Мақола

Advanced hybrid evaluation of water treatment using porous materials for adsorption separation via machine learning and mechanistic models

Pan ZhangSchool of Mechanical Engineering, Sichuan University Jinjiang College, Meishan, Sichuan, 620860, ChinaUsama S. AltimariDepartment of Medical Laboratories Technology, AL-Nisour University College, Baghdad, IraqKrunal VaghelaMarwadi University Research Center, Department of Computer Engineering, Faculty of Engineering & Technology, 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, Odisha-751030, 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 ArabiaHeyder MhohamdiDepartment of computers Techniques engineering, College of technical engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq

Case Studies in Thermal Engineeringjournal2025en

ABI

Аннотация

This study presents an advanced hybrid evaluation approach for predicting chemical concentration (C) in adsorption-based water treatment processes using a combination of tree-based machine learning models and Massively Parallel Hyperparameter Tuning. The objective is to accurately model the nonlinear relationships between spatial input parameters (x and y) and concentration outputs within a complex porous-material system. Three ensemble learning algorithms—Random Forest (RF), Gradient Boosting (GB), and Extra Trees (ET)—were systematically optimized and assessed to determine their suitability for high-precision concentration prediction. The tuning framework enabled extensive exploration of hyperparameter space, significantly enhancing model performance. Among the tested models, Extra Trees (ET) demonstrated outstanding predictive capability, achieving an R 2 value of 0.99924, along with the lowest MAPE (2.52675E-02) and MAE (5.59418E-03). These metrics confirm the ET model’s exceptional ability to capture subtle nonlinear trends and complex interactions inherent to adsorption-driven systems. In comparison, RF and GB also achieved strong performance but fell short of ET in both accuracy and robustness for the data analysis. The results highlight the effectiveness of parallelized hyperparameter optimization in improving predictive reliability for chemically intricate processes. This work underscores the value of machine-learning-assisted modeling for water treatment applications and provides a scalable framework that can support process design, operational decision-making, and further mechanistic integration in environmental and chemical engineering.

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Мавзулар

Hydrological Forecasting Using AI Machine Learning in Materials Science Wastewater Treatment and Nitrogen Removal

Идентификаторлар

DOI: 10.1016/j.csite.2025.107561

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