Abstract
Principal Component Analysis (PCA) serves as a valuable tool for analyzing membrane processes, offering insights into complex datasets, identifying crucial factors influencing membrane performance, aiding in design and optimization, and facilitating monitoring and fault diagnosis. In this study, PCA is applied to understand operational features affecting pervaporation desalination performance of PVA-based TFC membranes. PCA-biplot representation reveals that the first two principal components (PCs) accounted for 62.34% of the total variance, with normalized permeation with selective layer thickness (Pnorm), water permeation flux (P), and operational temperature (T) contributing significantly to PC1, while salt rejection dominates PC2. Membrane clustering indicates distinct influences, with membranes grouped based on correlation with operational factors. Excluding outliers increases total variance to 74.15%, showing altered membrane arrangements. Interestingly, the adopted strategy showed a high discrepancy between P and Pnorm, indicating the relevance of comparing between PVA membranes with specific layers and those with none. PCA results showed that Pnorm is more important than P in operational features, highlighting its significance in both research and practical applications. Our findings show that even know P remains a key performance property; Pnorm is critical for developing high-performance, efficient, and economically viable pervaporation desalination membranes. Subsequent PCA for membranes without specific layers (M1 to M6) and with specific layers (M7 to M11) highlights higher total variance and influence of variables, aiding in understanding membranes’ behavior and suitability under different conditions. Overall, PCA effectively delineates performance characteristics and potential applications of PVA-based TFC membranes. This study would confirm the applicability of the PCA approach in monitoring the operational efficiency of pervaporation desalination via these membranes.