Combined deep-learning optimization predictive models for determining carbon dioxide solubility in ionic liquids; Journal of Industrial Information Integration; Vol. 41
| Parent link: | Journal of Industrial Information Integration.— .— Amsterdam: Elsevier Science Publishing Company Inc. Vol. 41.— 2024.— Article number 100662, 21 p. |
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| সংস্থা লেখক: | |
| অন্যান্য লেখক: | , , , , , |
| সংক্ষিপ্ত: | This study explores the development of predictive models for carbon dioxide (CO2) solubility in ionic liquids based on a compiled dataset of 10,116 experimentally measured data points involving four input variables: pressure (P), temperature (T), cation type, and anion type. The deep-learning (DL) predictive models evaluated are standalone and hybrid versions of convolutional neural network (CNN) and long short-term memory (LSTM) algorithms with cuckoo optimization algorithm (COA) and gradient-based optimization (GBO). The laboratory-measured data was separated into training and test categories, and each category was normalized separately to improve the performance of the deep learning algorithms. The Mahalanobis distance-based quantile method was utilized to identify any outliers in the training data. Once identified, the outlier data points were eliminated from the training dataset. The control parameters of the deep learning algorithms were optimized using COA to enhance their efficiency, and the algorithms were hybridized with optimization algorithms to further improve their performance. The resulting models were analyzed to assess their accuracy, degree of overfitting, and the importance of input features. The study found that using 80% of the data for training and 20% for testing results in more accurate and generalizable models. Using the outlier detection method on the training data led to 307 data points being eliminated as outliers. Developing CO2-solubility predictive model showed that, the CNNsingle bondCOA model had the lowest RMSE and highest R2 among the developed models, indicating high generalizability for data unseen by the trained model. The analysis revealed that using optimization algorithms increased the CO2-solubility prediction performance of DL algorithms and reduced overfitting. T and cation type were the most and least important input features, respectively. Simultaneous changes in cation and anion type on CO2-solubility predictions displayed no systematic pattern. For increases in T, CO2 solubility typically decreased, whereas for increases in P CO2 solubility always increased but at variable rates. The results of this study can be used to develop accurate and generalizable CO2-solubility predictive models for various applications. Текстовый файл AM_Agreement |
| ভাষা: | ইংরেজি |
| প্রকাশিত: |
2024
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | https://doi.org/10.1016/j.jii.2024.100662 |
| বিন্যাস: | বৈদ্যুতিক গ্রন্থের অধ্যায় |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=674349 |
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| 200 | 1 | |a Combined deep-learning optimization predictive models for determining carbon dioxide solubility in ionic liquids |f Shadfar Davoodi, Hung Vo Thanh, David A. Wood [et al.] | |
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| 330 | |a This study explores the development of predictive models for carbon dioxide (CO2) solubility in ionic liquids based on a compiled dataset of 10,116 experimentally measured data points involving four input variables: pressure (P), temperature (T), cation type, and anion type. The deep-learning (DL) predictive models evaluated are standalone and hybrid versions of convolutional neural network (CNN) and long short-term memory (LSTM) algorithms with cuckoo optimization algorithm (COA) and gradient-based optimization (GBO). The laboratory-measured data was separated into training and test categories, and each category was normalized separately to improve the performance of the deep learning algorithms. The Mahalanobis distance-based quantile method was utilized to identify any outliers in the training data. Once identified, the outlier data points were eliminated from the training dataset. The control parameters of the deep learning algorithms were optimized using COA to enhance their efficiency, and the algorithms were hybridized with optimization algorithms to further improve their performance. The resulting models were analyzed to assess their accuracy, degree of overfitting, and the importance of input features. The study found that using 80% of the data for training and 20% for testing results in more accurate and generalizable models. Using the outlier detection method on the training data led to 307 data points being eliminated as outliers. Developing CO2-solubility predictive model showed that, the CNNsingle bondCOA model had the lowest RMSE and highest R2 among the developed models, indicating high generalizability for data unseen by the trained model. The analysis revealed that using optimization algorithms increased the CO2-solubility prediction performance of DL algorithms and reduced overfitting. T and cation type were the most and least important input features, respectively. Simultaneous changes in cation and anion type on CO2-solubility predictions displayed no systematic pattern. For increases in T, CO2 solubility typically decreased, whereas for increases in P CO2 solubility always increased but at variable rates. The results of this study can be used to develop accurate and generalizable CO2-solubility predictive models for various applications. | ||
| 336 | |a Текстовый файл | ||
| 371 | 0 | |a AM_Agreement | |
| 461 | 1 | |t Journal of Industrial Information Integration |c Amsterdam |n Elsevier Science Publishing Company Inc. | |
| 463 | 1 | |t Vol. 41 |v Article number 100662, 21 p. |d 2024 | |
| 610 | 1 | |a CO2 solubility | |
| 610 | 1 | |a Ionic water | |
| 610 | 1 | |a Hybrid deep-learning | |
| 610 | 1 | |a Convolutional neural network | |
| 610 | 1 | |a Cuckoo optimization algorithm | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a электронный ресурс | |
| 701 | 1 | |a Davoodi |b Sh. |c specialist in the field of petroleum engineering |c Research Engineer of Tomsk Polytechnic University |f 1990- |g Shadfar |9 22200 | |
| 701 | 0 | |a Vo Thanh Hung | |
| 701 | 1 | |a Wood |b D. A. |g David | |
| 701 | 1 | |a Mekhrad |b M. |g Mokhammad | |
| 701 | 1 | |a Reza |b H. M. |g Hajsaeedi Mohammad | |
| 701 | 1 | |a Rukavishnikov |b V. S. |c Director of the Center for Training and Retraining of Oil and Gas Specialists, Associate Professor of Tomsk Polytechnic University, Candidate of Technical Sciences |c Engineer of Tomsk Polytechnic University |f 1984- |g Valery Sergeevich |9 17614 | |
| 712 | 0 | 2 | |a Томский политехнический университет |c 1991- |9 26305 |4 570 |
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