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Kinematic Viscosity Prediction Guide: Reviewing and Evaluating Empirical Models for Diesel Fractions, and Biodiesel–Diesel Blends According to the Temperature and Feedstock

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Abstract

Experimental analysis of viscosity can be a straightforward and inexpensive analysis for few samples. However, in industrial processes that have high demands of properties measurements, the determination of viscosity and other properties involves time-consuming with sampling, analysis, and availability of results. Also in refineries, the sampling routines for experimental determination of the viscosity of streams are not enough to represent variations that occur in the process, such as the shift of an oil tank in distillation units. In addition, besides requiring cost of operating personnel and laboratory analyst, all of these steps can take up to one shift until the result is available. Therefore, as an alternative, the use of predictive methods of kinematic viscosity are essential. Empirical methods have been used in simulations and design calculations of streams and mixture at industries regarding kinematic viscosity (KV) of petroleum fractions and fuels at different temperatures. However, there are uncertainties about the most accurate method to use at specific condition (temperature, feedstock, volume fraction) which might affect the KV prediction of fuels with unknown composition. Therefore, we assembled and evaluated several methods to predict KV of different diesel systems. In addition, new methods for predicting KV of diesel fractions at several temperatures were also developed for improving the estimation accuracy. As a result, we developed a guide with suggestions of the most accurate models to be applied for diesel fraction from assays, diesel fractions S500 from blend system at several temperatures, and biodiesel–diesel blends at different temperatures, volume fractions, and feedstock.

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Abbreviations

KV:

Kinematic viscosity

K API :

API factor

ν T :

Kinematic viscosity at T temperature

d 15 :

Density at 15 °C

API :

API gravity

T b :

Boiling point

A, B :

Parameters for liquid related to viscosity

Z :

Parameter for viscosity temperature equation

w:

Mass fraction

V:

Volume fraction

x:

Mole fraction

M:

Molar mass

VBI :

Viscosity blend index

ν m :

Mixture kinematic viscosity

AAD:

Average absolute deviation

% AAD:

Percentage average absolute deviation

ν D :

Diluent viscosity for Miadonye et al. model

ν B :

Bitumen viscosity for Miadonye et al. model

ρ A, ρ B :

Density for fluid A and B for Shu model

T10, T30, T50:

Temperature at 10, 30, and 50 vol% distilled

T:

Temperature

FAME:

Fatty acid methyl esters

FAEE:

Fatty acid ethyl esters

C:

Walther parameter

α :

Alpha, a parameter from Lederer model

a, n:

Constants from Miadonye additional parameter model

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Acknowledgements

We thank Petrobras and ANP for providing experimental data and financial support. This study was developed within the scope of the Research, Development, and Innovation program for the petroleum sector, regulated by ANP, under the title “Análise integrada de indicadores de eficiência energética, SMS, econômicos e de processo através do contrato para Modernização e incorporação de novas funcionalidades ao software CalcProc—Suprimento de Macros para cálculo de Processamento de Óleo e Gás Calcproc (“SISTEMA”),” Project ANP Number 20792-8, with Petrobras as the project proponent. We also thank the financial support from Agência Nacional do Petróleo, Gás Natural e Biocombustíveis—ANP—by the ANP Human Resources Program for the Petroleum and Gas Sector—PRH-ANP Convênio FINEP/FUNCAMP, no 29.1, Ref. 5485.

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  1. School of Chemical Engineering, University of Campinas, Campinas, Brazil

    Shella M. Santos, Maria R. W. Maciel & Leonardo V. Fregolente

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  1. Shella M. Santos
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Santos, S.M., Maciel, M.R.W. & Fregolente, L.V. Kinematic Viscosity Prediction Guide: Reviewing and Evaluating Empirical Models for Diesel Fractions, and Biodiesel–Diesel Blends According to the Temperature and Feedstock. Int J Thermophys 42, 121 (2021). https://doi.org/10.1007/s10765-021-02868-z

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