Water remediation using sustainable kaolin-derived zeolite and machine learning-guided prediction of adsorption...

A chemistry-informed machine learning (CIML) model was developed for the prediction of equilibrium concentration (Ce) and adsorption capacity (Qe) of synthesized sustainable and cost-effective zeolite 4 A from kaolin clay. In this framework, the chemistry-informed integrates fundamental adsorption principles, including mass balance, kinetic descriptors (mass transfer dynamics), equilibrium relationship (saturation behaviour), stoichiometry consistency and dimensionless loading, into a learning architecture. The materials are thoroughly characterized using FT-IR, FE-SEM, XRD, and BET. In the experimental study, 1 g of zeolite was utilized to treat 10 L of dye effluent through column adsorption demonstrating the material's practical applicability for large volume wastewater treatment. The breakthrough curve exhibited sigmoidal profile, with Thomas model yielding an adsorption capacity of 56.6 mg/g. Extensive experimental data of 200 points under varying conditions supported machine learning simulations. Bayesian optimization identified the Gaussian process as the optimal model. The proposed CIML framework integrates the GP prediction (R² = 0.9979 for Ce and 0.9993 for Qe) with mass balance formulation, error propagation, residual correction stage and uncertainty quantification. To further access generalizability, external validation is performed using 63 independent data points under varying conditions. CIML performance is benchmarked against, conventional mass balance model, predicting Qe directly from Ce using mass balance equation and a two stage GP model lacking physical constraints. The results show that the proposed CIML framework achieves R² = 0.9993 and RMSE = 0.0493, outperforming both the mass balance model (R² = 0.9974 and RMSE = 0.0973) and the two stage GP model (R² = 0.9973, RMSE = 0.0938) corresponding to an approximate 47% reduction in Qe prediction error. Multi-layer validation confirmed robustness, while uncertainty quantification showed 96.8% coverage. Interpretability analysis identified the key influential variables. The results demonstrates that the CIML enhances accuracy, generalizability, and reliability for wastewater treatment applications.