Machine Learning-Optimized Electroosmotic Regeneration in Composite Adsorbents

This study introduces a machine learning framework to predict electroosmotic properties and regeneration kinetics of composite adsorption materials. By integrating Support Vector Regression (SVR) with residual boosting, the model accurately estimates electric permeability coefficients (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>=0.92, MSE=0.0056) using voltage (4-16 V), potential gradient (0.8-3.2 V/cm), and pore structure parameters. Experimental validation highlights SVR's superiority over conventional algorithms (XGBRegressor, Random Forest), with feature analysis identifying pore distribution (42%) and ionic mobility (24%) as dominant factors. The framework reveals voltage-dependent saturation thresholds (>26 mA/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) and provides design guidelines for optimizing adsorbent regeneration efficiency (>70% moisture stability) and electrokinetic performance. This approach reduces reliance on costly experimentation, advancing sustainable material development for environmental applications.