MODELING AND CHARACTERIZATION OF ALGAL GROWTH IN ANAEROBIC DIGESTION WASTEWATER FOR PROCESS OPTIMIZATION

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Original abstract

Microalgae cultivation using anaerobic digestion (AD) wastewater is an attractive way of producing nutrient-rich biomass and treating wastewater. Integration of computational modeling provides valuable insights into algal-bacterial interactions and process scalability. With the help of metabolic flux analysis, kinetic modeling, cellular automata, and computational fluid dynamics (CFD), the predictive capability of algal growth patterns and reactor efficiency could be enhanced. Each modeling approach captures different facets of microalgal systems, including intracellular fluxes, nutrient kinetics, spatial heterogeneity, and hydrodynamic behavior, offering a more complete process understanding. Furthermore, artificial intelligence-based models have been investigated for nutrient utilization and downstream processes, thereby tackling key issues in the commercialization of microalgae. Sequentially integrating these models enables feedback-oriented optimization throughout the production process. Integrating experimental validation with advanced computational methods offers an effective means of optimizing microalgal growth in industrial scale-up. Microalgae, especially Chlorella vulgaris, have the potential to utilize both organic and inorganic carbon substrates, significantly enhancing biomass yield and nutrient recovery. Natural competition between algae and heterotrophic bacteria in anaerobic digestion effluent needs to be modeled and optimized for better algal growth. A mathematical model named Anaerobic Digestion Bacteria Algae (ADBA) was developed in this study to explain the kinetics of algae growth in anaerobic digestion wastewater. This was achieved by integrating Monod and Haldane kinetic models to simulate nutrient utilization, bacterial competition, and light attenuation. Experimental validation showed that optimized culture methods improved biomass yield, thus demonstrating the feasibility of large-scale application in wastewater bioremediation and biomass production. The R2 of the model fit was 0.90, adjusted for 25 kinetic parameters. The culture resulted in ~80% of total ammonium-nitrogen and 90% of organic carbon being reduced, along with odor removal and improvement in turbidity. Light utilization was observed to be high when effluent turbidity was < 0.5 absorption units (AU), and high light intensity offset the effect of turbidity. The ideal ammonium-nitrogen and organic carbon levels for algal growth were determined to be 100–1,000 mg/L and 5,000–10,000 mg/L, respectively. This research emphasizes the importance of process modeling in attaining economically viable and sustainable microalgal cultivation. The framework established herein enhances the basic understanding of mixotrophic algal cultivation and offers scalable biomass production and wastewater treatment solutions. By combining experimental and computational approaches, this research effort contributes to the knowledge of microalgae culture as a promising option for renewable energy and environmental sustainability.

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