Abstract
The bioprocess industry is actively exploring technologies associated with the fourth industrial revolution, with modeling offering considerable potential for process optimization. Nevertheless, model adoption in industry remains limited. This is partly because model development continues to depend heavily on offline sampling, and because relatively few industrial applications convincingly demonstrate their practical value. This study therefore first examines the benefits of online rheology and online biomass measurements for model development and demonstrates, among other aspects, that online biomass significantly improves model fidelity. The second part examines how electricity prices affect process conditions, a key factor in production, and finds that, contrary to common practice, maximizing all operating parameters is not the most cost-effective strategy. Finally, an insilico framework for model predictive control, applied to a reactor endfill scenario, demonstrates that oxygencontrolled processes can be dynamically optimized, highlighting the strong potential of bioprocess models for industrial usage.