Semester
Summer
Date of Graduation
2025
Document Type
Problem/Project Report
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Chemical and Biomedical Engineering
Committee Chair
Yuhe Tian
Committee Member
Yuxin Wang
Committee Member
Oishi Sanyal
Abstract
Abstract
Membrane fouling is a persistent and critical challenge in membrane-based separation processes, resulting in reduced performance, higher energy consumption, and increased operational and maintenance costs. Addressing this issue is essential for improving the long-term sustainability and efficiency of membrane systems. This report provides a comprehensive overview of membrane fouling mechanisms—such as organic, inorganic, particulate, and biofouling—and explores the growing role of Artificial Intelligence (AI) in tackling this problem. Specifically, it reviews three widely adopted AI techniques: Artificial Neural Networks (ANN), Fuzzy Logic (FL), and Support Vector Machines (SVM). Each method is examined through a series of literature-based case studies, focusing on model structure, input parameters, data preprocessing strategies, and prediction performance.
The analysis highlights the unique strengths and limitations of each AI approach. ANN models are effective at capturing complex, nonlinear relationships in large datasets but may suffer from overfitting and require careful parameter tuning. FL offers interpretable rule-based systems and is well-suited for integrating expert knowledge, while SVM demonstrates superior performance in handling small and noisy datasets due to its robust margin-based optimization. The report also discusses how hybrid methods and the integration of fouling mechanism knowledge into AI models can further improve prediction accuracy and practical applicability.
Ultimately, this study shows that AI-based approaches have significant potential to support early fouling detection, optimize cleaning strategies, and reduce chemical consumption. The findings not only validate the effectiveness of AI in enhancing membrane fouling prediction but also suggest promising directions for future research, including real-time monitoring, hybrid modeling, and the incorporation of physical-chemical mechanisms into data-driven frameworks.
Recommended Citation
Jiang, Yazhou, "Artificial Intelligence Approaches for Membrane Fouling Prediction" (2025). Graduate Theses, Dissertations, and Problem Reports. 12995.
https://researchrepository.wvu.edu/etd/12995