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Source Tracing of Raw Material Components in Wood Vinegar Distillation Process Based on Machine Learning and Aspen Simulation

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Source Tracing of Raw Material Components in Wood Vinegar Distillation Process Based on Machine Learning and Aspen Simulation. / Liao, Siqi; Sun, Wanting; Zheng, Haoran et al.
In: ChemEngineering, Vol. 9, No. 2, 32, 13.03.2025.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Liao, S, Sun, W, Zheng, H & Xu, Q 2025, 'Source Tracing of Raw Material Components in Wood Vinegar Distillation Process Based on Machine Learning and Aspen Simulation', ChemEngineering, vol. 9, no. 2, 32. https://doi.org/10.3390/chemengineering9020032

APA

Liao, S., Sun, W., Zheng, H., & Xu, Q. (2025). Source Tracing of Raw Material Components in Wood Vinegar Distillation Process Based on Machine Learning and Aspen Simulation. ChemEngineering, 9(2), Article 32. https://doi.org/10.3390/chemengineering9020032

Vancouver

Liao S, Sun W, Zheng H, Xu Q. Source Tracing of Raw Material Components in Wood Vinegar Distillation Process Based on Machine Learning and Aspen Simulation. ChemEngineering. 2025 Mar 13;9(2):32. doi: 10.3390/chemengineering9020032

Author

Liao, Siqi ; Sun, Wanting ; Zheng, Haoran et al. / Source Tracing of Raw Material Components in Wood Vinegar Distillation Process Based on Machine Learning and Aspen Simulation. In: ChemEngineering. 2025 ; Vol. 9, No. 2.

Bibtex

@article{2f03e8df2a524cc58856ab3f3560e5f3,
title = "Source Tracing of Raw Material Components in Wood Vinegar Distillation Process Based on Machine Learning and Aspen Simulation",
abstract = "As a kind of high-oxygen organic liquid produced during biomass pyrolysis, wood vinegar possesses significant industrial value due to its rich composition of acetic acid, phenols, and other bioactive compounds. In this study, we explore the application of advanced machine learning models in optimizing the dual-column distillation process for wood vinegar production, such as Random Forest algorithms. Through the integration of Aspen Plus simulation and deep learning, an adaptive control strategy is proposed to enhance the separation efficiency of key components under varying feed conditions. The experimental results demonstrate that the Random Forest model exhibits superior predictive accuracy to traditional decision tree methods, and an R2 of 0.9728 can be achieved for phenol concentration prediction. This AI-driven system can provide real-time process optimization, enhancing energy efficiency, stabilizing component yields, and contributing to the advancement of sustainable practices within the biomass chemical industry. These findings are anticipated to offer valuable insights into the integration of green chemistry principles with intelligent control systems to facilitate the achievement of Industry 4.0 objectives in bio-based production.",
author = "Siqi Liao and Wanting Sun and Haoran Zheng and Qiyang Xu",
year = "2025",
month = mar,
day = "13",
doi = "10.3390/chemengineering9020032",
language = "English",
volume = "9",
journal = "ChemEngineering",
issn = "2305-7084",
publisher = "MDPI AG",
number = "2",

}

RIS

TY - JOUR

T1 - Source Tracing of Raw Material Components in Wood Vinegar Distillation Process Based on Machine Learning and Aspen Simulation

AU - Liao, Siqi

AU - Sun, Wanting

AU - Zheng, Haoran

AU - Xu, Qiyang

PY - 2025/3/13

Y1 - 2025/3/13

N2 - As a kind of high-oxygen organic liquid produced during biomass pyrolysis, wood vinegar possesses significant industrial value due to its rich composition of acetic acid, phenols, and other bioactive compounds. In this study, we explore the application of advanced machine learning models in optimizing the dual-column distillation process for wood vinegar production, such as Random Forest algorithms. Through the integration of Aspen Plus simulation and deep learning, an adaptive control strategy is proposed to enhance the separation efficiency of key components under varying feed conditions. The experimental results demonstrate that the Random Forest model exhibits superior predictive accuracy to traditional decision tree methods, and an R2 of 0.9728 can be achieved for phenol concentration prediction. This AI-driven system can provide real-time process optimization, enhancing energy efficiency, stabilizing component yields, and contributing to the advancement of sustainable practices within the biomass chemical industry. These findings are anticipated to offer valuable insights into the integration of green chemistry principles with intelligent control systems to facilitate the achievement of Industry 4.0 objectives in bio-based production.

AB - As a kind of high-oxygen organic liquid produced during biomass pyrolysis, wood vinegar possesses significant industrial value due to its rich composition of acetic acid, phenols, and other bioactive compounds. In this study, we explore the application of advanced machine learning models in optimizing the dual-column distillation process for wood vinegar production, such as Random Forest algorithms. Through the integration of Aspen Plus simulation and deep learning, an adaptive control strategy is proposed to enhance the separation efficiency of key components under varying feed conditions. The experimental results demonstrate that the Random Forest model exhibits superior predictive accuracy to traditional decision tree methods, and an R2 of 0.9728 can be achieved for phenol concentration prediction. This AI-driven system can provide real-time process optimization, enhancing energy efficiency, stabilizing component yields, and contributing to the advancement of sustainable practices within the biomass chemical industry. These findings are anticipated to offer valuable insights into the integration of green chemistry principles with intelligent control systems to facilitate the achievement of Industry 4.0 objectives in bio-based production.

U2 - 10.3390/chemengineering9020032

DO - 10.3390/chemengineering9020032

M3 - Journal article

VL - 9

JO - ChemEngineering

JF - ChemEngineering

SN - 2305-7084

IS - 2

M1 - 32

ER -