Mesosilicate materials for environmental applications: Adsorption separation analysis via hybrid computational and machine learning tools

Abstract

In this extensive analysis, we delve into the utilization of advanced hybrid model for molecular separation description via mesoporous materials. The process considered here is adsorption which is utilized for molecular separation in liquid solution. Data for adsorption are obtained in terms of solute concentration by solving mass transfer equations numerically. Then, the data is used for training and validation of several machine learning techniques to establish the hybrid model. For machine learning model, several methods are utilized including three robust tree-based ensemble models, namely Gradient Boosting (GB), Random Forest (RF), and Extra Trees (ET). The methos were utilized to tackle the challenging task of predicting chemical concentrations in a dataset comprising an array of over 19,000 data. These data points are characterized by input variables representing coordinates (x and y), while the output variable encapsulates chemical concentration (C). By subjecting these models to a meticulous optimization process, we achieved substantial enhancements in their predictive capabilities. RF emerged as the standout performer, boasting a remarkable R2 score of 0.99902. ET and GB also demonstrated commendable performance improvements, underscoring the versatility of tree-based ensemble models in delivering precise and reliable predictions for chemical concentrations within intricate and multifaceted datasets.

Keywords

• Adsorption
• Separation
• Computational fluid dynamics
• Mass transfer
• Gradient boosting
• Machine learning