Physics-guided clustering and transfer-enhanced cGAN data augmentation for robust biomass gasification product prediction

Yifei Wu; Xiaojun Yang; Zongwei Zhang; Shuiting Ding

doi:10.1016/j.biombioe.2026.109350

journal article Oct 01, 2026

Physics-guided clustering and transfer-enhanced cGAN data augmentation for robust biomass gasification product prediction

Yifei Wu

Xiaojun Yang

Zongwei Zhang Shuiting Ding

Biomass and Bioenergy Vol. 213 pp. 109350 · Elsevier BV

View at Publisher Save 10.1016/j.biombioe.2026.109350

Topics

No keywords indexed for this article. Browse by subject →

References

59

[1]

Song "Recent development of biomass gasification for H2 rich gas production" Appl. Energy Combust. Sci. (2022)

[2]

Mujtaba "Lignocellulosic biomass from agricultural waste to the circular economy: a review with focus on biofuels, biocomposites and bioplastics" J. Clean. Prod. (2023) 10.1016/j.jclepro.2023.136815

[3]

Wang "Technologies and perspectives for achieving carbon neutrality" Innovation (2021)

[4]

Yin (2025)

[5]

Zhuang "Development status and challenges of biomass energy utilization technology under background of emission peak and carbon neutrality strategy in China" Sol. Energy (2024)

[6]

Glushkov "Current status of the pyrolysis and gasification mechanism of biomass" Energies (2021) 10.3390/en14227541

[7]

Unveiling the potential of pyrolysis-gasification for hydrogen-rich syngas production from biomass and plastic waste

Rahul Mishra, Chi-Min Shu, Anjani R.K. Gollakota et al.

Energy Conversion and Management 2024 10.1016/j.enconman.2024.118997

[8]

Yi "The yield improvement of three state products from biomass pyrolysis based on machine learning" Chem. Ind. For. Prod. (2023)

[9]

Kumar "A review on the modelling of hydrothermal liquefaction of biomass and waste feedstocks" Energy Nexus (2022) 10.1016/j.nexus.2022.100042

[10]

Qiu "Construction of hydrothermal liquefaction system for efficient production of biomass-derived furfural: solvents, catalysts and mechanisms" Fuel (2023) 10.1016/j.fuel.2023.129278

[11]

Bradbury "A kinetic model for pyrolysis of cellulose" J. Appl. Polym. Sci. (1979) 10.1002/app.1979.070231112

[12]

Ranzi "Chemical kinetics of biomass pyrolysis" Energy Fuels (2008) 10.1021/ef800551t

[13]

Ranzi "Mathematical modeling of fast biomass pyrolysis and bio-oil formation. note i: kinetic mechanism of biomass pyrolysis" ACS Sustain. Chem. Eng. (2017) 10.1021/acssuschemeng.6b03096

[14]

Yan "Research progress on key mechanisms of biomass gasification based on density functional theory simulation" Sol. Energy (2025)

[15]

Zhang "Machine learning for hydrothermal treatment of biomass: a review" Bioresour. Technol. (2023) 10.1016/j.biortech.2022.128547

[16]

Meena "Production of biofuels from biomass: predicting the energy employing artificial intelligence modelling" Bioresour. Technol. (2021) 10.1016/j.biortech.2021.125642

[17]

Ai "Prediction and optimization design of porous structure properties of biomass-derived biochar using machine learning methods" Energy (2024) 10.1016/j.energy.2024.133707

[18]

Alsaeedi "A review of biomass energy and gasification: bibliometrics, hotspots and emerging trends" Biomass Bioenergy (2026) 10.1016/j.biombioe.2025.108797

[19]

Selwal "Emerging technologies in biomass conversion: bioengineering and nanocatalysts to ai-driven process optimization" Biomass Bioenergy (2025) 10.1016/j.biombioe.2025.108054

[20]

Cardarelli "Kinetic analysis and prediction modeling by advanced machine learning of pyrolysis of dairy cattle manure from conventional and organic systems" Biomass Bioenergy (2025) 10.1016/j.biombioe.2025.108247

[21]

Ervilha "Bayesian optimization-guided machine learning models for bio-oil yield prediction from lignocellulosic biomass pyrolysis" Biomass Bioenergy (2025) 10.1016/j.biombioe.2025.108280

[22]

Kristanto "Enhancing machine learning prediction of biomass pyrolysis yields through taxonomy-based preprocessing and rigorous model tuning" Biomass Bioenergy (2026) 10.1016/j.biombioe.2025.108524

[23]

Garg "Advances in ai-driven biomass processing: a review of conversion technologies, optimization strategies, and smart energy integration" ACS Omega (2025) 10.1021/acsomega.5c05427

[24]

Ascher "Machine learning methods for modelling the gasification and pyrolysis of biomass and waste" Renew. Sustain. Energy Rev. (2022) 10.1016/j.rser.2021.111902

[25]

Song "Comprehensive experimental assessment of biomass steam gasification with different types: correlation and multiple linear regression analysis with feedstock characteristics" Renew. Energy (2024) 10.1016/j.renene.2024.121649

[26]

Grossi "Introduction to artificial neural networks" Eur. J. Gastroenterol. Hepatol. (2007) 10.1097/meg.0b013e3282f198a0

[27]

Random Forests

Leo Breiman

Machine Learning 2001 10.1023/a:1010933404324

[28]

Support vector machines

M.A. Hearst, S.T. Dumais, E. Osuna et al.

IEEE Intelligent Systems and their Applications 1998 10.1109/5254.708428

[29]

Sakheta "Improved prediction of biomass gasification models through machine learning" Comput. Chem. Eng. (2024) 10.1016/j.compchemeng.2024.108834

[30]

Qi "Biomass hydrothermal gasification characteristics study: based on deep learning for data generation and screening strategies" Energy (2024) 10.1016/j.energy.2024.133492

[31]

Goodfellow "Generative adversarial nets" Adv. Neural Inf. Process. Syst. (2014)

[32]

Hong "Conditional generative adversarial network for structured domain adaptation" (2018)

[33]

Ma "Hydrogen yield prediction for supercritical water gasification based on generative adversarial network data augmentation" Appl. Energy (2023) 10.1016/j.apenergy.2023.120814

[34]

Okolie "Can biomass structural composition be predicted from a small dataset using a hybrid deep learning approach" Ind. Crop. Prod. (2023) 10.1016/j.indcrop.2023.117191

[35]

Cai "Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest" Appl. Energy (2020) 10.1016/j.apenergy.2020.114566

[36]

Gradient boosting for extreme quantile regression

Jasper Velthoen, Clément Dombry, Juan-Juan Cai et al.

Extremes 2023 10.1007/s10687-023-00473-x

[37]

Smith "A comparison of random forest regression and multiple linear regression for prediction in neuroscience" J. Neurosci. Methods (2013) 10.1016/j.jneumeth.2013.08.024

[38]

Yavari "Solution gas-oil ratio estimation using histogram gradient boosting regression, machine learning, and mathematical models: a comparative analysis" Energy Sources, Part A Recovery, Util. Environ. Eff. (2024)

[39]

Zhang "Gan-maml strategy for biomass energy production: overcoming small dataset limitations" Appl. Energy (2025) 10.1016/j.apenergy.2025.125568

[40]

Finn "Model-agnostic meta-learning for fast adaptation of deep networks" (2017)

[41]

Sankaranarayanan "Learning from synthetic data: addressing domain shift for semantic segmentation" (2018)

[42]

Luo "Taking a closer look at domain shift: category-level adversaries for semantics consistent domain adaptation" (2019)

[43]

The k-means Algorithm: A Comprehensive Survey and Performance Evaluation

Mohiuddin Ahmed, Raihan Seraj, Syed Mohammed Shamsul Islam

Electronics 2020 10.3390/electronics9081295

[44]

Savaresi "A comparative analysis on the bisecting k-means and the pddp clustering algorithms" Intell. Data Anal. (2004) 10.3233/ida-2004-8403

[45]

Woldesellasse "Data augmentation using conditional generative adversarial network (cGAN): application for prediction of corrosion pit depth and testing using neural network" J. Pipeline Sci. Eng. (2023) 10.1016/j.jpse.2022.100091

[46]

Performance Comparison of New Adjusted Min-Max with Decimal Scaling and Statistical Column Normalization Methods for Artificial Neural Network Classification

Saichon Sinsomboonthong

International Journal of Mathematics and Mathemati... 2022 10.1155/2022/3584406

[47]

Maaten "Visualizing data using t-sne" J. Mach. Learn. Res. (2008)

[48]

Liu "Pay attention to mlps" Adv. Neural Inf. Process. Syst. (2021)

[49]

Chen "Dynamic relu" (2020)

[50]

Kyurkchiev (2015)

Showing 50 of 59 references

Metrics

0

Citations

59

References

Details

Published: Oct 01, 2026
Vol/Issue: 213
Pages: 109350
License: View

Authors

Y

Yifei Wu

Technology R&D Department Fujian Longking Co., Ltd. Longyan 364000 China

Division of Nephrology Renmin Hospital of Wuhan University Wuhan China; Nephrology and Urology Research Institute of Wuhan University Wuhan China

S

Shuiting Ding

Funding

Tianjin Municipal Education Commission Award: 2024KJ106

Cite This Article

Yifei Wu, Xiaojun Yang, Zongwei Zhang, et al. (2026). Physics-guided clustering and transfer-enhanced cGAN data augmentation for robust biomass gasification product prediction. Biomass and Bioenergy, 213, 109350. https://doi.org/10.1016/j.biombioe.2026.109350

Physics-guided clustering and transfer-enhanced cGAN data augmentation for robust biomass gasification product prediction

You May Also Like