Integrated hybrid modeling and SHAP (SHapley Additive exPlanations) to predict and explain the adsorption properties of thermoplastic polyurethane (TPU) porous materials.

As a novel type of oil-water separation material, thermoplastic polyurethane (TPU) porous material exhibits many excellent properties such as low density, high specific surface area, and outstanding oil-water separation performance. However, the performance of thermoplastic polyurethane (TPU) porous materials is often impeded by various factors, and conducting numerous experiments to investigate the relationship between these factors and the adsorption performance can be both expensive and time-consuming. As an alternative to these experiments, machine learning (ML) techniques can be used to estimate experimental results.