(629d) Multi-Objective Dynamic Optimisation of a Fed-Batch Nosiheptide Reactor

Rodman, A. D. - Presenter, University of Edinburgh
Gerogiorgis, D. I., University of Edinburgh
Thiopeptide antibiotics are highly potent and structurally complex; this growing class of secondary metabolites are formed by ribosomal peptide biosynthesis. Nosiheptide, a sulfur-containing peptide antibiotic obtained through fermentation, can be regarded as the structural prototype of the class e thiopeptide antibiotics. It exerts exceptional antibiotic activity in vitro and in a mouse model against critical Gram-positive pathogens such as MRSA, VRE, or Clostridium difficile. Shown non-toxic at high dosages it has previously been used for applications in farm animals (Benazet at al., 1980). Recently the first total synthesis of nosiheptide was reported, utilizing double macrocyclization of a fully functionalized linear precursor (Wojtas et al., 2016). Given low indusial yields, strong motivation exists to dynamically optimise the process for improved product yield while reducing production time and cost. Similar multi-objective optimisation problems are common in the chemical industry (Rodman, Fraga and Gerogiorgis, 2017), which can be solved with both stochastic and deterministic techniques.

The nosiheptide fermentation process is an extremely complex biochemical reaction, with the complete reaction mechanism not strictly understood. A simplified mechanistic fermentation process model has been proposed, considering only the most essential interactions in the biochemical network (Wojtas et al., 2016). The model considers the temperature and pH dependence of biomass growth, in addition to substrate consumption and product production, with the 19 model parameters determined from the model authors experimental campaigns.

To circumvent mass transfer inhibition at excessive substrate concentrations in the broth the nosiheptide reactor is operated in a fed-batch mode, where the reactor is only partially filled initially, with substrate supplemented over time. Herein the multi-objective dynamic optimisation problem can be explicitly formulated: our two minimisation criteria are batch time and inverse yield with four decision (control) profiles to compute towards optimising the system: both the dynamic feed flow rate and the dissolved oxygen concentration must be determined in addition to the reactor temperature and pH profile subject to operability constraints.

This contribution employs (epsilon constrained or variable weighted sum) handling of the bi-objectives problem, allowing the Pareto front trade-off between the competing objectives to be visualised. A direct method for dynamic optimisation (simultaneous strategy) has been performed in each case to compute the four 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 NLP form (Biegler, Cervantes, and Wächter, 2007). The resultant large scale NLP problem is been solved for each instance using IPOT (Wächter and Biegler, 2006), and global optimality is ensured with a multi-start search via Latin Hypercube Sampling of the input space for initialisation.

The algorithm employed demonstrates that appropriate reactor operation and control can significantly improve product yield. Additionally, explicit visualization of the objective trade-offs provides valuable insight to decision makers towards critical economic process decisions.


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