Estimating the Physical Properties of Nanofluids Using a Connectionist Intelligent Model Known as Gaussian Process Regression Approach
This work aims to develop a robust machine learning model for the prediction of the relative viscosity of nanoparticles (NPs) including Al2O3, TiO2, SiO2, CuO, SiC, and Ag based on the most important input parameters affecting them covering the size, concentration, thickness of the interfacial layer...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | International Journal of Chemical Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/1017341 |
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| author | Tzu-Chia Chen Ali Thaeer Hammid Avzal N. Akbarov Kaveh Shariati Mina Dinari Mohammed Sardar Ali |
| author_facet | Tzu-Chia Chen Ali Thaeer Hammid Avzal N. Akbarov Kaveh Shariati Mina Dinari Mohammed Sardar Ali |
| author_sort | Tzu-Chia Chen |
| collection | DOAJ |
| description | This work aims to develop a robust machine learning model for the prediction of the relative viscosity of nanoparticles (NPs) including Al2O3, TiO2, SiO2, CuO, SiC, and Ag based on the most important input parameters affecting them covering the size, concentration, thickness of the interfacial layer, and intensive properties of NPs. In order to develop a comprehensive artificial intelligence model in this study, sixty-nine data samples were collected. To this end, the Gaussian process regression approach with four basic function kernels (Matern, squared exponential, exponential, and rational quadratic) was exploited. It was found that Matern outperformed other models with R2 = 0.987, MARE (%) = 6.048, RMSE = 0.0577, and STD = 0.0574. This precise yet simple model can be a good alternative to the complex thermodynamic, mathematical-analytical models of the past. |
| format | Article |
| id | doaj-art-6373aa8b21a54f6dbd9d78f64d99727e |
| institution | Kabale University |
| issn | 1687-8078 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Chemical Engineering |
| spelling | doaj-art-6373aa8b21a54f6dbd9d78f64d99727e2025-02-03T05:50:01ZengWileyInternational Journal of Chemical Engineering1687-80782022-01-01202210.1155/2022/1017341Estimating the Physical Properties of Nanofluids Using a Connectionist Intelligent Model Known as Gaussian Process Regression ApproachTzu-Chia Chen0Ali Thaeer Hammid1Avzal N. Akbarov2Kaveh Shariati3Mina Dinari4Mohammed Sardar Ali5Department of Industrial Engineering and ManagementComputer Engineering Techniques DepartmentHead of the Department of Faculty Orthopedic DentistryDepartment of Chemical EngineeringDepartment of LawDepartment of Information TechnologyThis work aims to develop a robust machine learning model for the prediction of the relative viscosity of nanoparticles (NPs) including Al2O3, TiO2, SiO2, CuO, SiC, and Ag based on the most important input parameters affecting them covering the size, concentration, thickness of the interfacial layer, and intensive properties of NPs. In order to develop a comprehensive artificial intelligence model in this study, sixty-nine data samples were collected. To this end, the Gaussian process regression approach with four basic function kernels (Matern, squared exponential, exponential, and rational quadratic) was exploited. It was found that Matern outperformed other models with R2 = 0.987, MARE (%) = 6.048, RMSE = 0.0577, and STD = 0.0574. This precise yet simple model can be a good alternative to the complex thermodynamic, mathematical-analytical models of the past.http://dx.doi.org/10.1155/2022/1017341 |
| spellingShingle | Tzu-Chia Chen Ali Thaeer Hammid Avzal N. Akbarov Kaveh Shariati Mina Dinari Mohammed Sardar Ali Estimating the Physical Properties of Nanofluids Using a Connectionist Intelligent Model Known as Gaussian Process Regression Approach International Journal of Chemical Engineering |
| title | Estimating the Physical Properties of Nanofluids Using a Connectionist Intelligent Model Known as Gaussian Process Regression Approach |
| title_full | Estimating the Physical Properties of Nanofluids Using a Connectionist Intelligent Model Known as Gaussian Process Regression Approach |
| title_fullStr | Estimating the Physical Properties of Nanofluids Using a Connectionist Intelligent Model Known as Gaussian Process Regression Approach |
| title_full_unstemmed | Estimating the Physical Properties of Nanofluids Using a Connectionist Intelligent Model Known as Gaussian Process Regression Approach |
| title_short | Estimating the Physical Properties of Nanofluids Using a Connectionist Intelligent Model Known as Gaussian Process Regression Approach |
| title_sort | estimating the physical properties of nanofluids using a connectionist intelligent model known as gaussian process regression approach |
| url | http://dx.doi.org/10.1155/2022/1017341 |
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