Genetic Algorithm-Based Optimization of Online Diesel Fuel Upgrading Process for Nuclear Power Emergency
To enhance the oxidative stability of aging diesel fuel stored in nuclear power emergency systems, we propose a novel hybrid optimization framework that integrates a Genetic Algorithm (GA), State-Space Network (SSN) modeling, and Computational Fluid Dynamics (CFD) simulation. Unlike previous studies...
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| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6782 |
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| author | Lanqi Zhang Hao Li Fengyi Liu Xiangnan Chu Qi Ma Haotian Ye |
| author_facet | Lanqi Zhang Hao Li Fengyi Liu Xiangnan Chu Qi Ma Haotian Ye |
| author_sort | Lanqi Zhang |
| collection | DOAJ |
| description | To enhance the oxidative stability of aging diesel fuel stored in nuclear power emergency systems, we propose a novel hybrid optimization framework that integrates a Genetic Algorithm (GA), State-Space Network (SSN) modeling, and Computational Fluid Dynamics (CFD) simulation. Unlike previous studies that address treatment efficiency, flow optimization, or simulation separately, our method achieves real-time, simulation-informed optimization by embedding CFD-based performance evaluation directly into the GA fitness function. The SSN is employed to construct a comprehensive superstructure of feasible conditioning paths, which are dynamically explored and optimized by the GA under flow and boundary constraints. The CFD model, implemented via Ansys Fluent, accurately simulates the antioxidant mixing process in the tank and provides feedback on concentration uniformity at key monitoring points. The results demonstrate that the proposed framework reduces the conditioning time by 5.38% and significantly enhances the additive distribution uniformity. This work offers a generalizable approach for intelligent diesel upgrading in high-reliability energy systems and contributes a structured pathway for integrating data-driven optimization with physical process simulation. |
| format | Article |
| id | doaj-art-51a19b64c3284ce981cc428db82c3a8e |
| institution | Matheson Library |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-51a19b64c3284ce981cc428db82c3a8e2025-06-25T13:26:21ZengMDPI AGApplied Sciences2076-34172025-06-011512678210.3390/app15126782Genetic Algorithm-Based Optimization of Online Diesel Fuel Upgrading Process for Nuclear Power EmergencyLanqi Zhang0Hao Li1Fengyi Liu2Xiangnan Chu3Qi Ma4Haotian Ye5China Nuclear Power Operations Co., Ltd., Shenzhen 518026, ChinaChina Nuclear Power Operations Co., Ltd., Shenzhen 518026, ChinaSchool of Chemical Engineering, Dalian University of Technology, Dalian 116024, ChinaYangjiang Nuclear Power Co., Ltd., Yangjiang 529941, ChinaYangjiang Nuclear Power Co., Ltd., Yangjiang 529941, ChinaSchool of Chemical Engineering, Dalian University of Technology, Dalian 116024, ChinaTo enhance the oxidative stability of aging diesel fuel stored in nuclear power emergency systems, we propose a novel hybrid optimization framework that integrates a Genetic Algorithm (GA), State-Space Network (SSN) modeling, and Computational Fluid Dynamics (CFD) simulation. Unlike previous studies that address treatment efficiency, flow optimization, or simulation separately, our method achieves real-time, simulation-informed optimization by embedding CFD-based performance evaluation directly into the GA fitness function. The SSN is employed to construct a comprehensive superstructure of feasible conditioning paths, which are dynamically explored and optimized by the GA under flow and boundary constraints. The CFD model, implemented via Ansys Fluent, accurately simulates the antioxidant mixing process in the tank and provides feedback on concentration uniformity at key monitoring points. The results demonstrate that the proposed framework reduces the conditioning time by 5.38% and significantly enhances the additive distribution uniformity. This work offers a generalizable approach for intelligent diesel upgrading in high-reliability energy systems and contributes a structured pathway for integrating data-driven optimization with physical process simulation.https://www.mdpi.com/2076-3417/15/12/6782genetic algorithmCFD simulationaged dieselonline upgradingoptimizationstate-space network |
| spellingShingle | Lanqi Zhang Hao Li Fengyi Liu Xiangnan Chu Qi Ma Haotian Ye Genetic Algorithm-Based Optimization of Online Diesel Fuel Upgrading Process for Nuclear Power Emergency Applied Sciences genetic algorithm CFD simulation aged diesel online upgrading optimization state-space network |
| title | Genetic Algorithm-Based Optimization of Online Diesel Fuel Upgrading Process for Nuclear Power Emergency |
| title_full | Genetic Algorithm-Based Optimization of Online Diesel Fuel Upgrading Process for Nuclear Power Emergency |
| title_fullStr | Genetic Algorithm-Based Optimization of Online Diesel Fuel Upgrading Process for Nuclear Power Emergency |
| title_full_unstemmed | Genetic Algorithm-Based Optimization of Online Diesel Fuel Upgrading Process for Nuclear Power Emergency |
| title_short | Genetic Algorithm-Based Optimization of Online Diesel Fuel Upgrading Process for Nuclear Power Emergency |
| title_sort | genetic algorithm based optimization of online diesel fuel upgrading process for nuclear power emergency |
| topic | genetic algorithm CFD simulation aged diesel online upgrading optimization state-space network |
| url | https://www.mdpi.com/2076-3417/15/12/6782 |
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