Article

Advancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamics

Heyder MhohamdiDepartment of Computers Techniques Engineering, College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq. [email protected]Usama S. AltimariDepartment of Medical Laboratories Technology, AL-Nisour University College, Baghdad, IraqKrunal VaghelaDepartment of Computer Engineering, Faculty of Engineering & Technology, Marwadi University Research Center, Marwadi University, Rajkot, Gujarat, IndiaV VivekDepartment of Computer Science and Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, IndiaS. S. HotaDepartment of Computer Application, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, 751030, Odisha, IndiaDevendra SinghDepartment of Computer Science & Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, Uttarakhand, IndiaMahesh ManchandaCentre for Promotion of Research, Graphic Era Deemed to be University, Dehradun, Uttarakhand, 248002, IndiaShirin ShomurotovaDepartment of Chemistry Teaching Methods, Tashkent State Pedagogical University named after Nizami, Bunyodkor street 27, Tashkent, UzbekistanPrakhar TomarCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, IndiaMohammad Mahtab AlamDepartment of Basic Medical Sciences, College of Applied Medical Science, King Khalid University, Abha, 61421, Saudi Arabia

Scientific Reportsjournal2025en

ABI

Abstract

A combination of artificial intelligence (AI) and computational fluid dynamics was carried out to advance the modeling of adsorption separation processes. A comparative examination of three AI-based regression models including Gaussian Process Regression (GPR), Multi-layer Perceptron (MLP), and Polynomial Regression (PR) was carried out to predict chemical concentrations of solute in a dataset with two input variables (x and y) and one output feature (C in mol/m3). Employing gradient-based hyperparameter optimization, the results reveal that MLP outperforms GPR and PR with a significantly higher R2 score (MLP: 0.999, GPR: 0.966, PR: 0.980) and lower RMSE (MLP: 0.583, GPR: 3.022, PR: 2.370). Moreover, MLP demonstrates the lowest Average Absolute Relative Deviation (AARD%) at 2.564%, compared to GPR’s 18.733% and PR’s 11.327%. Five-fold cross-validation confirms MLP’s reliability (R² = 0.998 ± 0.001, RMSE = 0.590 ± 0.015). These findings underscore the practical utility of machine learning models, especially MLP, for accurate chemical concentration in environmental monitoring and process optimization with particular application for adsorption process.

Topics

Machine Learning and Data Classification Data Stream Mining Techniques Anomaly Detection Techniques and Applications

Identifiers

DOI: 10.1038/s41598-025-15538-z

Citations and references

Cited by 1 25 references

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