Surfactant representation using COSMO screened charge density for adsorption isotherm prediction using Physics-Informed Neural Network (PINN).

Predicting surfactant adsorption using the currently available isotherm model is limited to one or two independent variables: equilibrium concentration and temperature. This study aims to develop an adsorption model that includes molecular features, testing conditions, and solid properties in the model. A Physics-Informed Neural Network (PINN) was structured by integrating adsorption isotherm into artificial neural networks (ANN). The model was trained using a dataset containing 56 adsorption isotherms and 20 types of anionic and nonionic surfactants under various conditions with sand and silica oxide as their solids. The surfactants were quantified using sets of descriptors generated from molecular counting, charge distribution, and Conductor-like Screening Model (COSMO) screened charge density. The COSMO-screened charge density descriptors provide the highest accuracy in representing the surfactant molecule. The interpretation of molecular structure effect and surfactant-solid interaction described using COSMO-screened charge density showed that adsorption between the surfactant and solid media involves hydrogen bonding and hydrophobic interaction. The PINN model achieves high accuracy with 93% training and 85% validation with fivefold cross-validation. Later, the model was evaluated and used to generate an adsorption isotherm and predict unseen surfactant adsorption. Adsorption prediction with unseen surfactants showed high accuracy with the surfactant for familiar structure (RMSE 0.07 mg/g) and promising profile for the whole new structure (RMSE 2.95 mg/g). Scientific contribution This study advances the field by integrating COSMO-screened charge density descriptors into a physics-informed deep learning model to predict surfactant adsorption isotherms, accounting for molecular features, testing conditions, and solid properties. The incorporation of COSMO-screened charge density offers a novel approach to accurately represent surfactant molecules, enabling accurate prediction of their adsorption behavior. This approach extends conventional models, which are often limited to empirical parameters or fewer variables. This physics-informed framework significantly enhances the understanding of surfactant-solid interactions and offers a robust predictive tool for optimizing surfactant formulations, aiming to minimize adsorption losses in chemical enhanced oil recovery and environmental remediation.