Establishing rheological models of lignin-based solutions via molecular parameters using machine learning

Abstract

The rheological models of natural polymer-based solutions are difficult to be established because of the significant non-Newtonian behavior and highly discrete rheological data caused by different molecular parameters including the molecular weights and size of clusters. In this study, a typical natural polymer-lignin was selected and dissolved in polyethylene glycol (PEG) as the lignin-based solutions. The experimental rheological data of different PEG-lignin solutions were trained with machine learning. The rheological models were established considering the molecular parameters including the molecular weights and size of clusters. The models show a high accuracy in predicting the viscosities of different PEG-lignin solutions with the coefficient of determination over 0.9815, mean absolute error less than 0.0132, and average absolute relative deviation less than 6.95 % in both Newtonian and non-Newtonian regimes. The models and relevant methodology can provide scenarios for further application of natural polymer solutions in process industries.