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A machine learning technique for estimating the zeta potential of clay minerals under various brine conditions

Published online by Cambridge University Press:  17 October 2025

Yanqi Huang ,
Ayat Hussein Adhab ,
Vicky Jain ,
Abhinav Kumar ,
Roopashree R ,
Aditya Kashyap ,
Suman Saini ,
Pushpa Negi Bhakuni ,
Morug Salih Mahdi  and
Aseel Salah Mansoor
...Show all authors
Yanqi Huang
Affiliation:
Henan Sixth Geological Brigade Co., Ltd , Zhengzhou Henan, 450000, China
Ayat Hussein Adhab*
Affiliation:
Department of Pharmacy, Al-Zahrawi University College , Karbala, Iraq
Vicky Jain*
Affiliation:
Marwadi University Research Center, Department of Chemistry, Faculty of Science, Marwadi University , Rajkot 360003, Gujarat, India
Abhinav Kumar*
Affiliation:
A Sharda School of Engineering and Sciences, Sharda University, Knowledge Park III, Greater Noida, Uttar Pradesh-201310, India. B Department of Mechanical Engineering, Karpagam Academy of Higher Education, Coimbatore 641021, India
Roopashree R
Affiliation:
Department of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University) , Bangalore, Karnataka, India
Aditya Kashyap
Affiliation:
Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University , Rajpura, 140401, Punjab, India
Suman Saini
Affiliation:
Department of Chemistry, Chandigarh Engineering College, Chandigarh Group of Colleges-Jhanjeri , Mohali1 40307, Punjab, India
Pushpa Negi Bhakuni
Affiliation:
A Department of Allied Science, Graphic Era Hill University , Bhimtal, India B Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
Morug Salih Mahdi
Affiliation:
College of MLT, Ahl Al Bayt University , Iraq
Aseel Salah Mansoor
Affiliation:
Gilgamesh Ahliya University , Baghdad, Iraq
Usama Kadem Radi
Affiliation:
College of Pharmacy, National University of Science and Technology , Dhi Qar, 64001, Iraq
Nasr Saadoun Abd
Affiliation:
Medical technical college, Al-Farahidi University , Iraq
Mamurakhon Toshpulatova
Affiliation:
Department of Mathematics and Teaching Methods in Primary Education, Tashkent State Pedagogical University, Bunyodkor street 27, Tashkent, Uzbekistan
Mehrdad Mottaghi
Affiliation:
Faculty of Chemistry, Kabul University , Kabul, Afghanistan
*
Corresponding authors: Mehrdad Mottaghi, Yanqi Huang, and Vicky Jain; Emails: mmottaghi41@gmail.com; hyq19950817@163.com; vicky.jain@marwadieducation.edu.in
Corresponding authors: Mehrdad Mottaghi, Yanqi Huang, and Vicky Jain; Emails: mmottaghi41@gmail.com; hyq19950817@163.com; vicky.jain@marwadieducation.edu.in
Corresponding authors: Mehrdad Mottaghi, Yanqi Huang, and Vicky Jain; Emails: mmottaghi41@gmail.com; hyq19950817@163.com; vicky.jain@marwadieducation.edu.in
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Abstract

Zeta potential plays a crucial role in determining the wettability and stability of clay particles in porous media, impacting their behavior when interacting with fluids. The present study aimed to address the problem of accurate estimation of zeta potential values for diverse clay particles within various brine samples using advanced machine learning techniques. Methods including decision tree, random forest, adaptive boosting, K-nearest neighbors, convolutional neural networks, and ensemble learning were employed to predict zeta potential based on input parameters such as clay type (kaolinite, chlorite, illite, and smectite), total dissolved solids, pH, and ionic strength. The leverage method was used to identify outliers within the dataset, while a sensitivity analysis quantified the influence of input factors. The training process employed k-fold cross-validation to minimize overfitting. Results revealed adaptive boosting as the most effective approach, achieving high prediction accuracy and minimal error values. Sensitivity analysis identified pH as the dominant factor reducing zeta potential magnitude, while ionic strength and total dissolved solids enhanced zeta potential. The findings contribute significantly to understanding clay–fluid interactions and provide a robust computational framework for industrial applications.

Keywords

clay particlesionic strengthmachine learningoutlier detectionsensitivity analysiszeta potential

Information

Type
Original Paper
Information
Clays and Clay Minerals , Volume 73 , 2025 , e33
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of The Clay Minerals Society

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