Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods

The 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...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2024-04, Vol.54 (8), p.6333-6353
Main Authors: Golabi, Arash, Erradi, Abdelkarim, Qiblawey, Hazim, Tantawy, Ashraf, Bensaid, Ahmed, Shaban, Khaled
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Control methods
Deep learning
Desalination
Dynamic models
Energy consumption
Flow velocity
Machines
Manufacturing
Mechanical Engineering
Optimization
Process controls
Processes
Production costs
Reagents
Reverse osmosis
Storage tanks
Water demand
Water levels
Water quality
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Summary:The 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.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-024-05452-8