On modelling of surface tension of CMC-α-Fe2O3 nanoparticles by fuzzy-hybrid approach: A comparison study

Surface tension is one of the most important rheological parameters of nanoliquids. It influences the thermophysical and mass transfer properties of nanostructures. Accurate estimation of the surface tension from operating variables is critical for determining optimal production processes. However, the challenges of producing nanoparticles and measuring their properties introduce experimental errors in the data used for mathematical modelling. Crisp regression approaches provide adequate representation of the data, but they do not provide information about the experimental uncertainty. In this study, a fuzzy-hybrid approach is proposed for mathematical modelling of surface tension of carboxymethyl cellulose/chitosan-alpha-Fe2O3 nanoparticles. Then, the proposed model is compared with a crisp model from a previous study. Error analysis is conducted to validate the constructed fuzzy model. It is observed that the fuzzy-hybrid modelling approach has yielded significantly lower error values (a 60%-90% improvement in all error metrics on average), and thus, it is superior to the crisp approach. This study contributes to the subject of modelling rheological properties. It is shown that the fuzzy-hybrid approach has impressive potential to be utilized for modelling the rheological properties of nanostructures.

Automated summary

Surface tension is a crucial rheological parameter in nanoliquids, affecting the thermophysical and mass transfer properties of nanostructures. Accurately estimating surface tension is essential for optimizing production processes. However, experimental errors in data used for mathematical modeling due to challenges in nanoparticle production and property measurement exist. This study proposes a fuzzy-hybrid approach to model surface tension of carboxymethyl cellulose/chitosan-alpha-Fe2O3 nanoparticles, which outperforms a crisp approach in terms of error metrics. This research contributes to the field of modeling rheological properties and highlights the potential of using the fuzzy-hybrid approach for nanoscale rheological modeling.