Predictive Modeling of the Hydrate Formation Temperature in Highly Pressurized Natural Gas Pipelines
In this study, we aim to develop advanced machine learning regression models for the prediction of hydrate temperature based on the chemical composition of sweet gas mixtures. Data were collected in accordance with the BOTAS Gas Network Code specifications, approved by the Turkish Energy Market Regu...
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MDPI AG
2024-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/21/5306 |
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| author | Mustafa Karaköse Özgün Yücel |
| author_facet | Mustafa Karaköse Özgün Yücel |
| author_sort | Mustafa Karaköse |
| collection | DOAJ |
| description | In this study, we aim to develop advanced machine learning regression models for the prediction of hydrate temperature based on the chemical composition of sweet gas mixtures. Data were collected in accordance with the BOTAS Gas Network Code specifications, approved by the Turkish Energy Market Regulatory Authority (EMRA), and generated using DNV GasVLe v3.10 software, which predicts the phase behavior and properties of hydrocarbon-based mixtures under various pressure and temperature conditions. We employed linear regression, decision tree regression, random forest regression, generalized additive models, and artificial neural networks to create prediction models for hydrate formation temperature (HFT). The performance of these models was evaluated using the hold-out cross-validation technique to ensure unbiased results. This study demonstrates the efficacy of ensemble learning methods, particularly random forest with an <i>R</i><sup>2</sup> and <i>Adj. R</i><sup>2</sup> of 0.998, for predicting hydrate formation conditions, thereby enhancing the safety and efficiency of gas transport and processing. This research illustrates the potential of machine learning techniques in advancing the predictive accuracy for hydrate formations in natural gas pipelines and suggests avenues for future optimizations through hybrid modeling approaches. |
| format | Article |
| id | doaj-art-65a1e8df45e04ea4b45656500d7e596c |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-65a1e8df45e04ea4b45656500d7e596c2025-08-20T02:55:39ZengMDPI AGEnergies1996-10732024-10-011721530610.3390/en17215306Predictive Modeling of the Hydrate Formation Temperature in Highly Pressurized Natural Gas PipelinesMustafa Karaköse0Özgün Yücel1Department of Chemical Engineering, Gebze Technical University, Kocaeli 41400, TurkeyDepartment of Chemical Engineering, Gebze Technical University, Kocaeli 41400, TurkeyIn this study, we aim to develop advanced machine learning regression models for the prediction of hydrate temperature based on the chemical composition of sweet gas mixtures. Data were collected in accordance with the BOTAS Gas Network Code specifications, approved by the Turkish Energy Market Regulatory Authority (EMRA), and generated using DNV GasVLe v3.10 software, which predicts the phase behavior and properties of hydrocarbon-based mixtures under various pressure and temperature conditions. We employed linear regression, decision tree regression, random forest regression, generalized additive models, and artificial neural networks to create prediction models for hydrate formation temperature (HFT). The performance of these models was evaluated using the hold-out cross-validation technique to ensure unbiased results. This study demonstrates the efficacy of ensemble learning methods, particularly random forest with an <i>R</i><sup>2</sup> and <i>Adj. R</i><sup>2</sup> of 0.998, for predicting hydrate formation conditions, thereby enhancing the safety and efficiency of gas transport and processing. This research illustrates the potential of machine learning techniques in advancing the predictive accuracy for hydrate formations in natural gas pipelines and suggests avenues for future optimizations through hybrid modeling approaches.https://www.mdpi.com/1996-1073/17/21/5306machine learninghydrate formationmethanenatural gassupervised |
| spellingShingle | Mustafa Karaköse Özgün Yücel Predictive Modeling of the Hydrate Formation Temperature in Highly Pressurized Natural Gas Pipelines Energies machine learning hydrate formation methane natural gas supervised |
| title | Predictive Modeling of the Hydrate Formation Temperature in Highly Pressurized Natural Gas Pipelines |
| title_full | Predictive Modeling of the Hydrate Formation Temperature in Highly Pressurized Natural Gas Pipelines |
| title_fullStr | Predictive Modeling of the Hydrate Formation Temperature in Highly Pressurized Natural Gas Pipelines |
| title_full_unstemmed | Predictive Modeling of the Hydrate Formation Temperature in Highly Pressurized Natural Gas Pipelines |
| title_short | Predictive Modeling of the Hydrate Formation Temperature in Highly Pressurized Natural Gas Pipelines |
| title_sort | predictive modeling of the hydrate formation temperature in highly pressurized natural gas pipelines |
| topic | machine learning hydrate formation methane natural gas supervised |
| url | https://www.mdpi.com/1996-1073/17/21/5306 |
| work_keys_str_mv | AT mustafakarakose predictivemodelingofthehydrateformationtemperatureinhighlypressurizednaturalgaspipelines AT ozgunyucel predictivemodelingofthehydrateformationtemperatureinhighlypressurizednaturalgaspipelines |