Tabular generative modeling framework for multi-property data synthesis of pyrolyzed biochar
[Display omitted] •Generation framework for synthesizing biochar property data was established.•Missing records in reference dataset were imputed by MultipleMICE and MissForest.•Synthpop accurately captured multimodal, normal, and skewed distribution patterns.•Synthpop and TVAE preserved inter-featu...
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| Vydáno v: | Bioresource technology Ročník 438; s. 133207 |
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| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
Elsevier Ltd
01.12.2025
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| Témata: | |
| ISSN: | 0960-8524, 1873-2976, 1873-2976 |
| On-line přístup: | Získat plný text |
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| Abstract | [Display omitted]
•Generation framework for synthesizing biochar property data was established.•Missing records in reference dataset were imputed by MultipleMICE and MissForest.•Synthpop accurately captured multimodal, normal, and skewed distribution patterns.•Synthpop and TVAE preserved inter-feature correlations within synthetic data.•Synthpop was a reliable model to generate high fidelity data for biochar property.
Biochar properties are governed by trade-offs among feedstock types, modification methods, and pyrolysis conditions, complicating the design of engineered biochar for specific applications. In this study, four data generative models, including Tabular Generative Adversarial Network (TGAN), Conditional Tabular Generative Adversarial Network (CTGAN), Tabular Variational Autoencoder (TVAE), and statistical Synthpop, were developed to predict biochar properties using MultipleMICE- and MissForest-imputed reference datasets (n = 461). The Synthpop model outperformed others in synthetic data quality, achieving high distribution similarity score (0.97) by accurately capturing multimodal, normal, and skewed distribution patterns for both continuous features (KSComplement = 0.98) and categorical variables (TVComplement = 0.95). Correlation trends evaluation indicated that TVAE and Synthpop models effectively preserved inter-feature dependencies between synthetic and original real data. Experimental validation with bagasse- and bamboo-derived biochar confirmed the reliability of Synthpop model in generating high fidelity data for biochar properties such as surface morphology, elemental composition, and surface functional groups. Specifically, up to 70.8 % of features for bagasse biochar and 66.7 % of features for bamboo biochar exhibited low relative errors (<5 %). Overall, this work introduced an applicable framework and a reliable Synthpop model for synthesizing pyrolyzed biochar properties, providing insights into rapid screening of application-specific biochar. |
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| AbstractList | Biochar properties are governed by trade-offs among feedstock types, modification methods, and pyrolysis conditions, complicating the design of engineered biochar for specific applications. In this study, four data generative models, including Tabular Generative Adversarial Network (TGAN), Conditional Tabular Generative Adversarial Network (CTGAN), Tabular Variational Autoencoder (TVAE), and statistical Synthpop, were developed to predict biochar properties using MultipleMICE- and MissForest-imputed reference datasets (n = 461). The Synthpop model outperformed others in synthetic data quality, achieving high distribution similarity score (0.97) by accurately capturing multimodal, normal, and skewed distribution patterns for both continuous features (KSComplement = 0.98) and categorical variables (TVComplement = 0.95). Correlation trends evaluation indicated that TVAE and Synthpop models effectively preserved inter-feature dependencies between synthetic and original real data. Experimental validation with bagasse- and bamboo-derived biochar confirmed the reliability of Synthpop model in generating high fidelity data for biochar properties such as surface morphology, elemental composition, and surface functional groups. Specifically, up to 70.8 % of features for bagasse biochar and 66.7 % of features for bamboo biochar exhibited low relative errors (<5 %). Overall, this work introduced an applicable framework and a reliable Synthpop model for synthesizing pyrolyzed biochar properties, providing insights into rapid screening of application-specific biochar. Biochar properties are governed by trade-offs among feedstock types, modification methods, and pyrolysis conditions, complicating the design of engineered biochar for specific applications. In this study, four data generative models, including Tabular Generative Adversarial Network (TGAN), Conditional Tabular Generative Adversarial Network (CTGAN), Tabular Variational Autoencoder (TVAE), and statistical Synthpop, were developed to predict biochar properties using MultipleMICE- and MissForest-imputed reference datasets (n = 461). The Synthpop model outperformed others in synthetic data quality, achieving high distribution similarity score (0.97) by accurately capturing multimodal, normal, and skewed distribution patterns for both continuous features (KSComplement = 0.98) and categorical variables (TVComplement = 0.95). Correlation trends evaluation indicated that TVAE and Synthpop models effectively preserved inter-feature dependencies between synthetic and original real data. Experimental validation with bagasse- and bamboo-derived biochar confirmed the reliability of Synthpop model in generating high fidelity data for biochar properties such as surface morphology, elemental composition, and surface functional groups. Specifically, up to 70.8 % of features for bagasse biochar and 66.7 % of features for bamboo biochar exhibited low relative errors (<5 %). Overall, this work introduced an applicable framework and a reliable Synthpop model for synthesizing pyrolyzed biochar properties, providing insights into rapid screening of application-specific biochar.Biochar properties are governed by trade-offs among feedstock types, modification methods, and pyrolysis conditions, complicating the design of engineered biochar for specific applications. In this study, four data generative models, including Tabular Generative Adversarial Network (TGAN), Conditional Tabular Generative Adversarial Network (CTGAN), Tabular Variational Autoencoder (TVAE), and statistical Synthpop, were developed to predict biochar properties using MultipleMICE- and MissForest-imputed reference datasets (n = 461). The Synthpop model outperformed others in synthetic data quality, achieving high distribution similarity score (0.97) by accurately capturing multimodal, normal, and skewed distribution patterns for both continuous features (KSComplement = 0.98) and categorical variables (TVComplement = 0.95). Correlation trends evaluation indicated that TVAE and Synthpop models effectively preserved inter-feature dependencies between synthetic and original real data. Experimental validation with bagasse- and bamboo-derived biochar confirmed the reliability of Synthpop model in generating high fidelity data for biochar properties such as surface morphology, elemental composition, and surface functional groups. Specifically, up to 70.8 % of features for bagasse biochar and 66.7 % of features for bamboo biochar exhibited low relative errors (<5 %). Overall, this work introduced an applicable framework and a reliable Synthpop model for synthesizing pyrolyzed biochar properties, providing insights into rapid screening of application-specific biochar. [Display omitted] •Generation framework for synthesizing biochar property data was established.•Missing records in reference dataset were imputed by MultipleMICE and MissForest.•Synthpop accurately captured multimodal, normal, and skewed distribution patterns.•Synthpop and TVAE preserved inter-feature correlations within synthetic data.•Synthpop was a reliable model to generate high fidelity data for biochar property. Biochar properties are governed by trade-offs among feedstock types, modification methods, and pyrolysis conditions, complicating the design of engineered biochar for specific applications. In this study, four data generative models, including Tabular Generative Adversarial Network (TGAN), Conditional Tabular Generative Adversarial Network (CTGAN), Tabular Variational Autoencoder (TVAE), and statistical Synthpop, were developed to predict biochar properties using MultipleMICE- and MissForest-imputed reference datasets (n = 461). The Synthpop model outperformed others in synthetic data quality, achieving high distribution similarity score (0.97) by accurately capturing multimodal, normal, and skewed distribution patterns for both continuous features (KSComplement = 0.98) and categorical variables (TVComplement = 0.95). Correlation trends evaluation indicated that TVAE and Synthpop models effectively preserved inter-feature dependencies between synthetic and original real data. Experimental validation with bagasse- and bamboo-derived biochar confirmed the reliability of Synthpop model in generating high fidelity data for biochar properties such as surface morphology, elemental composition, and surface functional groups. Specifically, up to 70.8 % of features for bagasse biochar and 66.7 % of features for bamboo biochar exhibited low relative errors (<5 %). Overall, this work introduced an applicable framework and a reliable Synthpop model for synthesizing pyrolyzed biochar properties, providing insights into rapid screening of application-specific biochar. |
| ArticleNumber | 133207 |
| Author | Han, Wei Zhou, Rongbing Huang, Jingang Xu, Xiaobin Wang, Ruilin Li, Weishuai Yang, Yang Qiu, Shanshan |
| Author_xml | – sequence: 1 givenname: Yang surname: Yang fullname: Yang, Yang organization: College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, PR China – sequence: 2 givenname: Weishuai surname: Li fullname: Li, Weishuai organization: College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, PR China – sequence: 3 givenname: Jingang orcidid: 0000-0002-5736-6148 surname: Huang fullname: Huang, Jingang email: hjg@hdu.edu.cn organization: College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, PR China – sequence: 4 givenname: Ruilin surname: Wang fullname: Wang, Ruilin organization: College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, PR China – sequence: 5 givenname: Wei surname: Han fullname: Han, Wei organization: College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, PR China – sequence: 6 givenname: Rongbing surname: Zhou fullname: Zhou, Rongbing organization: College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, PR China – sequence: 7 givenname: Shanshan surname: Qiu fullname: Qiu, Shanshan organization: College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, PR China – sequence: 8 givenname: Xiaobin surname: Xu fullname: Xu, Xiaobin organization: China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing, Hangzhou Dianzi University, Hangzhou 310018, PR China |
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| Keywords | Pyrolysis Synthpop Biochar Generative models Synthetic data |
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•Generation framework for synthesizing biochar property data was established.•Missing records in reference dataset were imputed by... Biochar properties are governed by trade-offs among feedstock types, modification methods, and pyrolysis conditions, complicating the design of engineered... |
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| Title | Tabular generative modeling framework for multi-property data synthesis of pyrolyzed biochar |
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