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

Biochar water treatment stands at a decisive crossroads, where the promise of large-scale application meets the reality of laboratory trial-and-error. This study contends that the fundamental bottleneck to progress lies in the field's persistent reliance on empirical experimentation and black-box data models. We therefore propose a conceptual research paradigm that aims to deeply integrate physics-informed machine learning (PIML) with life cycle assessment (LCA). The novelty of this framework lies in three dimensions: (i) the bidirectional information flow between PIML and LCA, enabling simultaneous material design and sustainability assessment; (ii) the embedding of fundamental physical laws (adsorption isotherms, kinetics, thermodynamics) directly into learning architectures to ensure physical consistency; and (iii) the extension to a water-energy-soil-food closed-loop system for holistic resource management. While the individual components of this framework have been demonstrated in other domains, their integrated application to biochar water treatment remains in early development stages. This perspective outlines potential pathways and identifies critical research gaps that must be addressed to realize this vision. The focus is on charting future directions rather than reporting established achievements. Through critical evaluation, we assess current integrated models under small-sample constraints and explicitly pinpoint explainability and cross-scale generalization as two indispensable gaps that industrial deployment demands be bridged. Building on this foundation, we outline a blueprint for a closed-loop system coupling water, energy, soil, and food, and present a three-phase roadmap for future research. This study seeks to offer a constructive perspective with the hope of supporting biochar technology toward more sustainable implementation.