Refinement of a kinetic adsorption model through Artificial Intelligence

The adsorption process involves capturing contaminants at active sites on adsorbent materials. Its economic efficiency and simplicity distinguish it as a preferred option amidst concerns about anthropogenic pollution. Traditional kinetic models present challenges in fitting nonlinear behaviors of porous materials, prompting the exploration of alternative approaches. Machine learning has a wide range of applications in various fields, including environmental engineering. These models possess generalization capabilities and are used as predictive tools, but they can lead to undesired behaviors if the system is complex and the model fails to capture this complexity. A novel methodology is proposed where the calibration of traditional models is studied using a machine learning model for adsorption kinetics, with hexavalent chromium as the adsorbate and activated carbon as the adsorbent. Furthermore, the technique of adding synthetic data to influence the model's capacity is studied, considering whether it leads to overfitting.Traditional models include pseudo first and second order kinetics, while multilayer perceptron is used as the machine learning model. The models obtained through the proposed methodology exhibit significant performance compared to traditional models. Additionally, an improved interpretability in their behavior compared to using only an artificial intelligence model is observed. Calibration ensures physical interpretation while enhancing generalization. The incorporation of synthetic data into the artificial intelligence model resulted in overfitting, subsequent ensemble methods effectively leveraged this, reducing bias related errors. The impact of synthetic data on calibration has demonstrated favorable outcomes.