Advancing biorefinery design through the integration of metabolic models

Abstract

This study introduces an innovative methodology for early-stage biorefinery design and analysis through the integration of metabolic models into superstructure optimization. The focus is on two types of metabolic models: i) those where ideal growth conditions are assumed, and ii) those where growth is dependent on environmental variables. Metabolic models offer a comprehensive and dynamic representation of the bioreactor system, unbound by the limitations of traditional kinetic models or the need of extensive experimental data. The integration of these models involves a curation and validation process, ensuring that the metabolic capabilities of the microorganisms are accurately represented. Once validated, the models are plugged into the superstructure in the form of surrogate equations that can be handled by the optimization problem for the superstructure. The effectiveness of this methodology is then tested and critically analyzed with the help of two case studies. The primary contribution of this work lies in its effective identification of the most favorable combinations of substrates and microorganisms. The outcome establishes the feasibility of utilizing different substrates and microorganisms in a biorefinery context, thereby highlighting the value of metabolic models in a superstructure optimization framework for exploring early-stage biorefinery schemes. This approach holds significant promise in enhancing the efficiency and sustainability of future biorefinery systems.