Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid surface complexation-machine learning model.

This study aimed to develop surface complexation modeling-machine learning (SCM-ML) hybrid model for chromate and arsenate adsorption on goethite. The feasibility of two SCM-ML hybrid modeling approaches was investigated. Firstly, we attempted to utilize ML algorithms and establish the parameter model, to link factors influencing the adsorption amount of oxyanions with optimized surface complexation constants. However, the results revealed the optimized chromate or arsenate surface complexation constants might fall into local extrema, making it unable to establish a reasonable mapping relationship between adsorption conditions and surface complexation constants by ML algorithms. In contrast, species-informed models were successfully obtained, by incorporating the surface species information calculated from the unoptimized SCM with the adsorption condition as input features. Compared with the optimized SCM, the species-informed model could make more accurate predictions on pH edges, isotherms, and kinetic data for various input conditions (for chromate: root mean square error (RMSE) on test set = 5.90 %; for arsenate: RMSE on test set = 4.84 %). Furthermore, the utilization of the interpretable formula based on Local Interpretable Model-Agnostic Explanations (LIME) enabled the species-informed model to provide surface species information like SCM. The species-informed SCM-ML hybrid modeling method proposed in this study has great practicality and application potential, and is expected to become a new paradigm in surface adsorption model.

Prediction of adsorption of metal cations by clay minerals using machine learning.

Adsorption of heavy metals by clay minerals occurs widely at the solid-liquid interface in natural environments, and in this paper, the phenomenon of adsorption of Cd2+, Cu2+, Pb2+, Zn2+, Ni2+ and Co2+ by montmorillonite, kaolinite and illite was simulated using machine learning. We firstly used six machine learning models including Random Forest(R), Extremely Forest(E), Gradient Boosting Decision Tree(G), Extreme Gradient Boosting(X), Light Gradient Boosting(LGB) and Category Boosting(CAT) to feature engineer the metal cations and the parameters of the minerals, and based on the feature engineering results, we determined the first order hydrolysis constant(log K), solubility product constant(SPC), and higher hydrolysis constant (HHC) as the descriptors of the metal cations, and site density(SD) and cation exchange capacity(CEC) as the descriptors of the clay minerals. After comparing the predictive effects of different data cleaning methods (pH50 method, Box method and pH50-Box method) and six model combinations, it was finally concluded that the best simulation results could be achieved by using the pH 50-Box method for data cleaning and Extreme Gradient Boosting for modelling (RMSE = 4.158 %, R2 = 0.977). Finally, model interpretation was carried out using Shapley explanation plot (SHAP) and partial dependence plot(PDP) to analyse the potential connection between each input variable and the output results. This study combines machine learning with geochemical analysis of the mechanism of heavy metal adsorption by clay minerals, which provides a different research perspective from the traditional surface complexation model.

Prediction of heavy metal removal performance of sulfate-reducing bacteria using machine learning.

A robust modeling approach for predicting heavy metal removal by sulfate-reducing bacteria (SRB) is currently missing. In this study, four machine learning models were constructed and compared to predict the removal of Cd, Cu, Pb, and Zn as individual ions by SRB. The CatBoost model exhibited the best predictive performance across the four subsets, achieving R2 values of 0.83, 0.91, 0.92, and 0.83 for the Cd, Cu, Pb, and Zn models, respectively. Feature analysis revealed that temperature, pH, sulfate concentration, and C/S (the mass ratio of chemical oxygen demand to sulfate) had significant impacts on the outcomes. These features exhibited the most effective metal removal at 35 °C and sulfate concentrations of 1000-1200 mg/L, with variations observed in pH and C/S ratios. This study introduced a new modeling approach for predicting the treatment of metal-containing wastewater by SRB, offering guidance for optimizing operational parameters in the biological sulfidogenic process.

Using machine learning to explore oxyanion adsorption ability of goethite with different specific surface area.

In this study, we developed prediction models for the adsorption of divalent and trivalent oxyanions on goethite based on machine learning algorithms. After verifying the reliability of the models, the importance of goethite specific surface area (SSA) and the average oxyanion adsorption capacities of goethite with different SSAs were calculated by shapley additive explanations (SHAP) importance analysis and partial dependence (PD) analysis. Despite there were differences in the feature importance of divalent and trivalent oxyanions, the contribution of goethite's SSA to the adsorption amount ranked the fourth based on SHAP importance, indicating SSA played the important role in oxyanion adsorption. Meanwhile, the PD values of SSA and the optimized complexation constants from surface complexation modeling (SCM) both indicated a non-monotonic relationship between the goethite with different SSA and its oxyanions binding capacity. When the total site concentration and crystal face composition were used as the machine learning model input features, the SHAP importance values of crystal faces and the PD decomposition results indicated that the (001) face showed the crucial influence on oxyanions adsorption amount. These findings demonstrated the important role of crystal face composition in goethite's adsorption ability, and provided a theoretical explanation for the variations of oxyanions adsorption amount on different SSA goethite.