Abba, S. I., Usman, J., Abdulazeez, I., Lawal, D. U., Baig, N., Usman, A. G., & Aljundi, I. H. (2023). Integrated Modeling of Hybrid Nanofiltration/Reverse Osmosis Desalination Plant Using Deep Learning-Based Crow Search Optimization Algorithm. Water, 15(19), 3515.

Researchers from Near East University’s Operational Research Centre in Healthcare, in collaboration with different universities, spearheaded a study delving into the transformative role of artificial intelligence (AI) in revolutionizing desalination processes to tackle global water scarcity challenges.

The study primarily focuses on the efficacy of the hybrid nanofiltration/reverse osmosis (NF–RO) process and introduces an innovative AI model termed LSTM-CSA, combining deep learning Long Short-Term Memory (LSTM) with the metaheuristic Crow Search Algorithm (CSA). This model was rigorously tested and validated, employing uncertainty assessments via Monte Carlo simulations and diverse performance criteria, such as root mean square error (RMSE) and mean absolute error (MAE).

The outcomes demonstrated the remarkable potential of AI models in optimizing energy usage, identifying energy-saving opportunities, and suggesting sustainable operating strategies. The LSTM-CSA model exhibited superior accuracy compared to LSTM, underscoring the added value of integrating CSA with deep learning for enhanced predictive capability. Innovative 2D graphical visualization techniques, including cumulative distribution function (CDF) and fan plot, provided comprehensive accuracy evaluations by considering various assessment indicators.

Beyond technical achievements, the study holds profound implications. AI emerges as a pivotal tool not only in improving desalination efficiency but also in steering the sector toward sustainability. By optimizing energy use, AI contributes significantly to SDGs, particularly SDG 6 (Clean Water and Sanitation) and SDG 13 (Climate Action). Moreover, AI-guided advancements in brine treatment techniques minimize waste, maximize resource utilization, and promote circular economy principles, aligning with SDG 12 (Responsible Consumption and Production).

This research underscores the imperative for innovative solutions in addressing water scarcity challenges and advancing sustainable development. AI’s transformative capabilities pave the way for more efficient and eco-friendly desalination processes, marking a significant stride towards achieving SDGs while fostering a sustainable and resilient future for global water resources.

 

For further details, access the original paper from the publisher’s link:

https://www.mdpi.com/2073-4441/15/19/3515