Dynamic Model Selection and Optimal Batch Design for Polyhydroxyalkanoate (PHA) Production by Cupriavidus necator

Abstract

Mathematical modelling of microbial polyhydroxyalkanoates (PHAs) production is essential to develop optimal bioprocess design. Though the use of mathematical models in PHA production has increased over the years, the selection of kinetics and model identification strategies from experimental data remains largely heuristic. In this study, PHA production from Cupriavidus necator utilizing sucrose and urea was modelled using a parametric discretization approach. Product formation kinetics and relevant parameters were established from urea-free experimental sets, followed by the selection of growth models from a batch containing both sucrose and urea. Logistic growth and Luedeking-Piret model for PHA production was selected based on regression coefficient (R²: 0.941), adjusted R² (0.930) and AICc values (−42.764). Model fitness was further assessed through cross-validation, confidence interval and sensitivity analysis of the parameters. Model-based optimal batch startup policy, incorporating multi-objective desirability, suggests an accumulation of 2.030 g l⁻¹ of PHA at the end of 120 h. The modelling framework applied in this study can be used not only to avoid over-parameterization and identifiability issues but can also be adopted to design optimal batch startup policies.