Performance Improvement of Advanced Reverse Osmosis Processes by Applying Deep Learning and Reinforcement Learning Models to Process Design and Optimization

Abstract

As global water scarcity intensifies, the demand for seawater desalination continues to grow. Currently, over 60% of seawater desalination plants worldwide utilize reverse osmosis (RO) processes, favored for their mature technology and excellent energy efficiency. Further advancements in RO processes can be achieved through the development of hybrid systems and changes in configurations, aimed at improving recovery rate, water quality, and energy efficiency. Successful optimization of desalination plants requires a comprehensive approach to design, operation, and control. This dissertation aims to enhance seawater desalination performance by optimizing the design, operation, and control of various RO processes using simulation and artificial intelligence. Firstly, simulation-based optimization is applied to design and estimate the costs of novel processes, such as the forward osmosis (FO)/Crystallization/RO hybrid process, before plant construction under high-salinity and high-temperature conditions. This approach ensures the economic feasibility and efficiency of new desalination technologies under challenging environmental conditions. Subsequently, actual data from RO plants in the Middle East is interpolated using the non-autoregressive multiresolution sequence imputation (NAOMI) technique, and real-time predictions of key operational variables are made using deep learning models. This enables rapid operational optimization in the early stages of operation, when data is often missing due to process interruptions and breakdowns. Finally, autonomous control through reinforcement learning is implemented to optimize closed circuit reverse osmosis (CCRO) systems. By enabling systems to adapt and optimize operations in real-time, reinforcement learning enhances process efficiency and resilience, even in dynamic and uncertain environments. In summary, this dissertation presents a holistic approach to improving seawater desalination through the integration of simulation-based optimization, data-driven modeling, and autonomous control.