Dynamic Optimization of a Fed-Batch Nosiheptide Reactor

Nosiheptide is a sulfur‐containing peptide antibiotic, showing exceptional activity against critical pathogens such as Methicillin‐Resistant Staphylococcus Aureus (MRSA) and Vancomycin‐Resistant Enterococci (VRE) with applications for livestock and can be synthesized via fed‐batch fermentation. A simplified mechanistic fed‐batch fermentation model for nosiheptide production from the literature considers temperature‐ and pH‐dependence of biomass growth, substrate consumption, nosiheptide production and oxygen mass transfer into the fermentation broth. Herein, we perform dynamic simulation over a broad range of possible feeding policies to understand and visualize the region of attainable reactor performances and productivities. We then formulate a dynamic optimization problem for maximization of nosiheptide production for different constraints of batch duration subject to operability constraints. A direct method for dynamic optimization (simultaneous strategy) has been performed in each case to compute the optimal control trajectories. Orthogonal polynomials on finite elements are used to approximate the control and state trajectories allowing the continuous problem to be converted to a Nonlinear Programme (NLP). The resultant large‐scale NLP problem is solved using IPOPT. Optimal operation requires feed rate to be manipulated in such a way that the inhibitory mechanism of the substrate can be avoided, with significant nosiheptide yield improvement realized.