AI-Driven Environmental Sustainability Assessment of Desalination Technologies via Automated Literature Mining

Abstract

The growing global need for freshwater has led to greater dependency on seawater desalination. This field has often been criticized for its high energy use and environmental concerns. Various desalination methods have been developed, including membrane, thermal and hybrid systems; however, their environmental impacts differ from situation to situation. This paper puts forward a framework for the first time utilizing Artificial Intelligence (AI) to analyze to evaluate and classify the various methods of desalination technologically using vast amounts of scientific literature. With the use of Natural Language Processing (NLP), machine learning, and automated data mining, the framework captures the main operational parameters, energy consumption, and environmental consequences within over twenty years of research. The data are then subjected to AI-aided multi-criteria decision-making to evaluate each technique and classify it by its environmental sustainability. The findings prove, i.e. on the highly heterogeneous and heavily biased environmental data that AI improves the precision, efficiency, and neutrality of environmental assessments. This research provides a template and a basis for extensive automated Artificial Intelligence; it also improves the efficiency of environmental assessment and optimization of desalination systems.