An Intelligent Deep Learning Model for CO2 Adsorption Prediction
In this paper, we propose a supervised deep learning neural network (D-CNN) approach to predict CO2 adsorption form the textural and compositional features of biomass porous carbon waste and adsorption features. Both the textural and compositional features of biomass porous carbon waste are utilized...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2022-01-01
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| Series: | Adsorption Science & Technology |
| Online Access: | http://dx.doi.org/10.1155/2022/8136302 |
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| author | Hanan Ahmed Hosni Mahmoud Nada Ali Hakami Alaaeldin M. Hafez |
| author_facet | Hanan Ahmed Hosni Mahmoud Nada Ali Hakami Alaaeldin M. Hafez |
| author_sort | Hanan Ahmed Hosni Mahmoud |
| collection | DOAJ |
| description | In this paper, we propose a supervised deep learning neural network (D-CNN) approach to predict CO2 adsorption form the textural and compositional features of biomass porous carbon waste and adsorption features. Both the textural and compositional features of biomass porous carbon waste are utilized as inputs for the D-CNN architecture. A deep learning neural network (D-CNN) is proposed to predict the adsorption rate of CO2 on zeolites. The adsorbed amount will be classified and predicted by the D-CNN. Three tree machine learning models, namely, gradient decision model (GDM), scalable boosting tree model (SBT), and gradient variant decision tree model (GVD), were fused. A feature importance metric was proposed using feature permutation, and the effect of each feature on the target output variable was investigated. The important extracted features from the three employed model were fused and used as the fusion feature set in our proposed model: fusion matrix deep learning model (FMDL). A dataset of 1400 data items, on adsorbent type and various adsorption pressure, is used as inputs for the D-CNN model. Comparison of the proposed model is done against the three tree models, which utilizes a single training layer. The error measure of the D-CNN and the tree model architectures utilize the mean square error confirming the efficiency of 0.00003 for our model, 0.00062 for the SBT, 0.00091 for the GDM, and 0.00098 for the GVD, after 150 epochs. The produced weight matrix was able to predict the CO2 adsorption under diverse process settings with high accuracy of 96.4%. |
| format | Article |
| id | doaj-art-39d6bd3657624ac78a19df1bbff3d027 |
| institution | Kabale University |
| issn | 2048-4038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Adsorption Science & Technology |
| spelling | doaj-art-39d6bd3657624ac78a19df1bbff3d0272025-01-02T23:44:59ZengSAGE PublishingAdsorption Science & Technology2048-40382022-01-01202210.1155/2022/8136302An Intelligent Deep Learning Model for CO2 Adsorption PredictionHanan Ahmed Hosni Mahmoud0Nada Ali Hakami1Alaaeldin M. Hafez2Department of Computer SciencesJazan UniversityDepartment of Information SystemsIn this paper, we propose a supervised deep learning neural network (D-CNN) approach to predict CO2 adsorption form the textural and compositional features of biomass porous carbon waste and adsorption features. Both the textural and compositional features of biomass porous carbon waste are utilized as inputs for the D-CNN architecture. A deep learning neural network (D-CNN) is proposed to predict the adsorption rate of CO2 on zeolites. The adsorbed amount will be classified and predicted by the D-CNN. Three tree machine learning models, namely, gradient decision model (GDM), scalable boosting tree model (SBT), and gradient variant decision tree model (GVD), were fused. A feature importance metric was proposed using feature permutation, and the effect of each feature on the target output variable was investigated. The important extracted features from the three employed model were fused and used as the fusion feature set in our proposed model: fusion matrix deep learning model (FMDL). A dataset of 1400 data items, on adsorbent type and various adsorption pressure, is used as inputs for the D-CNN model. Comparison of the proposed model is done against the three tree models, which utilizes a single training layer. The error measure of the D-CNN and the tree model architectures utilize the mean square error confirming the efficiency of 0.00003 for our model, 0.00062 for the SBT, 0.00091 for the GDM, and 0.00098 for the GVD, after 150 epochs. The produced weight matrix was able to predict the CO2 adsorption under diverse process settings with high accuracy of 96.4%.http://dx.doi.org/10.1155/2022/8136302 |
| spellingShingle | Hanan Ahmed Hosni Mahmoud Nada Ali Hakami Alaaeldin M. Hafez An Intelligent Deep Learning Model for CO2 Adsorption Prediction Adsorption Science & Technology |
| title | An Intelligent Deep Learning Model for CO2 Adsorption Prediction |
| title_full | An Intelligent Deep Learning Model for CO2 Adsorption Prediction |
| title_fullStr | An Intelligent Deep Learning Model for CO2 Adsorption Prediction |
| title_full_unstemmed | An Intelligent Deep Learning Model for CO2 Adsorption Prediction |
| title_short | An Intelligent Deep Learning Model for CO2 Adsorption Prediction |
| title_sort | intelligent deep learning model for co2 adsorption prediction |
| url | http://dx.doi.org/10.1155/2022/8136302 |
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