Data-driven modeling of combined electrocoagulation and sand-filtration for sustainable urban wastewater management: A case study on street food cart effluents

This study develops and validates compact, data-driven models to predict the removal efficiency of key pollutants from street food cart wastewater using a combined sand-filtration and electrocoagulation (EC-SF) system. The principal novelty of this work lies in the creation of a unified, interpretable modeling framework that explicitly captures the synergistic interplay between hydraulic loading and electrochemical driving force, a critical gap for decentralized wastewater sources. Building on experimental data, generalized predictive equations were derived as functions of two operational variables: applied voltage (22–30 V) and flow rate (1–5 L·h⁻¹). For each key wastewater parameter (Chemical Oxygen Demand (COD), Total Suspended Solids (TSS), Fat, Oil and Grease (FOG), and Conductivity (COND)) the removal efficiency was found to have a consistent logarithmic dependence on flow rate. The coefficients of these logarithmic relationships were further expressed as second-order polynomial functions of the applied voltage, resulting in a unified, voltage-parameterized model for each pollutant. The final models demonstrated high predictive accuracy, with coefficients of determination (R²) of 0.98, 0.91, 0.99, and 0.99 for COD, TSS, FOG, and COND, respectively. Error statistics were consistently low, with Root Mean Square Error (RMSE) values ranging from 0.43 to 4.02 and Mean Absolute Error (MAE) from 0.34 to 2.78. The framework successfully translates laboratory results into interpretable equations, offering a practical tool for optimizing the EC-SF process. This work provides a scalable and systematic approach to designing decentralized hybrid electrocoagulation-filtration units, reducing reliance on extensive trial-and-error experimentation for sustainable urban wastewater management.