A Topic Modeling Approach to Discover the Global and Local Subjects in Membrane Distillation Separation Process

Abstract

Membrane distillation (MD) is proposed as an environmentally friendly technology of emerging interest able to aid in the resolution of the worldwide water issue and brine processing by producing distilled water and treating high-saline solutions up to their saturation with a view toward reaching zero liquid discharge (ZLD) at relatively low temperature requirements and a low operating hydrostatic pressure. Topic modeling (TM), which is a Machine Learning (ML) method combined with Natural Language Processing (NLP), is a customizable approach that is ideal for researching massive datasets with unknown themes. In this study, we used BERTopic, a new cutting-edge Python library for topic modeling, to explore the global and local themes in the MD separation literature. By using the BERTopic model, the words describing the collected dataset were detected together with over- and underexplored research topics to guide MD researchers in planning their future works. The results indicated that two global themes are widely discussed and are relevant to MD scientists abroad. In brief, these topics are permeate flux, heat-energy recovery, surface modification, and polyvinylidene fluoride hydrophobic membranes. BERTopic discovered 62 local concepts. The most researched local topics were solar applications, membrane scaling, and electrospun membranes, while the least investigated were boron removal, dairy effluent applications, and nickel wastewater treatment. In addition, the topics were illustrated in a 2D plane to better understand the obtained results.