Physics-Informed Machine Learning for Optimized and Sustainable Biochar Water Treatment

Area of Science:

• Environmental Engineering
• Materials Science
• Computational Science

Background:

• Biochar water treatment faces challenges due to reliance on empirical data and black-box models.
• Current methods hinder large-scale application and sustainable implementation.
• A need exists for a more integrated and physically consistent approach.

Purpose of the Study:

• To propose a novel research paradigm integrating physics-informed machine learning (PIML) and life cycle assessment (LCA) for biochar water treatment.
• To address the limitations of empirical experimentation and black-box models in the field.
• To outline pathways and identify research gaps for advancing biochar technology.

Main Methods:

• Conceptual framework integrating PIML and LCA with bidirectional information flow.
• Embedding fundamental physical laws (adsorption, kinetics, thermodynamics) into PIML architectures.
• Extending the framework to a closed-loop water-energy-soil-food system for holistic management.

Main Results:

• The proposed framework enables simultaneous material design and sustainability assessment.
• Ensures physical consistency in PIML models by incorporating physical laws.
• Identifies explainability and cross-scale generalization as critical gaps for industrial deployment.

Conclusions:

• The integrated PIML-LCA framework offers a promising direction for sustainable biochar water treatment.
• Addressing identified research gaps is crucial for industrial adoption and closed-loop resource management.
• This perspective provides a roadmap for future research towards sustainable biochar technology implementation.