Data-driven analysis in the selective oligomerization of long-chain linear alpha olefin on zeolite catalysts: A machine learning-based parameter study
In this study, the oligomerization of 1-octene was investigated using various zeolites through both experimental and machine learning (ML) approaches. The structural characteristics of the zeolites and experimental conditions were used as input parameters to analyze the feature importance of each ke...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Fuel Processing Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378382024001346 |
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| author | Sung Woo Lee Marcel Jonathan Hidajat Seung Hyeok Cha Gwang-Nam Yun Dong Won Hwang |
| author_facet | Sung Woo Lee Marcel Jonathan Hidajat Seung Hyeok Cha Gwang-Nam Yun Dong Won Hwang |
| author_sort | Sung Woo Lee |
| collection | DOAJ |
| description | In this study, the oligomerization of 1-octene was investigated using various zeolites through both experimental and machine learning (ML) approaches. The structural characteristics of the zeolites and experimental conditions were used as input parameters to analyze the feature importance of each key factor on the oligomerization of 1-octene. By quantifying these influences, the reaction mechanism was elucidated, and a methodology for maximizing the oligomerization reaction was developed. The ML methods employed in this study were SHapley Additive exPlanations (SHAP) and Sure Independence Screening and Sparsifying Operator (SISSO). While the SHAP method is a well-validated conventional approach, it has limitations due to its high data requirements. Therefore, the SISSO method was applied, as it requires fewer data points and offers a transparent computational process. SISSO provided results in the form of human-interpretable equations, allowing for an analysis of these equations to deepen insights into the reaction mechanism. |
| format | Article |
| id | doaj-art-8c653523b1cf462c849c9d0d9fec8ab9 |
| institution | Kabale University |
| issn | 0378-3820 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Fuel Processing Technology |
| spelling | doaj-art-8c653523b1cf462c849c9d0d9fec8ab92025-01-15T04:11:31ZengElsevierFuel Processing Technology0378-38202025-03-01267108164Data-driven analysis in the selective oligomerization of long-chain linear alpha olefin on zeolite catalysts: A machine learning-based parameter studySung Woo Lee0Marcel Jonathan Hidajat1Seung Hyeok Cha2Gwang-Nam Yun3Dong Won Hwang4Green Carbon Research Center, Korea Research Institute of Chemical Technology (KRICT), 141 Gajeongro, Yuseong, Daejeon 34114, Republic of KoreaGreen Carbon Research Center, Korea Research Institute of Chemical Technology (KRICT), 141 Gajeongro, Yuseong, Daejeon 34114, Republic of Korea; Department of Advanced Materials and Chemical Engineering, University of Science and Technology (UST), 217 Gajeongro, Yuseong, Daejeon 34113, Republic of KoreaGreen Carbon Research Center, Korea Research Institute of Chemical Technology (KRICT), 141 Gajeongro, Yuseong, Daejeon 34114, Republic of Korea; Corresponding authors.Green Carbon Research Center, Korea Research Institute of Chemical Technology (KRICT), 141 Gajeongro, Yuseong, Daejeon 34114, Republic of Korea; Department of Advanced Materials and Chemical Engineering, University of Science and Technology (UST), 217 Gajeongro, Yuseong, Daejeon 34113, Republic of Korea; School of Chemical Engineering, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-Gu, Suwon, Gyeong Gi-Do 16419, Republic of Korea; Corresponding authors.Green Carbon Research Center, Korea Research Institute of Chemical Technology (KRICT), 141 Gajeongro, Yuseong, Daejeon 34114, Republic of Korea; Department of Advanced Materials and Chemical Engineering, University of Science and Technology (UST), 217 Gajeongro, Yuseong, Daejeon 34113, Republic of Korea; Corresponding authors.In this study, the oligomerization of 1-octene was investigated using various zeolites through both experimental and machine learning (ML) approaches. The structural characteristics of the zeolites and experimental conditions were used as input parameters to analyze the feature importance of each key factor on the oligomerization of 1-octene. By quantifying these influences, the reaction mechanism was elucidated, and a methodology for maximizing the oligomerization reaction was developed. The ML methods employed in this study were SHapley Additive exPlanations (SHAP) and Sure Independence Screening and Sparsifying Operator (SISSO). While the SHAP method is a well-validated conventional approach, it has limitations due to its high data requirements. Therefore, the SISSO method was applied, as it requires fewer data points and offers a transparent computational process. SISSO provided results in the form of human-interpretable equations, allowing for an analysis of these equations to deepen insights into the reaction mechanism.http://www.sciencedirect.com/science/article/pii/S0378382024001346OligomerizationMachine learningIsomerizationSHAPSISSOParameter |
| spellingShingle | Sung Woo Lee Marcel Jonathan Hidajat Seung Hyeok Cha Gwang-Nam Yun Dong Won Hwang Data-driven analysis in the selective oligomerization of long-chain linear alpha olefin on zeolite catalysts: A machine learning-based parameter study Fuel Processing Technology Oligomerization Machine learning Isomerization SHAP SISSO Parameter |
| title | Data-driven analysis in the selective oligomerization of long-chain linear alpha olefin on zeolite catalysts: A machine learning-based parameter study |
| title_full | Data-driven analysis in the selective oligomerization of long-chain linear alpha olefin on zeolite catalysts: A machine learning-based parameter study |
| title_fullStr | Data-driven analysis in the selective oligomerization of long-chain linear alpha olefin on zeolite catalysts: A machine learning-based parameter study |
| title_full_unstemmed | Data-driven analysis in the selective oligomerization of long-chain linear alpha olefin on zeolite catalysts: A machine learning-based parameter study |
| title_short | Data-driven analysis in the selective oligomerization of long-chain linear alpha olefin on zeolite catalysts: A machine learning-based parameter study |
| title_sort | data driven analysis in the selective oligomerization of long chain linear alpha olefin on zeolite catalysts a machine learning based parameter study |
| topic | Oligomerization Machine learning Isomerization SHAP SISSO Parameter |
| url | http://www.sciencedirect.com/science/article/pii/S0378382024001346 |
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