Kinetic modelling of Chlorella spp. wastewater treatment and resource recovery in the food industry

Effluents generated in the food and beverage industry typically contain elevated concentrations of organic carbon sources and inorganic nutrients, representing a significant environmental burden as global food demand continues to rise. Owing to their capacity to effectively assimilate carbon and nutrients while simultaneously generating biomass with potential valorization pathways, Chlorella spp. offers a dual benefit of contaminant removal and resource recovery. However, existing algal studies remain predominantly at the laboratory scale, and their competitive advantages have not been demonstrated for full-scale implementation. Kinetic modelling represents an essential tool to elucidate algal growth behavior, optimize operational conditions, and predict performance of algal systems for accurate scale-up, but limited studies are available to comprehensively review kinetic models of Chlorella spp. In this review, kinetic models were synthesized, including sigmoidal growth models (Gompertz, logistic, and Verhulst), single-factor and inhibition models (Monod and Andrews), light-dependent photobioreactor models (Huisman), multifactor and interaction models for co-cultures (Lotka-Volterra). Kinetic parameters from these models were compiled to support consistent projections from lab-scale to industrial-scale systems, enabling future techno-economic analysis and life-cycle assessment of algal systems. Other emerging technologies, such as artificial intelligence and machine learning approaches, were also reviewed. The review also identified benchmark processes and outlined a decision tree for developing strategies to enhance resource recovery and phycoremediation performance. Finally, key research needs have been highlighted to guide future work in the field.