In this study, we have developed a dynamic flux balance analysis (DFBA) model to simulate the growth behavior of Chlorella sp. cultivated in a fed-batch bioreactor utilizing fermentation waste such as acetate and butyrate as substrate. To enhance biomass productivity and substrate utilization efficiency, supplementary carbon sources such as glycerol and glucose were added to the fed stream. The genome-scale metabolic network of Chlamydomonas reinhardtii (iRC1080) was used as a reference model and subsequently modified to include the metabolic pathways for glucose and glycerol metabolism. To ensure biological relevance in biomass predictions, the Biological Objective Solution Search (BOSS) algorithm was applied to determine the appropriate stoichiometric coefficients for the biomass growth equation. This was accomplished by using the available flux data reported in literature involving various Chlorella species grown under various growth modes such as phototrophic, heterotrophic, and mixotrophic, utilizing the different carbon sources. This was accomplished by using available flux data from a range of literature-reported experimental studies involving various Chlorella species grown under phototrophic, heterotrophic, and mixotrophic conditions using different carbon sources.
Once the biomass objective function was refined, the DFBA simulations were carried out using the modified genome-scale model to predict the time evolution of biomass growth, substrate depletion, and nitrate consumption. These simulated profiles were validated against experimental results from open literature, confirming the model’s predictive accuracy. To improve computational performance and facilitate integration into a control framework, the metabolic model was systematically reduced using the NetworkReducer algorithm. The reduction preserved essential metabolic functionalities required for accurate simulation of growth and nutrient uptake dynamics.
The reduced model was embedded into a Model Predictive Control (MPC) framework to dynamically optimize biomass productivity and substrate utilization under environmental disturbances, particularly light intensity and temperature fluctuations conditions commonly encountered in real-world algal cultivation systems. The MPC optimization strategy utilized the dilution rate, glycerol, glucose, and nitrate feed concentrations as manipulated input variables. A multi-objective cost function was formulated to simultaneously maximize biomass productivity and minimize the cost associated with the consumption of additional carbon substrates (glycerol and glucose). This multi-objective problem was scalarized into a single-objective function by assigning weights () to each term, thus enabling prioritization and efficient solutions via standard MPC optimization routines.
Simulation results demonstrated that the MPC-guided DFBA model significantly outperformed the uncontrolled (open-loop) scenario in terms of biomass productivity, substrate efficiency, and process stability. This integrated modeling and control framework illustrates a powerful approach for optimizing algal cultivation processes, particularly in resource-variable environments. The study highlights the potential of combining systems biology (DFBA) and advanced control theory (MPC) to drive intelligent and cost-effective microalgal bioprocesses for sustainable biomanufacturing.