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AuthorGolabi, Arash
AuthorErradi, Abdelkarim
AuthorQiblawey, Hazim
AuthorTantawy, Ashraf
AuthorBensaid, Ahmed
AuthorShaban, Khaled
Available date2024-08-29T07:02:44Z
Publication Date2024-04-01
Publication NameApplied Intelligence
Identifierhttp://dx.doi.org/10.1007/s10489-024-05452-8
CitationGolabi, A., Erradi, A., Qiblawey, H., Tantawy, A., Bensaid, A., & Shaban, K. (2024). Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods. Applied Intelligence, 1-21.‏
ISSN0924669X
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85192829629&origin=inward
URIhttp://hdl.handle.net/10576/58411
AbstractThe reverse osmosis (RO) process is a well-established desalination technology, wherein energy-efficient techniques and advanced process control methods significantly reduce production costs. This study proposes an optimal real-time management method to minimize the total daily operation cost of an RO desalination plant, integrating a storage tank system to meet varying daily freshwater demand. Utilizing the dynamic model of the RO process, a cascade structure with two reinforcement learning (RL) agents, namely the deep deterministic policy gradient (DDPG) and deep Q-Network (DQN), is developed to optimize the operation of the RO plant. The DDPG agent, manipulating the high-pressure pump, controls the permeate flow rate to track a reference setpoint value. Simultaneously, the DQN agent selects the optimal setpoint value and communicates it to the DDPG controller to minimize the plant’s operation cost. Monitoring storage tanks, permeate flow rates, and water demand enables the DQN agent to determine the required amount of permeate water, optimizing water quality and energy consumption. Additionally, the DQN agent monitors the storage tank’s water level to prevent overflow or underflow of permeate water. Simulation results demonstrate the effectiveness and practicality of the designed RL agents.
Languageen
PublisherSpringer
SubjectData-driven controller
Deep deterministic policy gradient
Deep Q-Network
Desalination process
Dynamic modeling
Optimal management
Reinforcement learning
Reverse osmosis
TitleOptimal operation of reverse osmosis desalination process with deep reinforcement learning methods
TypeArticle
Pagination6333-6353
Issue Number8
Volume Number54
dc.accessType Open Access


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