Graphene Oxide-Enhanced Reverse Osmosis with Deep Learning Based Water Quality Prediction

Water salinity, as one of the major environmental problems, turns water into a non-potable one and threatens business and agriculture. This work examines the properties of saline water and employs computational approaches to evaluate various salt concentrations, aiming to enhance the system's resilience. By the fusion of membrane technology with the reverse osmosis (RO) process, the study intends to improve desalination efficiency. GO membranes can be a potential alternative to conventional RO membranes in water purification as they possess higher permeability and better salt rejection. A deep learning architecture is incorporated to enhance the desalination process further for real-time salinity monitoring. At the same time, Kernel Principal Component Analysis (Kernel PCA) is employed for accurate feature extraction, and a Stacked Dilated Convolutional Neural Network (CNN) is used to ensure precise water purity predictions. The experimental results through the integrated system produce 97% pure water that enables precise salinity measurement and continuous monitoring. With the help of cutting-edge membrane technology and artificial intelligence, this research suggests an eco-friendly and scalable desalination process. The paper unveils how GO-based RO membranes and deep learning techniques can act as enablers for water purification systems to not only improve their efficacy but also flexibility in dealing with varying salinity levels.