Abstract
Nowadays, acquiring a water supply for urban and industrial uses is one of the greatest challenges facing humanity for ensuring sustainability. Membrane technology has been considered cost-effective, encompasses lower energy requirements, and at the same time, offers acceptable performance. Electrospun nanofibrous membranes (ENMs) are considered a novel and promising strategy for the production of membranes that could be applied in several treatment processes, especially desalination and ion removal. In this study, we apply an unsupervised machine-learning strategy, the so-called principal component analysis (PCA), for the purpose of seeking discrepancies and similarities between different ENMs. The main purpose was to investigate the influence of membrane fabrication conditions, characteristics, and process conditions in order to seek the relevance of the application of different electrospun nanofibrous membranes (ENMs). Membranes were majorly classified into single polymers/layers, from one side, and dual multiple layer ENMs, from another side. For both classes, variables related to membrane fabrication conditions were not separated from membrane characterization variables. This reveals that membranes' characteristics not only depend on the chemical composition, but also on the fabrication conditions. On the other hand, the process conditions of ENM fabrication showed an extensive effect on membranes' performance.