A deep learning neural network approach for predicting the factors influencing heavy-metal adsorption by clay minerals

Abstract The treatment of water containing heavy metals has attracted increasing attention because the ingestion of such water poses risks to human health. Due to their relatively large specific surface areas and surface charges, clay minerals play a significant role in the adsorption of heavy metals in water. However, the major factors that influence the adsorption rates of clay minerals are not well understood, and thus methods to predict the sorption of heavy metals by clay minerals are lacking. A method that can identify the most appropriate clay minerals for removal of a given heavy metal, based on the predicted sorption of the clay minerals, is required. This paper presents a widely applicable deep learning neural network approach that yielded excellent predictions of the influence of the sorption ratio on the adsorption of heavy metals by clay minerals. The neural network model was based on datasets of heavy-metal parameters that are available generally. It yielded highly accurate predictions of the adsorption rate based on training data from the dataset and was able to account for a wide range of input parameters. A Pearson sensitivity analysis was used to determine the contributions of individual input parameters to the adsorption rates predicted by the neural network. This newly developed method can predict the major factors influencing heavy-metal adsorption rates. The model described here could be applied in a wide range of scenarios.