Optimizing wastewater treatment through artificial intelligence: recent advances and future prospects.

Area of Science:

• Environmental Engineering
• Computer Science
• Water Resource Management

Background:

• Wastewater treatment faces challenges in efficiency and resource optimization.
• Artificial intelligence (AI) offers potential solutions for improving these processes.

Purpose of the Study:

• To review AI models for wastewater treatment, focusing on pollutant removal efficiency prediction, real-time water quality monitoring, and fault detection.
• To assess the cost-effectiveness and global feasibility of AI in wastewater treatment.

Main Methods:

• Review of various AI models applied to wastewater treatment.
• Analysis of prediction accuracy (R² values) for pollutant removal.
• Examination of pilot projects and case studies worldwide.

Main Results:

• AI models achieve high accuracy (R²: 0.64-1.00) in predicting pollutant removal efficiency.
• AI enables real-time monitoring of key water quality parameters (pH, COD, BOD, etc.).
• Successful global pilot projects demonstrate AI's feasibility and positive impact.

Conclusions:

• AI is a transformative technology for optimizing wastewater treatment processes.
• AI implementation shows promise for cost-effectiveness and improved operational efficiency.
• Ethical considerations and future research directions for AI in wastewater treatment are identified.