Sustainable water desalination using eductor and waste heat: A review and suggestion for future research

Abstract

This review critically examines the dual utilization of waste heat and eductor systems, exploring the integration of eductor technology with low-grade waste heat in desalination processes, offering a transformative approach to enhancing energy efficiency, reducing operational costs, and minimizing environmental impact. The review explores the operational conditions, design modifications, current challenges, and potential applications of eductor-based desalination systems through case studies. The findings highlight the efficacy of eductors in reducing the footprint of thermal desalination units and their potential for large-scale freshwater production. Eductor-based systems create efficient vacuum conditions, significantly lowering the energy requirements of various desalination technologies, including Flash Evaporation Desalination (FED), Multi-Effect Distillation (MED), Adsorption Desalination (AD), Humidification-Dehumidification (HDH) Desalination, and Membrane Distillation (MD). Key findings demonstrate that eductor-based systems not only improve the performance and sustainability of these technologies but also offer a viable solution for utilizing industrial waste heat and renewable energy sources. The experimental study from the literature reveals that exergy destruction is directly proportional to the secondary mass flow rate. One challenge highlighted in the literature is minimizing exergy destruction while balancing the entrainment and pressure ratios inside the eductor. Another issue highlighted in the literature is the correlation between pressure oscillations' frequency and direct contact condensation's efficiency in the secondary flow. The review identifies gaps in current research and critical areas for future research, such as optimizing multi-nozzle ejector designs, developing dynamic auto-adjusting ejectors, and integrating advanced control strategies like artificial intelligence and machine learning.