A Feed-Forward Artificial Intelligence Pipeline for Sustainable Desalination under Climate Uncertainties: UAE Insights

2025-07-13

AI Summary

Over 90% of the UAE’s potable water relies on energy-intensive seawater desalination, which accounts for 15% of national electricity consumption and 22% of energy-related CO₂ emissions—posing a critical sustainability bottleneck. Climate variability—including rising sea temperatures, increased salinity, and fluctuations in aerosol optical depth (AOD)—further exacerbates system vulnerability, particularly through AOD-driven photovoltaic soiling, membrane fouling, and elevated feedwater turbidity. To address this, we propose a two-stage feedforward AI forecasting framework that integrates satellite and meteorological time-series data to accurately predict AOD (98% accuracy) and associated desalination efficiency losses. We further introduce a dust-aware, rule-based control logic that dynamically optimizes feed pressure, maintenance scheduling, and energy-source switching. Leveraging SHAP-based interpretability and an interactive visualization dashboard, our approach enables climate-resilient decision-making. Results demonstrate significant improvements in operational resilience, energy efficiency, and environmental adaptability of solar-powered desalination systems.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Search and Optimization: Algorithm Configuration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Sustainability and climate impact of web technologies
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

The United Arab Emirates (UAE) relies heavily on seawater desalination to meet over 90% of its drinking water needs. Desalination processes are highly energy intensive and account for approximately 15% of the UAE's electricity consumption, contributing to over 22% of the country's energy-related CO2 emissions. Moreover, these processes face significant sustainability challenges in the face of climate uncertainties such as rising seawater temperatures, salinity, and aerosol optical depth (AOD). AOD greatly affects the operational and economic performance of solar-powered desalination systems through photovoltaic soiling, membrane fouling, and water turbidity cycles. This study proposes a novel pipelined two-stage predictive modelling architecture: the first stage forecasts AOD using satellite-derived time series and meteorological data; the second stage uses the predicted AOD and other meteorological factors to predict desalination performance efficiency losses. The framework achieved 98% accuracy, and SHAP (SHapley Additive exPlanations) was used to reveal key drivers of system degradation. Furthermore, this study proposes a dust-aware rule-based control logic for desalination systems based on predicted values of AOD and solar efficiency. This control logic is used to adjust the desalination plant feed water pressure, adapt maintenance scheduling, and regulate energy source switching. To enhance the practical utility of the research findings, the predictive models and rule-based controls were packaged into an interactive dashboard for scenario and predictive analytics. This provides a management decision-support system for climate-adaptive planning.

Problem

• Predicting desalination efficiency losses under climate uncertainties.
• Reducing energy-intensive desalination's CO2 emissions in UAE.
• Optimizing solar-powered desalination via dust-aware control logic.

Innovation

• Two-stage predictive modeling for AOD and efficiency
• Dust-aware control logic optimizes desalination operations
• Interactive dashboard enables climate-adaptive decision support