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Generation of Correlations for Dynamic Viscosity, Heat Capacity, Heat Capacity Ratio and Thermal Conductivity of Opteon by the Application of the Proportional Nodes Data Fitting Method

Authors: *Mumah, S.N., Akande, H.F., Mudi, K.Y. And Samuel, F.

DOI Info: http://doi.org/10.5281/zenodo.10442787

ABSTRACT

Accurate correlations of data are essential requirements for the design, simulation and optimization of chemical processes. Various correlation generating methods for data fitting exist with various levels of complexity and accuracy. This paper presents a new method – proportional node curve fitting method (PNCFM) for correlating data for surfaces, that is, with two dependent variables. It is based on the precise estimation of correlations for the boundary curves and the variation of the nodes at selected points of the other curves within the boundary. When compared with generally used method like the double Chebyshev polynomials method, it is found to be less complex and the generated correlations are much simpler and with higher accuracy. The method has been used to develop correlations for four properties (dynamic viscosity, heat capacity, heat capacity ratio (Cp/Cv), and thermal conductivity) for saturated and superheated Opteon, a refrigerant used for automotive air-conditioners. The data generated from the correlations for the superheated vapour have satisfactory average percentage deviation from actual data (less than ±0.084%, ±0.0156%, ±0.37% and ±0.056% for dynamic viscosity, heat capacity, heat capacity ratio (Cp/Cv), and thermal conductivity, respectively). In addition, the data generated from the correlations for the saturated vapour have satisfactory coefficient of determination R2, (very close to 1.0). The method can be used to develop correlations for non-overlapping data with 2 dependent variables.


Affiliations: Department of Chemical Engineering, College of Engineering, Kaduna Polytechnic, Kaduna, Nigeria.
Keywords: Correlations, Proportional Nodes, Curve Fitting, Opteon, Properties
Published date: 2023/12/30

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ISSN: 2635-3342 (Print)

ISSN: 2635-3350 (Online)

DOI: In progress

ISI Impact Factor: In progress

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