Optimizing Liquid Membrane Technologies in Industry through AI and Machine Learning-Based Performance Prediction

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) has emerged as a transformative approach to predicting and optimizing the performance of liquid membranes in industrial applications. Liquid membranes, including Supported Liquid Membranes (SLMs), Emulsion Liquid Membranes (ELMs), and Polymer Inclusion Membranes (PIMs), play a critical role in processes such as wastewater treatment, metal ion separation, and pharmaceutical purification. This review explores the latest advancements in combining AI/ML with liquid membrane technology, focusing on predictive modeling, optimization techniques, and real-world applications. Key methodologies discussed include data collection, preprocessing, model development, and validation strategies. Through case studies, the review highlights how AI-driven approaches, such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Genetic Algorithms (GAs), enhance membrane performance by improving efficiency, selectivity, and operational stability. Despite challenges like data limitations and integration complexities, AI and ML present promising prospects for advancing membrane-based technologies, contributing to more efficient, sustainable, and cost-effective industrial processes.