Viktor Martinek, Ian Bell, Roland Herzog, Markus Richter and Xiaoxian Yang*,
{"title":"Entropy Scaling of Viscosity IV─Application to 124 Industrially Important Fluids","authors":"Viktor Martinek, Ian Bell, Roland Herzog, Markus Richter and Xiaoxian Yang*, ","doi":"10.1021/acs.jced.4c0045110.1021/acs.jced.4c00451","DOIUrl":null,"url":null,"abstract":"<p >In our previous work [<contrib-group><span>Yang, X.</span></contrib-group> <cite><i>J. Chem. Eng. Data</i></cite> <span>2021</span>, <em>66</em>, 1385–1398], a residual entropy scaling (RES) approach was developed to link viscosity to residual entropy using a 4-term power function for 39 refrigerants. In further research [<contrib-group><span>Yang, X.</span></contrib-group> <cite><i>Int. J. Thermophys.</i></cite> <span>2022</span>, <em>43</em>, 183], this RES approach was extended to 124 pure fluids containing fluids from light gases (hydrogen and helium) to dense fluids (e.g., heavy hydrocarbons) and fluids with strong association force (e.g., water). In these previous research studies, the model was developed by manual optimization of the power function. The average absolute relative deviation (AARD) of experimental data from the RES model is approximately 3.36%, which is higher than the 2.74% obtained with the various models in REFPROP 10.0. In the present work, the power function was optimized by iteratively fitting the global (fluid-independent power terms) and local parameters (fluid-specific and group-specific parameters) and screening the experimental data. The resulting equation has only three terms instead of four. Most notably, the AARD of the new RES model is reduced down to 2.76%; this is very close to the various multiparameter models in REFPROP 10.0, while the average relative deviation (ARD) amounts to 0.03%, which is smaller than REFPROP 10.0’s 0.7%. A Python package is provided for the use of the developped model.</p>","PeriodicalId":42,"journal":{"name":"Journal of Chemical & Engineering Data","volume":"70 2","pages":"727–742 727–742"},"PeriodicalIF":2.0000,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acs.jced.4c00451","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical & Engineering Data","FirstCategoryId":"1","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.jced.4c00451","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
In our previous work [Yang, X.J. Chem. Eng. Data2021, 66, 1385–1398], a residual entropy scaling (RES) approach was developed to link viscosity to residual entropy using a 4-term power function for 39 refrigerants. In further research [Yang, X.Int. J. Thermophys.2022, 43, 183], this RES approach was extended to 124 pure fluids containing fluids from light gases (hydrogen and helium) to dense fluids (e.g., heavy hydrocarbons) and fluids with strong association force (e.g., water). In these previous research studies, the model was developed by manual optimization of the power function. The average absolute relative deviation (AARD) of experimental data from the RES model is approximately 3.36%, which is higher than the 2.74% obtained with the various models in REFPROP 10.0. In the present work, the power function was optimized by iteratively fitting the global (fluid-independent power terms) and local parameters (fluid-specific and group-specific parameters) and screening the experimental data. The resulting equation has only three terms instead of four. Most notably, the AARD of the new RES model is reduced down to 2.76%; this is very close to the various multiparameter models in REFPROP 10.0, while the average relative deviation (ARD) amounts to 0.03%, which is smaller than REFPROP 10.0’s 0.7%. A Python package is provided for the use of the developped model.
期刊介绍:
The Journal of Chemical & Engineering Data is a monthly journal devoted to the publication of data obtained from both experiment and computation, which are viewed as complementary. It is the only American Chemical Society journal primarily concerned with articles containing data on the phase behavior and the physical, thermodynamic, and transport properties of well-defined materials, including complex mixtures of known compositions. While environmental and biological samples are of interest, their compositions must be known and reproducible. As a result, adsorption on natural product materials does not generally fit within the scope of Journal of Chemical & Engineering Data.