Prediction of Phosphate Adsorption Amount and Capacity from Water via Machine Learning
The use of machine learning to train data that collected from the existed database for constructing the predictive framework can accurately and swiftly predict the adsorption performance on phosphate,avoid time-consuming process.In this study,8 physiochemical features of adsorbents and 4 environmental features are collected to construct the predictive model for the prediction of adsorption amount of phosphate and adsorption capacity of the adsorbent.The trained model accurately predicts the adsorption amount and capacity with the maximum R2 of 0.997 and 0.999,and the minimum RMSE and MAE of 0.001,respectively.The adsorption amount is primarily subject to the physical features of adsorbents and the environmental features,while the adsorption capacity is mainly controlled by the physiochemical features of adsorbents.Compared with the current opinion that the adsorption of phosphate is primarily delivered by chemisorption,this study finds that physisorption is the primary contributor to the adsorption mechanism based on the interpretation of the trained model.And thus,the promotion of adsorption amount depends on the improvement of adsorption environment,while the promotion of adsorption capacity should pay more attention on the modification of physical properties of adsorbents.The simplified models with the most influ-ential features as the inputs also deliver the accurate prediction especially for small values of adsorption amount and capacity with the maximum R2 of 0.970 and 0.974 for amount and capacity,respectively.This study develops and simplifies the predic-tive frameworks for adsorption amount and capacity,and the importance of different features are sorted and evaluated,which enlarges the knowledge on the way to quickly predict phosphate adsorption.
万方数据
维普
