Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods
<p dir="ltr">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 tota...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2024
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إضافة وسم
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| _version_ | 1864513543044333568 |
|---|---|
| author | Arash Golabi (21841484) |
| author2 | Abdelkarim Erradi (13475740) Hazim Qiblawey (16030546) Ashraf Tantawy (21841487) Ahmed Bensaid (21259505) Khaled Shaban (20074425) |
| author2_role | author author author author author |
| author_facet | Arash Golabi (21841484) Abdelkarim Erradi (13475740) Hazim Qiblawey (16030546) Ashraf Tantawy (21841487) Ahmed Bensaid (21259505) Khaled Shaban (20074425) |
| author_role | author |
| dc.creator.none.fl_str_mv | Arash Golabi (21841484) Abdelkarim Erradi (13475740) Hazim Qiblawey (16030546) Ashraf Tantawy (21841487) Ahmed Bensaid (21259505) Khaled Shaban (20074425) |
| dc.date.none.fl_str_mv | 2024-05-13T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10489-024-05452-8 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Optimal_operation_of_reverse_osmosis_desalination_process_with_deep_reinforcement_learning_methods/29715005 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Engineering practice and education Environmental engineering Information and computing sciences Machine learning Reinforcement learning Reverse osmosis Desalination process Dynamic modeling Deep deterministic policy gradient Deep Q-Network Optimal management Data-driven controller |
| dc.title.none.fl_str_mv | Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10489-024-05452-8" target="_blank">https://dx.doi.org/10.1007/s10489-024-05452-8</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c18bcd2fbec6eab3929e130dbef15ddf |
| identifier_str_mv | 10.1007/s10489-024-05452-8 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29715005 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Optimal operation of reverse osmosis desalination process with deep reinforcement learning methodsArash Golabi (21841484)Abdelkarim Erradi (13475740)Hazim Qiblawey (16030546)Ashraf Tantawy (21841487)Ahmed Bensaid (21259505)Khaled Shaban (20074425)EngineeringEngineering practice and educationEnvironmental engineeringInformation and computing sciencesMachine learningReinforcement learningReverse osmosisDesalination processDynamic modelingDeep deterministic policy gradientDeep Q-NetworkOptimal managementData-driven controller<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10489-024-05452-8" target="_blank">https://dx.doi.org/10.1007/s10489-024-05452-8</a></p>2024-05-13T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10489-024-05452-8https://figshare.com/articles/journal_contribution/Optimal_operation_of_reverse_osmosis_desalination_process_with_deep_reinforcement_learning_methods/29715005CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/297150052024-05-13T09:00:00Z |
| spellingShingle | Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods Arash Golabi (21841484) Engineering Engineering practice and education Environmental engineering Information and computing sciences Machine learning Reinforcement learning Reverse osmosis Desalination process Dynamic modeling Deep deterministic policy gradient Deep Q-Network Optimal management Data-driven controller |
| status_str | publishedVersion |
| title | Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods |
| title_full | Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods |
| title_fullStr | Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods |
| title_full_unstemmed | Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods |
| title_short | Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods |
| title_sort | Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods |
| topic | Engineering Engineering practice and education Environmental engineering Information and computing sciences Machine learning Reinforcement learning Reverse osmosis Desalination process Dynamic modeling Deep deterministic policy gradient Deep Q-Network Optimal management Data-driven controller |