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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Vydané v:Bioresource technology Ročník 438; s. 133207
Hlavní autori: Yang, Yang, Li, Weishuai, Huang, Jingang, Wang, Ruilin, Han, Wei, Zhou, Rongbing, Qiu, Shanshan, Xu, Xiaobin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Elsevier Ltd 01.12.2025
Predmet:
Biochar
Cellulose - chemistry
Charcoal - chemistry
Generative models
Models, Theoretical
Pyrolysis
Synthetic data
Synthpop
Pyrolysis
Synthpop
Biochar
Generative models
Synthetic data
ISSN:0960-8524, 1873-2976, 1873-2976
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Shrnutí:[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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0960-8524
1873-2976
1873-2976
DOI:10.1016/j.biortech.2025.133207