Validation Studies for the Viscosity Blending Rules for Lubricant Oil Mixtures

Accurate prediction of mixture viscosity during lubricant oil mixing or blending processes is crucial in oil packaging industries, as it is a key indicator for maintaining product specifications and quality. Furthermore, it can guide best practices which lead to both operational efficiency and cost reduction [1]. The petroleum and petrochemical sector employ multi-product pipelines to transport numerous products. A common challenge is product cross contamination caused by the batch mode of transport resulting in the formation of mixed oils, regarded as low-grade products. Thus, reliable models to accurately predict the mixture viscosity of a binary system are essential for informing when the desired product viscosities are met.

In this study, we seek to validate several existing mixture viscosities blending correlations that will be used in detailed process operation and control studies. The viscosity is measured using a CANNON CT-1000 Constant Temperature Bath, Ubbelohde viscometer and a timer. We formulated a blend of binary oils with mass fraction ranging from 0 to 1 and measured the mixture viscosities using the ASTM D445 procedure [2]. In this procedure, the blend viscosities are measured at 40 °C using separate measurements to prove the reproducibility and reliability of each measurement. The existing correlations used in this study include the Kendall–Monroe model [7], Refutas model [8], ASTM D-7152 model [4], Double logarithmic model [5], Lederer model [3], and Exponential mixing models [6]. We evaluated each model using the mixture viscosity data. We determined that the Kendall–Monroe and Lederer models consistently demonstrated the best agreement with measured values across all sample sets. In this study we give an example of the importance of validating and selecting correlations with good reliability. Ultimately, adopting the most appropriate viscosity blending rule can lead to significant improvements in overall process operations and estimations, operational efficiency, and overall decision-making.

References

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