Modeling is a crucial tool in the biomanufacturing industry, namely in fermentation processes. This work discusses both mechanistic and data-driven models, each with unique benefits and application potential. It discusses semi-parametric hybrid modeling, a growing field that combines these two types of models for more accurate and easy result extrapolation. The characteristics and structure of such hybrid models will be examined. Moreover, its versatility will be highlighted, showing its usefulness in various stages of process development, including real-time monitoring and optimization. Scale-up remains one of the most relevant topics in fermentation processes, as it is important to have reproducible critical quality attributes, such as titer and yield, on larger scales. Furthermore, the process still relies on empirical correlations and iterative optimization. For these reasons, it is important to improve scale-up predictions, through e.g., the use of digital tools. Perspectives will be presented on the potential that hybrid modeling has by predicting performance across different process scales. This could provide more efficient and reliable biomanufacturing processes that require less resource consumption through experimentation.

The agitation power input substantially impacts the oxygen transfer rate in aerobic fermentation processes. Furthermore, in a carbon‐limited process where the substrate feed rate is controlled by the concentration of dissolved oxygen, the power input will consequently govern the feed rate. Thus, these external design factors affect the cell metabolic rates significantly. We aim to investigate the impact of different power input levels, 11.6 and 2.2 kW/m ³ , on the microorganism's cellular metabolism by applying proteomics and transcriptomics analyses on a pilot‐scale (550 L) enzyme‐producing Aspergillus oryzae fermentation process. Transcriptomics results indicate differences in carbon metabolism regulation and nutrient uptake in two different power input levels, with upregulation of carbon and ammonia transporters and downregulation of phosphate transporters in the lower power input level. Proteasome analysis reveals significant differences in ribosome‐associated proteins, corroborating higher product yield on substrate in the lower power input batches. This multi‐omics approach allows the identification of key metabolic differences that standard process characterization methods, such as cell dry weight quantification and off‐gas analyses, cannot capture. The study also demonstrates the potential of omics‐focused sampling techniques on a pilot (or production) scale for in‐depth process understanding and, therefore, further optimization of fermentation processes.