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\begin{document}

\title{Establishing Generalized Rheological Models of Lignin-based Solutions
via Molecular Parameters Using Machine Learning}



\author[1]{Zhongfan Luo}%
\author[1]{Jingjing Chen}%
\author[1]{Peishi Dong}%
\author[1]{Tonghuan Zhang}%
\author[1]{Danyang Cao}%
\author[2]{Yuanhui Ji}%
\author[3]{Xiaoyan Ji}%
\author[3]{Xin Feng}%
\author[1]{Jiahua Zhu}%
\author[1]{Xiaohua Lu}%
\author[3]{Liwen Mu}%
\affil[1]{Nanjing Tech University}%
\affil[2]{Southeast University}%
\affil[3]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\begin{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.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Manuscript-with highlights-FFF.docx`  available at \url{https://authorea.com/users/672315/articles/671440-establishing-generalized-rheological-models-of-lignin-based-solutions-via-molecular-parameters-using-machine-learning}

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