(396g) Enhanced Polyhydroxyalkanoate (PHA) Production through Multiscale Modeling and Process Control Strategies: A Novel Approach to Bio-Based Polymer Synthesis

In the current era marked by a high prevalence of plastics, most of these materials rely on non-renewable petrochemical sources and take an extremely long time to decompose. This contributes to landfilling as the primary method for plastic disposal [1, 2], while their combustion generates carbon dioxide, a greenhouse gas, and toxic byproducts like dioxins [3]. Consequently, bio-based polymers like polyhydroxyalkanoates (PHAs) have emerged as a promising alternative. These biopolymers are biocompatible, biodegradable, marine-degradable, and can be synthesized from natural resources like sugar through fermentation, with diverse applications [4, 5].

PHA can form various structures with one, two, or three monomeric species. For example, the homopolymer of 3-hydroxyburtyrate (3HB) is commonly produced due to its superior mechanical properties [6]. However, being a crystalline polymer, P(3HB) exhibits brittleness and stiffness [7]. To overcome these weaknesses, variants such as poly(3-hydroxybutyrate-co-4-hydroxybutyrate) (P(3HB-co-4HB) have been developed, offering some degree of rubbery properties [8]. Nevertheless, commercial production of P(3HB-co-4HB) remains limited due to its complex nature. A range of process inputs, including carbon source, oxygen content, pH, and temperature, influence the polymer properties [6]. Although the molecular weight distribution and monomeric fraction directly affect these properties, predicting them in practice is challenging. Therefore, a mathematical model is needed to understand how various system variables impact process outputs.

Motivated by these challenges, we have developed a novel multiscale modeling framework that accurately represents the complex nature of PHA production. This framework consists of two interconnected components. First, from a macroscopic perspective, fermentation occurs at the bioreactor scale. In this layer, cell metabolism is modeled using differential equations that account for carbon source consumption, monomer/by-product production, and variations in operating temperature, feed rate, pH, and other factors. Second, the microscopic layer focuses on PHA polymerization kinetics, which involves complex processes such as initialization, elongation, and termination reactions occurring among multiple polymer chains within cells. To capture these phenomena, kinetic Monte Carlo (kMC) simulations are employed, enabling the calculation of PHA chain length distribution and monomeric fraction.

Once developed, the model is validated using real-world process data. An on-line control strategy based on a model predictive controller (MPC) is then developed to optimize the process. The MPC determines optimal process inputs like temperature and feed rate, while satisfying process constraints, in order to achieve the desired biopolymer properties such as chain length distribution and monomeric fraction. Moreover, since these key parameters are unmeasurable during the process, a Kalman filter is designed to estimate them based on available system information like operating temperature and pH. The results demonstrate that the developed multiscale modeling framework can achieve desired set-point targets while used in an MPC, indicating the model’s practical applicability. Furthermore, the novel multiscale model predicts state concentrations such as substrate, oxygen, biomass, and PHA with high accuracy when compared to industry process data. In conclusion, this unique multiscale modeling approach effectively integrates fermentation and polymerization kinetics to obtain the desired biopolymer properties.

Literature cited:

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