Cd adsorption prediction of Fe mono/composite modified biochar based on machine learning: Application for controllable preparation

Abstract

In this study, artificial neural network (ANN) and random forest (RF) were constructed to predict the Cd adsorption capacity of Fe-modified biochar. The RF model outperformed ANN model in accuracy and predictive performance (R2 = 0.98). Through the contribution factors analysis of SHAP, structural characteristics (55.44%) were most important of Fe composite-modified biochar (CBC). And CBC have the best adsorption performance when C, Fe, O, H, N, and pH content were <50%, 10–20%, 10–20%, 0.5–1%, 0–2%, and >10, respectively. The Fe-Ca modified biochar (FeCa-BC) of different raw materials (wheat straw, corn straw and walnut shell) were successfully prepared according to the ML results, and the experimental data of FeCa-BC verified the accurate predictive ability of RF model (R2 = 0.89). The developed GUI toolbox results showed that the error between predicted and actual values was less than 5% based on the training set, testing set, and experimental validation set. The analysis of FTIR, XRD and XPS indicated that surface complexation, precipitation, and ion exchange were the main Cd adsorption mechanisms of FeCa-BC. This work presents new insights for the targeted preparation of functional biochar and its application in contaminated water through ML.