Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data
Pressure swing adsorption (PSA) modeling remains a challenging task since it exhibits strong dynamic and cyclic behavior. This study presents a systematic physics-informed machine learning method that integrates transfer learning and labeled data to construct a spatiotemporal model of the PSA proces...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-06-01
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| Series: | Green Chemical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666952824000591 |
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| author | Zhiqiang Wu Yunquan Chen Bingjian Zhang Jingzheng Ren Qinglin Chen Huan Wang Chang He |
| author_facet | Zhiqiang Wu Yunquan Chen Bingjian Zhang Jingzheng Ren Qinglin Chen Huan Wang Chang He |
| author_sort | Zhiqiang Wu |
| collection | DOAJ |
| description | Pressure swing adsorption (PSA) modeling remains a challenging task since it exhibits strong dynamic and cyclic behavior. This study presents a systematic physics-informed machine learning method that integrates transfer learning and labeled data to construct a spatiotemporal model of the PSA process. To approximate the latent solutions of partial differential equations (PDEs) in the specific steps of pressurization, adsorption, heavy reflux, counter-current depressurization, and light reflux, the system's network representation is decomposed into five lightweight sub-networks. On this basis, we propose a parameter-based transfer learning (TL) combined with domain decomposition to address the long-term integration of periodic PDEs and expedite the network training process. Moreover, to tackle challenges related to sharp adsorption fronts, our method allows for the inclusion of a specified amount of labeled data at the boundaries and/or within the system in the loss function. The results show that the proposed method closely matches the outcomes achieved through the conventional numerical method, effectively simulating all steps and cyclic behavior within the PSA processes. |
| format | Article |
| id | doaj-art-bc1520cc3209455cb8ecb4914c33aeb7 |
| institution | DOAJ |
| issn | 2666-9528 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Green Chemical Engineering |
| spelling | doaj-art-bc1520cc3209455cb8ecb4914c33aeb72025-08-20T02:57:08ZengKeAi Communications Co. Ltd.Green Chemical Engineering2666-95282025-06-016223324810.1016/j.gce.2024.08.004Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled dataZhiqiang Wu0Yunquan Chen1Bingjian Zhang2Jingzheng Ren3Qinglin Chen4Huan Wang5Chang He6School of Materials Science and Engineering, Sun Yat-Sen University, Guangzhou, 510275, ChinaSchool of Materials Science and Engineering, Sun Yat-Sen University, Guangzhou, 510275, ChinaSchool of Materials Science and Engineering, Sun Yat-Sen University, Guangzhou, 510275, China; Guangdong Engineering Center for Petrochemical Energy Conservation, Guangzhou, 510275, ChinaDepartment of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, ChinaGuangdong Engineering Center for Petrochemical Energy Conservation, Guangzhou, 510275, China; School of Chemical Engineering and Technology, Sun Yat-sen University, Zhuhai, 519082, ChinaState Key Laboratory of Heavy Oil Processing, China University of Petroleum-Beijing, Beijing, 102249, China; CNNC No. 7 Research and Design Institute Co. Ltd., Taiyuan, 030012, China; Corresponding author.Guangdong Engineering Center for Petrochemical Energy Conservation, Guangzhou, 510275, China; School of Chemical Engineering and Technology, Sun Yat-sen University, Zhuhai, 519082, China; Corresponding author.Pressure swing adsorption (PSA) modeling remains a challenging task since it exhibits strong dynamic and cyclic behavior. This study presents a systematic physics-informed machine learning method that integrates transfer learning and labeled data to construct a spatiotemporal model of the PSA process. To approximate the latent solutions of partial differential equations (PDEs) in the specific steps of pressurization, adsorption, heavy reflux, counter-current depressurization, and light reflux, the system's network representation is decomposed into five lightweight sub-networks. On this basis, we propose a parameter-based transfer learning (TL) combined with domain decomposition to address the long-term integration of periodic PDEs and expedite the network training process. Moreover, to tackle challenges related to sharp adsorption fronts, our method allows for the inclusion of a specified amount of labeled data at the boundaries and/or within the system in the loss function. The results show that the proposed method closely matches the outcomes achieved through the conventional numerical method, effectively simulating all steps and cyclic behavior within the PSA processes.http://www.sciencedirect.com/science/article/pii/S2666952824000591Physics-informed machine learningPressure swing adsorptionTransfer learningLabeled dataPartial differential equations |
| spellingShingle | Zhiqiang Wu Yunquan Chen Bingjian Zhang Jingzheng Ren Qinglin Chen Huan Wang Chang He Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data Green Chemical Engineering Physics-informed machine learning Pressure swing adsorption Transfer learning Labeled data Partial differential equations |
| title | Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data |
| title_full | Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data |
| title_fullStr | Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data |
| title_full_unstemmed | Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data |
| title_short | Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data |
| title_sort | pressure swing adsorption process modeling using physics informed machine learning with transfer learning and labeled data |
| topic | Physics-informed machine learning Pressure swing adsorption Transfer learning Labeled data Partial differential equations |
| url | http://www.sciencedirect.com/science/article/pii/S2666952824000591 |
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