Sparrow optimization gated recurrent convolutional network for temperature modeling of wave rotor refrigeration process
Temperature modeling plays an important role in the wave rotor refrigeration process control and optimization. However, considering data-driven nonlinear and time-delay modeling, how to determine the structure of the model is a challenging problem. To solve this problem, a novel sparrow optimization...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Measurement: Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950345025000284 |
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| _version_ | 1839593966730739712 |
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| author | Qi Li Kun Han Shifa Cui Yaru Shi |
| author_facet | Qi Li Kun Han Shifa Cui Yaru Shi |
| author_sort | Qi Li |
| collection | DOAJ |
| description | Temperature modeling plays an important role in the wave rotor refrigeration process control and optimization. However, considering data-driven nonlinear and time-delay modeling, how to determine the structure of the model is a challenging problem. To solve this problem, a novel sparrow optimization gated recurrent convolutional network (SGRC) deep learning method is proposed. Firstly, to exploit the advantages of convolutional neural network (CNN), the sample data is converted into grids along the time axis similar to the image input, which contains model structure and dynamic time-delay information. The multivariate and dynamic time-delay information is input into the CNN to extract the multivariate model structure features of the data. Then, after flattening the data into one-dimensional time series, input it into gated recurrent unit (GRU) layers to learn the temporal dependencies of the wave rotor refrigeration. The hyperparameters of the SGRC network are optimized using the sparrow search algorithm (SSA). Finally, simulation results based on wave rotor refrigeration industry data show that the proposed SGRC method achieves superior performance compared to traditional machine learning and other deep learning approaches, exhibiting lower RMSE and MAE values while attaining a higher R2 score. This enhancement significantly improves the generalization capability of the temperature model in the wave rotor refrigeration process. |
| format | Article |
| id | doaj-art-3d65acb4e8b5479db2bfbc0c9f79e8dc |
| institution | Matheson Library |
| issn | 2950-3450 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Energy |
| spelling | doaj-art-3d65acb4e8b5479db2bfbc0c9f79e8dc2025-08-03T04:43:32ZengElsevierMeasurement: Energy2950-34502025-09-017100061Sparrow optimization gated recurrent convolutional network for temperature modeling of wave rotor refrigeration processQi Li0Kun Han1Shifa Cui2Yaru Shi3School of Control Science and Engineering, Dalian University of Technology, Dalian, 116023, China; Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, 116023, China; Corresponding author. School of Control Science and Engineering, Dalian University of Technology, Dalian, 116023, China.School of Control Science and Engineering, Dalian University of Technology, Dalian, 116023, China; Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, 116023, ChinaSchool of Control Science and Engineering, Dalian University of Technology, Dalian, 116023, China; Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, 116023, ChinaSchool of Control Science and Engineering, Dalian University of Technology, Dalian, 116023, China; Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, 116023, ChinaTemperature modeling plays an important role in the wave rotor refrigeration process control and optimization. However, considering data-driven nonlinear and time-delay modeling, how to determine the structure of the model is a challenging problem. To solve this problem, a novel sparrow optimization gated recurrent convolutional network (SGRC) deep learning method is proposed. Firstly, to exploit the advantages of convolutional neural network (CNN), the sample data is converted into grids along the time axis similar to the image input, which contains model structure and dynamic time-delay information. The multivariate and dynamic time-delay information is input into the CNN to extract the multivariate model structure features of the data. Then, after flattening the data into one-dimensional time series, input it into gated recurrent unit (GRU) layers to learn the temporal dependencies of the wave rotor refrigeration. The hyperparameters of the SGRC network are optimized using the sparrow search algorithm (SSA). Finally, simulation results based on wave rotor refrigeration industry data show that the proposed SGRC method achieves superior performance compared to traditional machine learning and other deep learning approaches, exhibiting lower RMSE and MAE values while attaining a higher R2 score. This enhancement significantly improves the generalization capability of the temperature model in the wave rotor refrigeration process.http://www.sciencedirect.com/science/article/pii/S2950345025000284Wave rotor refrigeration processConvolutional neural networkGated recurrent unitSparrow search algorithmTemperature modeling |
| spellingShingle | Qi Li Kun Han Shifa Cui Yaru Shi Sparrow optimization gated recurrent convolutional network for temperature modeling of wave rotor refrigeration process Measurement: Energy Wave rotor refrigeration process Convolutional neural network Gated recurrent unit Sparrow search algorithm Temperature modeling |
| title | Sparrow optimization gated recurrent convolutional network for temperature modeling of wave rotor refrigeration process |
| title_full | Sparrow optimization gated recurrent convolutional network for temperature modeling of wave rotor refrigeration process |
| title_fullStr | Sparrow optimization gated recurrent convolutional network for temperature modeling of wave rotor refrigeration process |
| title_full_unstemmed | Sparrow optimization gated recurrent convolutional network for temperature modeling of wave rotor refrigeration process |
| title_short | Sparrow optimization gated recurrent convolutional network for temperature modeling of wave rotor refrigeration process |
| title_sort | sparrow optimization gated recurrent convolutional network for temperature modeling of wave rotor refrigeration process |
| topic | Wave rotor refrigeration process Convolutional neural network Gated recurrent unit Sparrow search algorithm Temperature modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2950345025000284 |
| work_keys_str_mv | AT qili sparrowoptimizationgatedrecurrentconvolutionalnetworkfortemperaturemodelingofwaverotorrefrigerationprocess AT kunhan sparrowoptimizationgatedrecurrentconvolutionalnetworkfortemperaturemodelingofwaverotorrefrigerationprocess AT shifacui sparrowoptimizationgatedrecurrentconvolutionalnetworkfortemperaturemodelingofwaverotorrefrigerationprocess AT yarushi sparrowoptimizationgatedrecurrentconvolutionalnetworkfortemperaturemodelingofwaverotorrefrigerationprocess |