Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis
Fe-based Fischer-Tropsch Synthesis (FTS) enables the selective conversion of syngas into long-chain hydrocarbons, which can be further refined to produce highly demanded liquid fuels and high-value chemical products. However, developing novel heterogeneous catalysts for FTS with desirable performanc...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Artificial Intelligence Chemistry |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949747724000204 |
| _version_ | 1831867336731131904 |
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| author | Yujun Liu Xiaolong Zhang Luotong Li Xingchen Liu Tingyu Lei Jiawei Bai Wenping Guo Yuwei Zhou Xingwu Liu Botao Teng Xiaodong Wen |
| author_facet | Yujun Liu Xiaolong Zhang Luotong Li Xingchen Liu Tingyu Lei Jiawei Bai Wenping Guo Yuwei Zhou Xingwu Liu Botao Teng Xiaodong Wen |
| author_sort | Yujun Liu |
| collection | DOAJ |
| description | Fe-based Fischer-Tropsch Synthesis (FTS) enables the selective conversion of syngas into long-chain hydrocarbons, which can be further refined to produce highly demanded liquid fuels and high-value chemical products. However, developing novel heterogeneous catalysts for FTS with desirable performance characteristics is a challenging task, as their performance depends on various factors such as precursor, support material, promoters, pretreatment conditions and the catalyst structures. Thus, it remains difficult to understand the structure-performance relationship of FTS and to optimize the catalyst formulations and operating conditions rationally. By integrating traditional chemistry with machine learning, we herein establish intrinsic correlations among reduction, reaction conditions, phase information and the methane selectivity of Fe-based FTS, using high quality experimental data. The content of the iron phases in the post-reaction phase, particularly χ-Fe5C2, significantly influences the methane selectivity of the catalyst. Four types of additives K, Cu, SiO2, and Ca could effectively suppress the methane selectivity, most likely by promoting or stabilizing the iron carbide phases, indicated by their strong correlation. The machine learned structure-performance relationships offers new insights into the design of Fe-based FTS catalysts, and could guide the further optimization of the preprocessing conditions and various parameter factors to minimize the methane selectivity of FTS. |
| first_indexed | 2024-04-24T12:49:58Z |
| format | Article |
| id | doaj.art-68d2580fca0042d7bab54c6b86262c79 |
| institution | Directory Open Access Journal |
| issn | 2949-7477 |
| language | English |
| last_indexed | 2025-03-21T18:36:19Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Artificial Intelligence Chemistry |
| spelling | doaj.art-68d2580fca0042d7bab54c6b86262c792024-06-07T04:11:01ZengElsevierArtificial Intelligence Chemistry2949-74772024-06-0121100062Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesisYujun Liu0Xiaolong Zhang1Luotong Li2Xingchen Liu3Tingyu Lei4Jiawei Bai5Wenping Guo6Yuwei Zhou7Xingwu Liu8Botao Teng9Xiaodong Wen10Tianjin Key Laboratory of Brine Chemical Engineering and Resource Eco-utilization, College of Chemical Engineering and Materials Science, Tianjin University of Science and Technology, Tianjin 300457, China; State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, ChinaTianjin Key Laboratory of Brine Chemical Engineering and Resource Eco-utilization, College of Chemical Engineering and Materials Science, Tianjin University of Science and Technology, Tianjin 300457, China; State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, ChinaTianjin Key Laboratory of Brine Chemical Engineering and Resource Eco-utilization, College of Chemical Engineering and Materials Science, Tianjin University of Science and Technology, Tianjin 300457, China; State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, ChinaState Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China; Corresponding authors at: State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China.State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, ChinaState Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, ChinaNational Energy Center for Coal to Clean Fuels, Synfuels China Co., Ltd., Huairou District, Beijing 101400, ChinaNational Energy Center for Coal to Clean Fuels, Synfuels China Co., Ltd., Huairou District, Beijing 101400, ChinaNational Energy Center for Coal to Clean Fuels, Synfuels China Co., Ltd., Huairou District, Beijing 101400, China; Corresponding authors.Tianjin Key Laboratory of Brine Chemical Engineering and Resource Eco-utilization, College of Chemical Engineering and Materials Science, Tianjin University of Science and Technology, Tianjin 300457, China; Corresponding authors.State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China; National Energy Center for Coal to Clean Fuels, Synfuels China Co., Ltd., Huairou District, Beijing 101400, China; Corresponding authors at: State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China.Fe-based Fischer-Tropsch Synthesis (FTS) enables the selective conversion of syngas into long-chain hydrocarbons, which can be further refined to produce highly demanded liquid fuels and high-value chemical products. However, developing novel heterogeneous catalysts for FTS with desirable performance characteristics is a challenging task, as their performance depends on various factors such as precursor, support material, promoters, pretreatment conditions and the catalyst structures. Thus, it remains difficult to understand the structure-performance relationship of FTS and to optimize the catalyst formulations and operating conditions rationally. By integrating traditional chemistry with machine learning, we herein establish intrinsic correlations among reduction, reaction conditions, phase information and the methane selectivity of Fe-based FTS, using high quality experimental data. The content of the iron phases in the post-reaction phase, particularly χ-Fe5C2, significantly influences the methane selectivity of the catalyst. Four types of additives K, Cu, SiO2, and Ca could effectively suppress the methane selectivity, most likely by promoting or stabilizing the iron carbide phases, indicated by their strong correlation. The machine learned structure-performance relationships offers new insights into the design of Fe-based FTS catalysts, and could guide the further optimization of the preprocessing conditions and various parameter factors to minimize the methane selectivity of FTS.http://www.sciencedirect.com/science/article/pii/S2949747724000204Artificial IntelligenceFischer-TropschCatalysisMethane |
| spellingShingle | Yujun Liu Xiaolong Zhang Luotong Li Xingchen Liu Tingyu Lei Jiawei Bai Wenping Guo Yuwei Zhou Xingwu Liu Botao Teng Xiaodong Wen Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis Artificial Intelligence Chemistry Artificial Intelligence Fischer-Tropsch Catalysis Methane |
| title | Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis |
| title_full | Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis |
| title_fullStr | Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis |
| title_full_unstemmed | Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis |
| title_short | Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis |
| title_sort | machine learning insights into catalyst composition and structural effects on ch4 selectivity in iron based fischer tropsch synthesis |
| topic | Artificial Intelligence Fischer-Tropsch Catalysis Methane |
| url | http://www.sciencedirect.com/science/article/pii/S2949747724000204 |
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