Artificial neural network for quantitative and qualitative determination of the viscosity of nanofluids by ATR-FTIR spectrometry

The multivariate data analysis refers to the process by which determination and classification of new unknown samples are made by accurate and economically practicable methods. In this paper, new analytical methods were utilized as a fast analytical method for regression and classification of polyacrylamide-nano silica (PAM-SiO2) as an influential factor in the rheological performance of the nanofluids, utilizing attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and chemometric methods. The effect of nanoparticle concentration on the viscosity measurement was determined. The spectral data were used for determination of viscosity value according to back-propagation artificial neural network (BP-ANN) algorithm. The root mean square errors (RMSEs) of model and test set in BP-ANN method were 0.051, and 3.548, respectively. In classification model, the ATR-FTIR spectral data were applied for analysis by using the counter-propagation artificial neural networks (CP-ANN) for classification of PAM-SiO2 nanofluids. The samples were classified based on the effect of nanoparticle concentration on viscosity values. The correct classification of the four classes was obtained by using CP-ANN modelling procedure. This work attempts to propose an analytical method to regression and classification viscosity of real-world data sets.

Automated summary

The study utilizes ATR-FTIR spectroscopy and chemometric methods to analyze the influential factors of rheological performance of PAM-SiO2 nanofluids and proposes an analytical method for regression and classification of viscosity values in real-world datasets.