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Establishing Generalized Rheological Models of Lignin-based Solutions via Molecular Parameters Using Machine Learning
  • +8
  • Zhongfan Luo,
  • Jingjing Chen,
  • Peishi Dong,
  • Tonghuan Zhang,
  • Danyang Cao,
  • Yuanhui Ji,
  • Xiaoyan Ji,
  • Xin Feng,
  • Jiahua Zhu,
  • Xiaohua Lu,
  • Liwen Mu
Zhongfan Luo
Nanjing Tech University
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Jingjing Chen
Nanjing Tech University
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Peishi Dong
Nanjing Tech University
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Tonghuan Zhang
Nanjing Tech University
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Danyang Cao
Nanjing Tech University
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Yuanhui Ji
Southeast University
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Xiaoyan Ji
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Jiahua Zhu
Nanjing Tech University
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Xiaohua Lu
Nanjing Tech University
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Liwen Mu

Corresponding Author:[email protected]

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Abstract

The rheological properties of natural polymer solutions are difficult to be modeled universally because of the strongly nonlinear relations between viscosities and the external factors and the discreteness of the rheological data owning to different molecular parameters including the molecular weights and size of clusters from different types of natural polymers and solvents. In this study, a typical natural polymer-lignin was selected and dissolved in polyethylene glycol (PEG). The rheological properties of different PEG-lignin solutions (PEG-Ls) and the molecular parameters of the pretreated lignin were tested. Subsequently, machine learning was applied to establish the generalized models considering the molecular parameters. The models were successfully developed in Newtonian and non-Newtonian regimes for PEG-Ls with correlation coefficients of 0.982 and 0.980, respectively. The models and relevant methodology can provide scenarios for further application of natural polymer solutions.
06 Oct 2023Submitted to AIChE Journal
08 Oct 2023Assigned to Editor
08 Oct 2023Submission Checks Completed
08 Oct 2023Review(s) Completed, Editorial Evaluation Pending
22 Oct 2023Reviewer(s) Assigned