Development of machine learning-based solubility models for estimation of Hydrogen solubility in oil: Models assessment and validation
This research was done to build computational models for estimating solubility of hydrogen (S) in a given system based on the inputs of temperature (T) and pressure (P). In fact, multiples models were built considering double inputs and single output. To achieve this, three different regression algo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X23009280 |
| _version_ | 1797656109337542656 |
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| author | Hulin Jin Zhiran Jin Yong-Guk Kim Chunyang Fan |
| author_facet | Hulin Jin Zhiran Jin Yong-Guk Kim Chunyang Fan |
| author_sort | Hulin Jin |
| collection | DOAJ |
| description | This research was done to build computational models for estimating solubility of hydrogen (S) in a given system based on the inputs of temperature (T) and pressure (P). In fact, multiples models were built considering double inputs and single output. To achieve this, three different regression algorithms were trained and tested on a comprehensive dataset using the optimization technique of Grey Wolf Optimization (GWO). The three models used were Elastic Net, Support Vector Regression (SVR), and Automatic Relevance Determination (ARD) Regression. R2 score, root mean square error (RMSE), and mean absolute error were just few of the measures used to assess the models' overall efficacy (MAE). The Elastic Net model achieved the highest R2 score of 0.965 with an RMSE of 8.84×10−2 and an MAE of 7.79×10−2. The SVR technique also performed well, indicated R2 of 0.949, an RMSE of 9.81×10−2, and an MAE of 7.79×10−2. The ARD Regression model revealed an R2 of 0.905 with an RMSE of 1.53×10−1 and an MAE of 1.32×10−1. These findings highlight the potential of machine learning models and metaheuristic algorithms such as GWO in accurately estimating gas solubility in oil, which can have significant implications for various industrial applications. |
| first_indexed | 2024-03-11T17:24:36Z |
| format | Article |
| id | doaj.art-3a20d5624ba148448b490e92fdd52097 |
| institution | Directory Open Access Journal |
| issn | 2214-157X |
| language | English |
| last_indexed | 2024-03-11T17:24:36Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj.art-3a20d5624ba148448b490e92fdd520972023-10-19T04:22:04ZengElsevierCase Studies in Thermal Engineering2214-157X2023-11-0151103622Development of machine learning-based solubility models for estimation of Hydrogen solubility in oil: Models assessment and validationHulin Jin0Zhiran Jin1Yong-Guk Kim2Chunyang Fan3School of Computer Science and Technology, Anhui University, Hefei, 230031, China; Corresponding author.Foothill Preparatory School, 91780, Temple City, CA, USADepartment of Computer Engineering, Sejong University, 05006, Seoul, South KoreaSchool of Computer Science and Technology, Anhui University, Hefei, 230031, ChinaThis research was done to build computational models for estimating solubility of hydrogen (S) in a given system based on the inputs of temperature (T) and pressure (P). In fact, multiples models were built considering double inputs and single output. To achieve this, three different regression algorithms were trained and tested on a comprehensive dataset using the optimization technique of Grey Wolf Optimization (GWO). The three models used were Elastic Net, Support Vector Regression (SVR), and Automatic Relevance Determination (ARD) Regression. R2 score, root mean square error (RMSE), and mean absolute error were just few of the measures used to assess the models' overall efficacy (MAE). The Elastic Net model achieved the highest R2 score of 0.965 with an RMSE of 8.84×10−2 and an MAE of 7.79×10−2. The SVR technique also performed well, indicated R2 of 0.949, an RMSE of 9.81×10−2, and an MAE of 7.79×10−2. The ARD Regression model revealed an R2 of 0.905 with an RMSE of 1.53×10−1 and an MAE of 1.32×10−1. These findings highlight the potential of machine learning models and metaheuristic algorithms such as GWO in accurately estimating gas solubility in oil, which can have significant implications for various industrial applications.http://www.sciencedirect.com/science/article/pii/S2214157X23009280Machine learningHydrocarbonsModelingGrey wolf optimizationCrude oil |
| spellingShingle | Hulin Jin Zhiran Jin Yong-Guk Kim Chunyang Fan Development of machine learning-based solubility models for estimation of Hydrogen solubility in oil: Models assessment and validation Case Studies in Thermal Engineering Machine learning Hydrocarbons Modeling Grey wolf optimization Crude oil |
| title | Development of machine learning-based solubility models for estimation of Hydrogen solubility in oil: Models assessment and validation |
| title_full | Development of machine learning-based solubility models for estimation of Hydrogen solubility in oil: Models assessment and validation |
| title_fullStr | Development of machine learning-based solubility models for estimation of Hydrogen solubility in oil: Models assessment and validation |
| title_full_unstemmed | Development of machine learning-based solubility models for estimation of Hydrogen solubility in oil: Models assessment and validation |
| title_short | Development of machine learning-based solubility models for estimation of Hydrogen solubility in oil: Models assessment and validation |
| title_sort | development of machine learning based solubility models for estimation of hydrogen solubility in oil models assessment and validation |
| topic | Machine learning Hydrocarbons Modeling Grey wolf optimization Crude oil |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X23009280 |
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