Enhanced Modeling and Control of Organic Rankine Cycle Systems via AM‐LSTM Networks Based Nonlinear MPC
ABSTRACT The organic Rankine cycle (ORC) serves as an effective means of converting low‐grade heat sources into power, playing a pivotal role in environmentally friendly production and energy recovery. However, the inherent complexity, strong and unidentified nonlinearity, and control constraints po...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Energy Science & Engineering |
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| Online Access: | https://doi.org/10.1002/ese3.1962 |
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| author | Yang Sun Ming Du Xiao Qi |
| author_facet | Yang Sun Ming Du Xiao Qi |
| author_sort | Yang Sun |
| collection | DOAJ |
| description | ABSTRACT The organic Rankine cycle (ORC) serves as an effective means of converting low‐grade heat sources into power, playing a pivotal role in environmentally friendly production and energy recovery. However, the inherent complexity, strong and unidentified nonlinearity, and control constraints pose significant challenges to designing an optimal controller for ORC systems. To address these issues, this research introduces a novel modeling and control framework for ORC systems. Leveraging an attention mechanism‐based long short‐term memory (AM‐LSTM) network, the dynamic characteristics of ORC systems, which are subject to non‐Gaussian disturbances, are accurately modeled. A performance metric based on survival information potential (SIP) is developed to optimize the network parameters. Furthermore, a multi‐objective optimization approach that integrates nonlinear model predictive control (NMPC) with the multiverse optimizer (MVO) algorithm is implemented to ensure effective control under varying operating conditions and constraints. Through extensive simulations, the proposed framework demonstrates superior accuracy, robustness, and control performance for ORC systems. |
| format | Article |
| id | doaj-art-3b3e627016ba4738a87b4d9bc16c7f9a |
| institution | Kabale University |
| issn | 2050-0505 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj-art-3b3e627016ba4738a87b4d9bc16c7f9a2025-01-21T11:38:24ZengWileyEnergy Science & Engineering2050-05052025-01-011319410610.1002/ese3.1962Enhanced Modeling and Control of Organic Rankine Cycle Systems via AM‐LSTM Networks Based Nonlinear MPCYang Sun0Ming Du1Xiao Qi2Energy Electricity Research Center Jinan University Zhuhai ChinaSchool of Control and Computer Engineering North China Electric Power University Beijing ChinaEnergy Electricity Research Center Jinan University Zhuhai ChinaABSTRACT The organic Rankine cycle (ORC) serves as an effective means of converting low‐grade heat sources into power, playing a pivotal role in environmentally friendly production and energy recovery. However, the inherent complexity, strong and unidentified nonlinearity, and control constraints pose significant challenges to designing an optimal controller for ORC systems. To address these issues, this research introduces a novel modeling and control framework for ORC systems. Leveraging an attention mechanism‐based long short‐term memory (AM‐LSTM) network, the dynamic characteristics of ORC systems, which are subject to non‐Gaussian disturbances, are accurately modeled. A performance metric based on survival information potential (SIP) is developed to optimize the network parameters. Furthermore, a multi‐objective optimization approach that integrates nonlinear model predictive control (NMPC) with the multiverse optimizer (MVO) algorithm is implemented to ensure effective control under varying operating conditions and constraints. Through extensive simulations, the proposed framework demonstrates superior accuracy, robustness, and control performance for ORC systems.https://doi.org/10.1002/ese3.1962LSTMmodel predictive controloptimalorganic Rankine cyclesurvival information potential |
| spellingShingle | Yang Sun Ming Du Xiao Qi Enhanced Modeling and Control of Organic Rankine Cycle Systems via AM‐LSTM Networks Based Nonlinear MPC Energy Science & Engineering LSTM model predictive control optimal organic Rankine cycle survival information potential |
| title | Enhanced Modeling and Control of Organic Rankine Cycle Systems via AM‐LSTM Networks Based Nonlinear MPC |
| title_full | Enhanced Modeling and Control of Organic Rankine Cycle Systems via AM‐LSTM Networks Based Nonlinear MPC |
| title_fullStr | Enhanced Modeling and Control of Organic Rankine Cycle Systems via AM‐LSTM Networks Based Nonlinear MPC |
| title_full_unstemmed | Enhanced Modeling and Control of Organic Rankine Cycle Systems via AM‐LSTM Networks Based Nonlinear MPC |
| title_short | Enhanced Modeling and Control of Organic Rankine Cycle Systems via AM‐LSTM Networks Based Nonlinear MPC |
| title_sort | enhanced modeling and control of organic rankine cycle systems via am lstm networks based nonlinear mpc |
| topic | LSTM model predictive control optimal organic Rankine cycle survival information potential |
| url | https://doi.org/10.1002/ese3.1962 |
| work_keys_str_mv | AT yangsun enhancedmodelingandcontroloforganicrankinecyclesystemsviaamlstmnetworksbasednonlinearmpc AT mingdu enhancedmodelingandcontroloforganicrankinecyclesystemsviaamlstmnetworksbasednonlinearmpc AT xiaoqi enhancedmodelingandcontroloforganicrankinecyclesystemsviaamlstmnetworksbasednonlinearmpc |