Abstract

Freshwater scarcity is a critical global issue, especially in remote areas or areas impacted by natural disasters. Water desalination is one of the most effective methods to provide fresh water where it is needed. However, large-scale desalination plants cannot provide fresh water in the case of a natural disaster far from the plant's location. To address this, portable desalination units powered by renewable energy sources can be developed to supply potable water and power as a by-product for emergency situations. This work explored the application of different desalination methods, including reverse osmosis (RO) and electrodialysis (ED), in conjunction with renewable energy sources within a portable system. The aim was to improve both the environmental sustainability and operational efficiency of a portable desalination unit while providing fresh water and energy where and when needed. This work consisted of three phases. Phase I aimed to improve the efficiency of a first-generation solar-powered, RO-based portable desalination unit. Phase II focused on testing and implementing Artificial Intelligence (AI) tools to determine the best operational parameters of the desalination unit under different feed salinity and intermittent solar power. Furthermore, this phase compared the effectiveness of ANN and ANFIS for unit modeling and optimization. Finally, Phase III aimed to investigate the potential of ED technologies for portable desalination applications. To achieve this goal, a second portable desalination unit was built focusing on ED desalination. The ED unit was tested experimentally at different feed flow rates and power to assess the unit’s efficiency under different operational conditions.

Date of publication

2024

Document Type

Thesis

Language

english

Persistent identifier

http://hdl.handle.net/10950/4743

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