A new paradigm in activated sludge management: an interpretable framework integrating mechanistic dynamics with a data-driven surrogate model.

Stable operation of activated sludge systems is frequently compromised by static thresholds, which often result in false alarms and purely data-driven "black box" models. To address this issue, this study developed and validated a three-tiered intelligent management framework that integrates both "knowledge-driven" and "data-driven" approaches. The first tier involves knowledge-driven diagnostic process. A dynamic model based on differential equations captures the core principles of "ecosystem balance". This model incorporates key information extracted from high-dimensional data (water quality, microorganisms, and extracellular polymeric substances(EPS)) through principal component analysis (PCA) to calculate the system's "dynamic theoretical equilibrium point" for the Sludge Volume Index (SVI), denoted as SVIeq. The model successfully reproduced historical macro-evolutionary trends in the SVI. The second tier is a data-driven proxy diagnostic. The core of this stage is "proxy modeling": risk states generated in the first stage, which have clear physical significance (the deviation between SVI and SVIeq), are used as high-quality labels to train an efficient random forest classifier. This model acts as a lightweight "proxy" for the mechanistic model, enabling the real-time diagnosis of the system state. The proxy model demonstrated excellent classification performance, providing reliable early warnings of future deterioration risk. The third tier uses SHAP (Shapley Additive Explanations) technology to provide interpretability. This provides precise, quantitative explanations for the diagnostic results by identifying the specific operational factors responsible for the risk.