An artificial intelligence modeling framework based on microbial community structure prediction enhances the pollutant removal efficiency of the algae-bacteria granular sludge system

> 0.94). Finally, the non-dominated sorting genetic algorithm was used to integrate the prediction models of the two stages, and the microbial community structure was selectively shaped to enhance the removal efficiency of any two of the carbon, nitrogen, and phosphorus pollutants in the ABGS system (removal rate >90 %). The results of this study provided a universally applicable and quantitative intelligent guidance model for the performance optimization of ABGS technology and other biological systems.