Prediction of phosphate adsorption amount, capacity and kinetics via machine learning: A generally physical-based process and proposed strategy of using descriptive text messages to enrich datasets

[Display omitted] •The accurate prediction of phosphate adsorption amount, capacity and kinetics.•Descriptive text information input enriches dataset and delivers rapid prediction.•Physisorption remains the major contributor for phosphate adsorption. The use of machine learning to predict phosphate adsorption performance by specific adsorbent holds great promise due to its ability to save time and reveal underlying mechanisms. However, the small size of the dataset, caused by the insufficient detailed information, limits the ability to fully train the model and obtain accurate and generalizable results. To address this issue, we employ a fuzzing strategy that substitutes detailed numeric information with descriptive text messages on the physiochemical properties of adsorbents (such as using “Porous Carbon” to substitute the adsorbent with 100