Maximizing batch fermentation efficiency by constrained model‐based optimization and predictive control of adenosine triphosphate turnover

We present a constrained model-based optimization and predictive control framework to maximize the production efficiency of batch fermentations based on core idea manipulating adenosine triphosphate (ATP) wasting. In many bioprocesses, enforced ATP wasting —rerouting use towards an energetically possibly suboptimal path— allows increasing metabolic flux product, thereby enhancing product yields specific productivities. However, this often comes at expense lower biomass reduced volumetric To overall efficiency, we formulate as optimal problem. This for balancing trade-offs between different objectives such yield productivity fermentations. Unlike static control, one obtains higher degree flexibility, adaptability, competitiveness. can be advantageous achieving sustainable economically efficient biotechnology industry. compensate model-plant mismatch, disturbances, uncertainties, propose not only solving problem once. Instead, exploit concept moving horizon model combined with constraint-based dynamic modeling capture fermentation dynamics. The approach is underlined considering industrially relevant bioproduction lactate by Escherichia coli. discuss practical challenges described strategy provide outlook future developments.