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.

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 Cd, Cu, Pb, Zn, Ni and Co 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 (pH method, Box method and pH-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.

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 R values of 0.