Advancing carbon-neutral wastewater treatment: Artificial intelligence-driven strategies for emission mitigation and...

Area of Science:

• Environmental Engineering
• Computer Science
• Sustainable Technology

Background:

• Wastewater treatment plants (WWTPs) contribute significantly to global carbon emissions (1-2%).
• Traditional methods struggle with multi-objective optimization for emission reduction, energy efficiency, and resource recovery.
• Artificial intelligence (AI) presents a promising alternative for optimizing complex WWTP processes.

Purpose of the Study:

• To systematically review AI applications for greenhouse gas (GHG) emission reduction in WWTPs.
• To highlight AI's role in predictive analytics, process optimization, and resource recovery.
• To identify challenges and propose a roadmap for AI implementation in sustainable wastewater treatment.

Main Methods:

• Systematic literature review of AI applications in wastewater treatment for carbon emission mitigation.
• Analysis of AI techniques including machine learning (ML) and artificial neural networks (ANN).
• Evaluation of AI's efficacy in GHG flux forecasting, adaptive control, and resource integration.

Main Results:

• AI demonstrates operational effectiveness in forecasting GHG emissions and optimizing aeration and chemical dosing.
• AI facilitates the integration of renewable energy sources within WWTPs.
• Key challenges include data heterogeneity, model generalizability, and integration with existing infrastructure.

Conclusions:

• AI holds transformative potential for advancing sustainable wastewater treatment and reducing carbon footprint.
• Development of hybrid physics-informed AI models and explainable decision-support systems is recommended.
• Addressing implementation barriers is crucial for the full-scale adoption of AI in WWTPs.