Rethinking Activated Sludge Modeling: A Critical Review of Modeling Strategies and the Role of Hybrid Integration

Area of Science:

• Environmental Engineering
• Computational Science

Background:

• Efficient wastewater treatment is crucial for the UN's Sustainable Development Goals, facing challenges from industrialization and population growth.
• Conventional mechanistic (white-box) models, like Activated Sludge Models, offer structured insights but struggle with dynamic microbial interactions and influent variability.
• Existing models require innovative technologies to reduce energy consumption and chemical reliance.

Purpose of the Study:

• To explore artificial intelligence (AI) techniques, specifically machine learning, for wastewater treatment modeling.
• To address the limitations of white-box models in capturing complex dynamics and real-world variability.
• To advocate for hybrid modeling approaches combining mechanistic understanding with AI.

Main Methods:

• Review of conventional mechanistic modeling techniques.
• Exploration of artificial intelligence (AI) and machine learning (ML) approaches for data-driven modeling.
• Proposal of a hybrid modeling framework integrating mechanistic and AI methods.

Main Results:

• AI models excel at identifying complex patterns in large datasets but require high-quality data and lack interpretability.
• Hybrid models combining mechanistic frameworks with AI show potential for enhanced predictive capabilities and robustness.
• A perspective framework incorporating multi-omics data into a hybrid digital twin is proposed for improved monitoring and decision-making.

Conclusions:

• Hybrid modeling offers a promising path to overcome limitations of traditional and purely AI-based approaches in wastewater treatment.
• Integrating multi-omics data within a hybrid digital twin framework can significantly enhance wastewater treatment efficiency.
• Future research should focus on developing and validating these integrated hybrid models for practical applications.