Abstract
The global challenges of water scarcity and environmental pollution are intensifying. Nutrients such as nitrogen and phosphorus in wastewater not only contribute significantly to water pollution but also represent valuable alternative resources to synthetic fertilizers in agricultural production. Efficient recovery of these nutrients can mitigate environmental pressures and support sustainable agriculture. However, conventional wastewater treatment and nutrient recovery methods often face limitations, including low efficiency, high costs, and operational complexity. In recent years, advances in artificial intelligence (AI)—including intelligent monitoring, deep learning–based modeling, and process optimization—have opened new technological pathways for wastewater nutrient recovery. This paper systematically reviews key technologies and persistent challenges in this field, with particular emphasis on AI applications in real-time monitoring, separation process optimization, and intelligent control. Drawing on practical cases and experimental data, the paper evaluates the performance of AI-driven systems in enhancing recovery efficiency, reducing energy consumption, and improving economic outcomes. It also explores the potential of data-driven decision-support tools and intelligent fertilization recommendation systems in agricultural contexts. Finally, the paper discusses prospects for the deep integration of AI with wastewater resource recovery technologies and the need for supportive policy frameworks, providing a scientific basis and technical reference for advancing intelligent wastewater treatment and nutrient recovery.
