Development of computational approach for calculation of hydrogen solubility in hydrocarbons for treatment of petroleum Article Swipe
Abdulrahman Sumayli
,
Saad M. Alshahrani
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.csite.2023.103574
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.csite.2023.103574
For hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with a RMSE of 1.38 × 10−2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions are estimated in different ranges of temperatures and pressures of 150 °C–350 °C and 1.2 MPa–10.8 MPa, respectively.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.csite.2023.103574
- OA Status
- gold
- Cited By
- 6
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387233856
All OpenAlex metadata
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https://openalex.org/W4387233856Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.csite.2023.103574Digital Object Identifier
- Title
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Development of computational approach for calculation of hydrogen solubility in hydrocarbons for treatment of petroleumWork title
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articleOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-10-01Full publication date if available
- Authors
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Abdulrahman Sumayli, Saad M. AlshahraniList of authors in order
- Landing page
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https://doi.org/10.1016/j.csite.2023.103574Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.csite.2023.103574Direct OA link when available
- Concepts
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Solubility, Hydrogen, Computer science, Support vector machine, Ridge, Mean squared error, Petroleum, Process (computing), Computation, Process engineering, Algorithm, Environmental science, Machine learning, Chemistry, Mathematics, Geology, Organic chemistry, Statistics, Engineering, Paleontology, Operating systemTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 2, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4302024152, https://openalex.org/W4220981587, https://openalex.org/W4310950572, https://openalex.org/W4214586880, https://openalex.org/W4322101974, https://openalex.org/W2503126452, https://openalex.org/W2580061642, https://openalex.org/W2794472508, https://openalex.org/W4296422460, https://openalex.org/W3212748319, https://openalex.org/W4295960244, https://openalex.org/W2290883490, https://openalex.org/W2331241569, https://openalex.org/W4239510810, https://openalex.org/W6798993050, https://openalex.org/W2063216290, https://openalex.org/W6680532697, https://openalex.org/W2753671882, https://openalex.org/W2284731747, https://openalex.org/W6634318372, https://openalex.org/W3198024082, https://openalex.org/W1502922572, https://openalex.org/W6766763557, https://openalex.org/W2011873184, https://openalex.org/W1977234485, https://openalex.org/W2016210396, https://openalex.org/W6804549830, https://openalex.org/W1981432147, https://openalex.org/W4385305672, https://openalex.org/W4360948923, https://openalex.org/W1528620860, https://openalex.org/W3186015590, https://openalex.org/W2137226992, https://openalex.org/W3215109731, https://openalex.org/W1571870753, https://openalex.org/W4211049957, https://openalex.org/W4212863985, https://openalex.org/W1172736100 |
| referenced_works_count | 38 |
| abstract_inverted_index.a | 97, 130 |
| abstract_inverted_index.H2 | 22 |
| abstract_inverted_index.To | 75 |
| abstract_inverted_index.We | 19, 92 |
| abstract_inverted_index.an | 137 |
| abstract_inverted_index.as | 42 |
| abstract_inverted_index.be | 160 |
| abstract_inverted_index.in | 9, 25, 102, 155, 162, 173, 181 |
| abstract_inverted_index.is | 11 |
| abstract_inverted_index.of | 6, 17, 82, 99, 132, 139, 152, 165, 171, 184, 188 |
| abstract_inverted_index.on | 111 |
| abstract_inverted_index.to | 13, 45 |
| abstract_inverted_index.we | 57, 106 |
| abstract_inverted_index.× | 134 |
| abstract_inverted_index.1.2 | 193 |
| abstract_inverted_index.150 | 189 |
| abstract_inverted_index.For | 0 |
| abstract_inverted_index.Our | 114 |
| abstract_inverted_index.WOA | 123 |
| abstract_inverted_index.and | 37, 70, 105, 136, 186, 192 |
| abstract_inverted_index.are | 179 |
| abstract_inverted_index.can | 148 |
| abstract_inverted_index.oil | 29, 177 |
| abstract_inverted_index.the | 4, 15, 21, 43, 46, 49, 53, 77, 80, 87, 94, 118, 125, 163, 169, 174 |
| abstract_inverted_index.was | 52 |
| abstract_inverted_index.°C | 191 |
| abstract_inverted_index.(H2) | 8 |
| abstract_inverted_index.1.38 | 133 |
| abstract_inverted_index.MPa, | 195 |
| abstract_inverted_index.RMSE | 131 |
| abstract_inverted_index.best | 78, 126 |
| abstract_inverted_index.four | 26, 175 |
| abstract_inverted_index.show | 116 |
| abstract_inverted_index.sole | 54 |
| abstract_inverted_index.that | 117, 144 |
| abstract_inverted_index.type | 39 |
| abstract_inverted_index.were | 40 |
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| abstract_inverted_index.These | 141 |
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| abstract_inverted_index.could | 159 |
| abstract_inverted_index.crude | 28 |
| abstract_inverted_index.heavy | 27, 176 |
| abstract_inverted_index.model | 120 |
| abstract_inverted_index.ridge | 72 |
| abstract_inverted_index.their | 108 |
| abstract_inverted_index.these | 83 |
| abstract_inverted_index.three | 59 |
| abstract_inverted_index.tuned | 121 |
| abstract_inverted_index.using | 31, 86, 96 |
| abstract_inverted_index.whale | 88 |
| abstract_inverted_index.which | 158 |
| abstract_inverted_index.while | 48 |
| abstract_inverted_index.(BRR). | 74 |
| abstract_inverted_index.(GPR), | 69 |
| abstract_inverted_index.(SVR), | 65 |
| abstract_inverted_index.(WOA). | 91 |
| abstract_inverted_index.0.991. | 140 |
| abstract_inverted_index.10−2 | 135 |
| abstract_inverted_index.Vector | 63 |
| abstract_inverted_index.inputs | 44 |
| abstract_inverted_index.models | 84, 95 |
| abstract_inverted_index.ranges | 183 |
| abstract_inverted_index.useful | 161 |
| abstract_inverted_index.Support | 62 |
| abstract_inverted_index.WOA-SVR | 119 |
| abstract_inverted_index.achieve | 76 |
| abstract_inverted_index.dataset | 98 |
| abstract_inverted_index.improve | 14 |
| abstract_inverted_index.knowing | 3 |
| abstract_inverted_index.machine | 32, 145 |
| abstract_inverted_index.models, | 47 |
| abstract_inverted_index.models: | 61 |
| abstract_inverted_index.process | 67 |
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| abstract_inverted_index.results | 115 |
| abstract_inverted_index.several | 112 |
| abstract_inverted_index.suggest | 143 |
| abstract_inverted_index.various | 103 |
| abstract_inverted_index.Bayesian | 71 |
| abstract_inverted_index.Besides, | 168 |
| abstract_inverted_index.Gaussian | 66 |
| abstract_inverted_index.accurate | 150 |
| abstract_inverted_index.achieves | 124 |
| abstract_inverted_index.compared | 107 |
| abstract_inverted_index.critical | 12 |
| abstract_inverted_index.employed | 58 |
| abstract_inverted_index.findings | 142 |
| abstract_inverted_index.hydrogen | 7, 50, 153, 172 |
| abstract_inverted_index.learning | 33, 146 |
| abstract_inverted_index.metrics. | 113 |
| abstract_inverted_index.overall, | 128 |
| abstract_inverted_index.process, | 2 |
| abstract_inverted_index.process. | 18 |
| abstract_inverted_index.R-squared | 138 |
| abstract_inverted_index.algorithm | 90 |
| abstract_inverted_index.different | 60, 156, 182 |
| abstract_inverted_index.estimated | 180 |
| abstract_inverted_index.evaluated | 93 |
| abstract_inverted_index.feedstock | 38 |
| abstract_inverted_index.fractions | 178 |
| abstract_inverted_index.optimized | 85 |
| abstract_inverted_index.pressure, | 36 |
| abstract_inverted_index.pressures | 187 |
| abstract_inverted_index.response. | 55 |
| abstract_inverted_index.°C–350 | 190 |
| abstract_inverted_index.MPa–10.8 | 194 |
| abstract_inverted_index.Regression | 64 |
| abstract_inverted_index.considered | 41 |
| abstract_inverted_index.efficiency | 16 |
| abstract_inverted_index.feedstocks | 30 |
| abstract_inverted_index.regression | 68, 73 |
| abstract_inverted_index.solubility | 5, 23, 51, 100, 154, 170 |
| abstract_inverted_index.techniques | 147 |
| abstract_inverted_index.computation | 24 |
| abstract_inverted_index.development | 164 |
| abstract_inverted_index.feedstocks, | 104, 157 |
| abstract_inverted_index.performance | 109, 127 |
| abstract_inverted_index.predictions | 151 |
| abstract_inverted_index.techniques. | 34 |
| abstract_inverted_index.Temperature, | 35 |
| abstract_inverted_index.hydrocarbons | 10 |
| abstract_inverted_index.investigated | 20 |
| abstract_inverted_index.measurements | 101 |
| abstract_inverted_index.optimization | 89 |
| abstract_inverted_index.performance, | 79 |
| abstract_inverted_index.temperatures | 185 |
| abstract_inverted_index.Specifically, | 56 |
| abstract_inverted_index.hydrogenation | 1 |
| abstract_inverted_index.respectively. | 196 |
| abstract_inverted_index.technologies. | 167 |
| abstract_inverted_index.hydrogen-related | 166 |
| abstract_inverted_index.hyper-parameters | 81 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5061190236, https://openalex.org/A5026936130 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I142608572, https://openalex.org/I47164929 |
| citation_normalized_percentile.value | 0.73565271 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |