A Feed-Forward Artificial Intelligence Pipeline for Sustainable Desalination under Climate Uncertainties: UAE Insights
This addresses sustainability challenges for desalination plants in the UAE, offering a decision-support system for climate-adaptive planning, though it is incremental as it applies existing AI methods to a specific domain.
The study tackled the problem of optimizing desalination performance in the UAE under climate uncertainties like aerosol optical depth (AOD), achieving 98% accuracy in predicting efficiency losses and proposing a control logic for adaptive management.
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.