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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

دليل التنبؤ باللزوجة الحركية: مراجعة وتقييم النماذج التجريبية لكسور الديزل وخلائط الديزل الحيوي والديزل وفقًا لدرجة الحرارة والمواد الخام
Authors: Shella M. Santos; Maria Regina Wolf Maciel; Leonardo Vasconcelos Fregolente;

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

Abstract

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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Keywords

Composite material, Pulp and paper industry, Fraction (chemistry), Volume (thermodynamics), Technical Aspects of Biodiesel Production, Biomedical Engineering, FOS: Mechanical engineering, Organic chemistry, FOS: Medical engineering, Fuel Chemistry, Environmental science, Catalysis, Filter (signal processing), Engineering, Tribological Properties of Lubricants and Additives, Waste management, FOS: Chemical engineering, Distillation, Fluid Flow and Transfer Processes, Chromatography, Viscosity, Chemical Kinetics of Combustion Processes, Mechanical Engineering, Physics, Chemical Engineering, Sampling (signal processing), Computer science, Raw material, Materials science, Chemistry, Physical Sciences, Process engineering, Thermodynamics, Computer vision, Biodiesel, Diesel fuel

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    popularity
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    influence
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
hybrid