Developing a Robust Model Based on the Gaussian Process Regression Approach to Predict Biodiesel Properties
Biodiesel is assumed a renewable and environmentally friendly fuel that possesses the potential to substitute petroleum diesel. The basic purpose of the present study is to design a precise algorithm based on Gaussian Process Regression (GPR) model with several kernel functions, i.e., Rational Quadr...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Chemical Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/5650499 |
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| author | Inna Pustokhina Amir Seraj Hafsan Hafsan Seyed Mojtaba Mostafavi S. M. Alizadeh |
| author_facet | Inna Pustokhina Amir Seraj Hafsan Hafsan Seyed Mojtaba Mostafavi S. M. Alizadeh |
| author_sort | Inna Pustokhina |
| collection | DOAJ |
| description | Biodiesel is assumed a renewable and environmentally friendly fuel that possesses the potential to substitute petroleum diesel. The basic purpose of the present study is to design a precise algorithm based on Gaussian Process Regression (GPR) model with several kernel functions, i.e., Rational Quadratic, Squared Exponential, Matern, and Exponential, to estimate biodiesel properties. These properties include kinematic viscosity (KV), pour point (PP), iodine value (IV), and cloud point (CP) as a function of fatty acid composition. In order to develop this model, some variables are assumed, such as molecular weight, carbon number, double bond numbers, monounsaturated fatty acids, polyunsaturated fatty acid, weight percent of saturated acid, and temperature. The performance and efficiency of the GPR model are measured through several statistical criteria and the results are summarized in root mean square error (RMSE) and coefficients of determination (R2). R2 and RMSE are sorted as 0.992 & 0.15697, 0.998 & 0.96580, 0.966 & 1.38659, and 0.968 & 1.56068 for four properties such as KV, IV, CP, and PP, respectively. It is worth to mention this point that the kernel function Squared Exponential shows a great performance for IV and PP and kernel functions Exponential and Matern indicate appropriate efficiency for CP and KV properties, respectively. On the other hand, the results of the offered GPR models are compared with those of the previous models, LSSVM-PSO and ANFIS. The outcomes proved the superiority of this model over two former models in point of estimating the biodiesel properties. |
| format | Article |
| id | doaj-art-ce55e0aaaa994e56989d947c0266798a |
| institution | Kabale University |
| issn | 1687-806X 1687-8078 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Chemical Engineering |
| spelling | doaj-art-ce55e0aaaa994e56989d947c0266798a2025-02-03T01:25:48ZengWileyInternational Journal of Chemical Engineering1687-806X1687-80782021-01-01202110.1155/2021/56504995650499Developing a Robust Model Based on the Gaussian Process Regression Approach to Predict Biodiesel PropertiesInna Pustokhina0Amir Seraj1Hafsan Hafsan2Seyed Mojtaba Mostafavi3S. M. Alizadeh4Department of Propaedeutics of Dental Diseases, I. M. Sechenov First Moscow State Medical University, Sechenov University, Moscow, RussiaDepartment of Instrumentation and Industrial Automation, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, IranBiology Department, Faculty of Science and Technology, Universitas Islam Negeri Alauddin Makassar, Sultan Alauddin Street, Gowa 92118, South Sulawesi, IndonesiaHiTech Institute of Theoretical and Computational Chemistry, IndiaPetroleum Engineering Department, Australian College of Kuwait, West Mishref, Kuwait City, KuwaitBiodiesel is assumed a renewable and environmentally friendly fuel that possesses the potential to substitute petroleum diesel. The basic purpose of the present study is to design a precise algorithm based on Gaussian Process Regression (GPR) model with several kernel functions, i.e., Rational Quadratic, Squared Exponential, Matern, and Exponential, to estimate biodiesel properties. These properties include kinematic viscosity (KV), pour point (PP), iodine value (IV), and cloud point (CP) as a function of fatty acid composition. In order to develop this model, some variables are assumed, such as molecular weight, carbon number, double bond numbers, monounsaturated fatty acids, polyunsaturated fatty acid, weight percent of saturated acid, and temperature. The performance and efficiency of the GPR model are measured through several statistical criteria and the results are summarized in root mean square error (RMSE) and coefficients of determination (R2). R2 and RMSE are sorted as 0.992 & 0.15697, 0.998 & 0.96580, 0.966 & 1.38659, and 0.968 & 1.56068 for four properties such as KV, IV, CP, and PP, respectively. It is worth to mention this point that the kernel function Squared Exponential shows a great performance for IV and PP and kernel functions Exponential and Matern indicate appropriate efficiency for CP and KV properties, respectively. On the other hand, the results of the offered GPR models are compared with those of the previous models, LSSVM-PSO and ANFIS. The outcomes proved the superiority of this model over two former models in point of estimating the biodiesel properties.http://dx.doi.org/10.1155/2021/5650499 |
| spellingShingle | Inna Pustokhina Amir Seraj Hafsan Hafsan Seyed Mojtaba Mostafavi S. M. Alizadeh Developing a Robust Model Based on the Gaussian Process Regression Approach to Predict Biodiesel Properties International Journal of Chemical Engineering |
| title | Developing a Robust Model Based on the Gaussian Process Regression Approach to Predict Biodiesel Properties |
| title_full | Developing a Robust Model Based on the Gaussian Process Regression Approach to Predict Biodiesel Properties |
| title_fullStr | Developing a Robust Model Based on the Gaussian Process Regression Approach to Predict Biodiesel Properties |
| title_full_unstemmed | Developing a Robust Model Based on the Gaussian Process Regression Approach to Predict Biodiesel Properties |
| title_short | Developing a Robust Model Based on the Gaussian Process Regression Approach to Predict Biodiesel Properties |
| title_sort | developing a robust model based on the gaussian process regression approach to predict biodiesel properties |
| url | http://dx.doi.org/10.1155/2021/5650499 |
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