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Deep Reinforcement Learning Framework for Dynamic Operational Optimization of Solar-Powered Seawater Desalination Systems.

Lima Hongou
Volume 5

Abstract

 

Solar-powered seawater desalination systems offer a sustainable solution for freshwater production in water-scarce regions by combining renewable energy generation with desalination technologies. However, their operation is strongly affected by the intermittent nature of solar energy, variable seawater quality, fluctuating water demand, changing weather conditions, energy storage limitations, and the nonlinear behavior of desalination processes. Conventional rule-based and static optimization strategies often fail to adapt effectively to these dynamic operating conditions, leading to increased energy consumption, unstable water production, reduced system efficiency, and suboptimal utilization of available solar power. To address these challenges, this paper proposes a Deep Reinforcement Learning framework for dynamic operational optimization of solar-powered seawater desalination systems.

The proposed framework formulates desalination plant operation as a sequential decision-making problem, in which an intelligent agent continuously interacts with the desalination-energy environment to learn optimal control policies. The system state includes solar irradiance, photovoltaic power output, battery state of charge, seawater temperature, feed salinity, water demand, permeate flow rate, recovery ratio, membrane pressure, specific energy consumption, storage tank level, and historical operating conditions. Based on these observations, the reinforcement learning agent selects adaptive operational actions such as adjusting desalination production rate, controlling pump operation, regulating membrane pressure, managing battery charging and discharging, scheduling water storage, and allocating solar energy between direct plant operation and energy storage. The reward function is designed to maximize freshwater production and solar energy utilization while minimizing energy consumption, operating cost, membrane stress, battery degradation, water shortage, and renewable energy curtailment.

Advanced deep reinforcement learning algorithms, including Deep Q-Network, Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Soft Actor–Critic, are investigated to support both discrete and continuous control strategies. A comprehensive simulation environment is developed using historical solar radiation data, seawater quality profiles, desalination process models, battery storage dynamics, and water demand patterns. The framework incorporates operational constraints related to membrane pressure limits, recovery ratio boundaries, battery capacity, pump efficiency, water storage capacity, and minimum freshwater demand. Performance is evaluated using total freshwater production, specific energy consumption, solar fraction, operating cost, renewable curtailment rate, battery cycling cost, water shortage probability, and system reliability.

Experimental evaluation is expected to demonstrate that the proposed deep reinforcement learning framework outperforms conventional rule-based control, model predictive control, heuristic optimization, and static scheduling methods by learning adaptive policies that respond effectively to rapid changes in solar availability and desalination demand. The framework can improve energy efficiency, stabilize water production, reduce dependence on grid or backup power, and enhance the long-term operational sustainability of solar-powered desalination systems. By integrating intelligent decision-making with renewable energy and desalination process control, the proposed system provides a scalable and adaptive solution for efficient freshwater production in arid, coastal, and off-grid regions.

 

Keywords

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