This final year thesis focuses on the application of artificial intelligence for optimizing energy consumption in a reverse osmosis desalination plant. In recent years, the world has faced a major challenge : water stress. As water is essential to human life, desalination plants have been developed to address this shortage. However, this approach has raised significant concern within the scientific community : high energy consumption, which accounts for approximately 53% of desalination plant operating costs. To address this issue, the integration of artificial intelligence into these plants has been considered. The main objective of this work is to develop and validate an AI model capable of optimizing the energy consumption of a seawater desalination plant, while maintaining the quality of the water produced and ensuring system stability.

The adopted methodology is based on an operational diagnosis of the desalination plant (process analysis, data preservation, identification of energy inefficiencies), the crea tion of an industrial dataset (data collection and cleaning), realistic AI modeling (real-time energy consumption prediction, training of the AI model to predict the optimal confi guration of the energy recovery device "ERD"), energy optimization through artificial intelligence (simulation of realistic scenarios, reduction of energy consumption and opti mization of operations), and making AI available to engineers through a smart dashboard (automatic real-time optimization, integration of predictive alarms).

The results obtained are based on four main axes : first, the reduction of energy inten sity of water production by 10 to 15 percent through AI optimization of pump variable speed drives and energy recovery devices (ERD) based on physicochemical fluctuations of seawater. Second, the development of high-fidelity predictive models limiting energy demand forecasting error to less than 5 percent and characterizing complex correlations between water quality parameters and membrane fouling for enhanced preventive main tenance. Third, the deployment of early warning systems capable of identifying critical failures with a 48 to 72-hour horizon, enabling the transition from a systematic preventive maintenance scheme to condition-based maintenance to eliminate unplanned downtime. Finally, the implementation of a digital twin interfaced with a smart dashboard, allowing real-time comparison of actual performance indicators with optimal setpoints generated by the AI while automating operational adjustment recommendations.

This work highlights the transformative role of artificial intelligence in the reverse osmosis seawater desalination sector and opens perspectives for the digitalization of de salination plants.