Optimized Bi-LSTM Networks for Modeling Ni(II) Biosorption Kinetics on Quercus crassipes Acorn Shells

Heavy metal pollution from anthropogenic sources poses significant risks to ecosystems and human health. Biosorption offers a sustainable removal method, but kinetics are poorly captured by traditional neural networks. This study introduces optimized Bidirectional Long Short-Term Memory (Bi-LSTM) networks for multivariate modeling of Ni(II) biosorption on Quercus crassipes acorn shells, trained using experimental (EKD), synthetic (SKD), and combined (CKD) datasets. A two-stage hyperparameter optimization with Optuna yielded models with R2 above 0.995 and low RMSE in 5-fold cross-validation. Second-stage models showed high stability, with coefficient of variation (CoV) values below 10% for RMSE. Based on unseen kinetics, production models showed slightly lower performance (R2 = 0.89–0.996): EKD1, EKD2, and CKD1 showed the most consistent performance across challenging conditions with R2 values of 0.9617, 0.9769, and 0.9415, respectively; SKD models achieved strong results under standard conditions (kinetic 1, SKD1 R2 = 0.9963). SHapley Additive exPlanations (SHAP) analysis identified contact time and initial Ni(II) concentration as key variables, with temperature, cation charge, and a salt interference code also contributing to model interpretability. These findings demonstrate that optimized Bi-LSTM networks offer a robust and interpretable data-driven solution for modeling Ni(II) removal under multivariate conditions, including the presence of salts.