. Coupled with emerging -omics technologies, NN and OPLS will benefit from massive data availability, and we foresee more robust prediction models that can be advantageous to kinetic or partial-kinetic (hybrid) models. 

Abstract A new biomanufacturing platform combining intracellular metabolic engineering of the oleaginous yeastYarrowia lipolyticaand extracellular bioreaction engineering provides efficient bioconversion of plant oils/animal fats into high‐value products. However, predicting the hydrodynamics and mass transfer parameters is difficult due to the high agitation and sparging required to create dispersed oil droplets in an aqueous medium for efficient yeast fermentation. In the current study, commercial computational fluid dynamic (CFD) solver Ansys CFX coupled with the MUSIG model first predicts two‐phase system (oil/water and air/water) mixing dynamics and their particle size distributions. Then, a three‐phase model (oil, air, and water) utilizing dispersed air bubbles and a polydispersed oil phase was implemented to explore fermenter mixing, gas dispersion efficiency, and volumetric mass transfer coefficient estimations (k_La). The study analyzed the effect of the impeller type, agitation speed, and power input on the tank's flow field and revealed that upward‐pumping pitched blade impellers (PBI) in the top two positions (compared to Rushton‐type) provided advantageous oil phase homogeneity and similar estimatedk_Lavalues with reduced power. These results show good agreement with the experimental mixing andk_Ladata.