Combining machine learning with multi-physics modelling for multi-objective optimisation and techno-economic analysis of electrochemical CO2 reduction process
As a carbon capture and utilization (CCU) technology, gas diffusion electrode (GDE) based electrochemical CO2 reduction reaction (eCO2RR) can convert CO2 to valuable products, such as formate and CO. However, the electrode parameters and operational conditions need to be studied and optimised to enh...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Carbon Capture Science & Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772656823000428 |
| _version_ | 1797398416098066432 |
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| author | Lei Xing Hai Jiang Xingjian Tian Huajie Yin Weidong Shi Eileen Yu Valerie J. Pinfield Jin Xuan |
| author_facet | Lei Xing Hai Jiang Xingjian Tian Huajie Yin Weidong Shi Eileen Yu Valerie J. Pinfield Jin Xuan |
| author_sort | Lei Xing |
| collection | DOAJ |
| description | As a carbon capture and utilization (CCU) technology, gas diffusion electrode (GDE) based electrochemical CO2 reduction reaction (eCO2RR) can convert CO2 to valuable products, such as formate and CO. However, the electrode parameters and operational conditions need to be studied and optimised to enhance the performance and reduce the net cost of the eCO2RR process before its industrial application. In this work, a machine learning algorithm, i.e., extended adaptive hybrid functions (E-AHF) is combined with a multi-physics model for the data-driven three-objective optimisation and techno-economic analysis of the GDE-based eCO2RR process. The effects of eight design variables on the product yield (PY), CO2 conversion (CR) and specific electrical energy consumption (SEEC) of the process are analysed. The results show that the R2 of the E-AHF model for the prediction of PY, CR and SEEC are all higher than 0.96, indicating the high accuracy of the developed machine learning algorithm for the prediction of the eCO2RR process. The process performance experiences a notable improvement after optimisation and is affected by a combination of eight variables, amongst which the electrolyte concentration having the most significant impact on PY and CR. The optimal trade-off single-pass PY, CR and SEEC are 3.25×10−9 kg s−1, 0.663% and 9.95 kWh kg−1 based on flow channels with 1 cm in length, respectively. The SEEC is reduced by nearly half and PY and CR are improved more than two times after optimisation. The production cost of the GDE-based eCO2RR process was approximately $378 t−1product (CO and formate), much lower than that of traditional CO2 utilisation factories ($835 t−1product). The electricity cost accounted for more than 80% of the total cost, amounting to $318 t−1, indicating that cheaper and cleaner electricity sources would further reduce the production cost of the process, which is the key to the economics of this technology. |
| first_indexed | 2024-03-09T01:25:21Z |
| format | Article |
| id | doaj.art-62bbdc6a1aef42e98599036f2d2b3bed |
| institution | Directory Open Access Journal |
| issn | 2772-6568 |
| language | English |
| last_indexed | 2024-03-09T01:25:21Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Carbon Capture Science & Technology |
| spelling | doaj.art-62bbdc6a1aef42e98599036f2d2b3bed2023-12-10T06:19:20ZengElsevierCarbon Capture Science & Technology2772-65682023-12-019100138Combining machine learning with multi-physics modelling for multi-objective optimisation and techno-economic analysis of electrochemical CO2 reduction processLei Xing0Hai Jiang1Xingjian Tian2Huajie Yin3Weidong Shi4Eileen Yu5Valerie J. Pinfield6Jin Xuan7School of Chemistry and Chemical Engineering, University of Surrey, Guildford GU2 7XH, UK; Corresponding author.School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang 212013, ChinaInstitute of Solid State Physics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaSchool of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang 212013, ChinaDepartment of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, UKDepartment of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, UKSchool of Chemistry and Chemical Engineering, University of Surrey, Guildford GU2 7XH, UKAs a carbon capture and utilization (CCU) technology, gas diffusion electrode (GDE) based electrochemical CO2 reduction reaction (eCO2RR) can convert CO2 to valuable products, such as formate and CO. However, the electrode parameters and operational conditions need to be studied and optimised to enhance the performance and reduce the net cost of the eCO2RR process before its industrial application. In this work, a machine learning algorithm, i.e., extended adaptive hybrid functions (E-AHF) is combined with a multi-physics model for the data-driven three-objective optimisation and techno-economic analysis of the GDE-based eCO2RR process. The effects of eight design variables on the product yield (PY), CO2 conversion (CR) and specific electrical energy consumption (SEEC) of the process are analysed. The results show that the R2 of the E-AHF model for the prediction of PY, CR and SEEC are all higher than 0.96, indicating the high accuracy of the developed machine learning algorithm for the prediction of the eCO2RR process. The process performance experiences a notable improvement after optimisation and is affected by a combination of eight variables, amongst which the electrolyte concentration having the most significant impact on PY and CR. The optimal trade-off single-pass PY, CR and SEEC are 3.25×10−9 kg s−1, 0.663% and 9.95 kWh kg−1 based on flow channels with 1 cm in length, respectively. The SEEC is reduced by nearly half and PY and CR are improved more than two times after optimisation. The production cost of the GDE-based eCO2RR process was approximately $378 t−1product (CO and formate), much lower than that of traditional CO2 utilisation factories ($835 t−1product). The electricity cost accounted for more than 80% of the total cost, amounting to $318 t−1, indicating that cheaper and cleaner electricity sources would further reduce the production cost of the process, which is the key to the economics of this technology.http://www.sciencedirect.com/science/article/pii/S2772656823000428Electrochemical CO2 reductionGas diffusion electrodeMulti-physics modellingMachine learningMulti-objective optimisation |
| spellingShingle | Lei Xing Hai Jiang Xingjian Tian Huajie Yin Weidong Shi Eileen Yu Valerie J. Pinfield Jin Xuan Combining machine learning with multi-physics modelling for multi-objective optimisation and techno-economic analysis of electrochemical CO2 reduction process Carbon Capture Science & Technology Electrochemical CO2 reduction Gas diffusion electrode Multi-physics modelling Machine learning Multi-objective optimisation |
| title | Combining machine learning with multi-physics modelling for multi-objective optimisation and techno-economic analysis of electrochemical CO2 reduction process |
| title_full | Combining machine learning with multi-physics modelling for multi-objective optimisation and techno-economic analysis of electrochemical CO2 reduction process |
| title_fullStr | Combining machine learning with multi-physics modelling for multi-objective optimisation and techno-economic analysis of electrochemical CO2 reduction process |
| title_full_unstemmed | Combining machine learning with multi-physics modelling for multi-objective optimisation and techno-economic analysis of electrochemical CO2 reduction process |
| title_short | Combining machine learning with multi-physics modelling for multi-objective optimisation and techno-economic analysis of electrochemical CO2 reduction process |
| title_sort | combining machine learning with multi physics modelling for multi objective optimisation and techno economic analysis of electrochemical co2 reduction process |
| topic | Electrochemical CO2 reduction Gas diffusion electrode Multi-physics modelling Machine learning Multi-objective optimisation |
| url | http://www.sciencedirect.com/science/article/pii/S2772656823000428 |
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