Abstract: The use of microalgae to recover nitrogen and phosphorus from wastewater has garnered significant attention, positioning it as one of the most promising and sustainable strategies in modern wastewater treatment. While various photobioreactors (PBRs) configurations have been widely applied for microalgae cultivation, limited research has focused on optimizing PBR design specifically to enhance nitrogen and phosphorus removal efficiency. The high operational costs of wastewater treatment, combined with the inherent variability of microalgal growth, have prompted the search for advanced solutions that improve nitrogen and phosphorus removal while minimizing resource consumption and enabling predictive process control. Recently, the integration of PBR systems with artificial intelligence and machine learning (AI/ML) modeling has emerged as a transformative approach to enhancing nutrient removal, particularly for nitrogen and phosphorus. This study first summarizes existing PBR designs tailored for diverse applications, then outlines strategies for system enhancement through the optimization of mixing methods, construction materials, light intensity, and light source configuration. Furthermore, computational fluid dynamics (CFD) and AI/ML modeling are presented as tools to guide the structural design and operational optimization of microalgae-based nitrogen and phosphorus removal processes. Finally, future research directions and key challenges are discussed.

Key words: Microalgae, Photobioreactor, Design, Optimization, Nitrogen phosphorus removal, Artificial intelligence

摘要： The use of microalgae to recover nitrogen and phosphorus from wastewater has garnered significant attention, positioning it as one of the most promising and sustainable strategies in modern wastewater treatment. While various photobioreactors (PBRs) configurations have been widely applied for microalgae cultivation, limited research has focused on optimizing PBR design specifically to enhance nitrogen and phosphorus removal efficiency. The high operational costs of wastewater treatment, combined with the inherent variability of microalgal growth, have prompted the search for advanced solutions that improve nitrogen and phosphorus removal while minimizing resource consumption and enabling predictive process control. Recently, the integration of PBR systems with artificial intelligence and machine learning (AI/ML) modeling has emerged as a transformative approach to enhancing nutrient removal, particularly for nitrogen and phosphorus. This study first summarizes existing PBR designs tailored for diverse applications, then outlines strategies for system enhancement through the optimization of mixing methods, construction materials, light intensity, and light source configuration. Furthermore, computational fluid dynamics (CFD) and AI/ML modeling are presented as tools to guide the structural design and operational optimization of microalgae-based nitrogen and phosphorus removal processes. Finally, future research directions and key challenges are discussed.

关键词: Microalgae, Photobioreactor, Design, Optimization, Nitrogen phosphorus removal, Artificial intelligence