Direct prediction of steam cracking products from naphtha bulk properties: Application of the two sub-networks ANN
Steam cracking of naphtha is an important process for the production of olefins. Applying artificial intelligence helps achieve high-frequency real-time optimization strategy and process control. This work employs an artificial neural network (ANN) model with two sub-networks to simulate the naphtha...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Chemical Engineering |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fceng.2022.983035/full |
| _version_ | 1828112350157209600 |
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| author | Yu Ren Yu Ren Zuwei Liao Yao Yang Yao Yang Jingyuan Sun Binbo Jiang Jingdai Wang Yongrong Yang |
| author_facet | Yu Ren Yu Ren Zuwei Liao Yao Yang Yao Yang Jingyuan Sun Binbo Jiang Jingdai Wang Yongrong Yang |
| author_sort | Yu Ren |
| collection | DOAJ |
| description | Steam cracking of naphtha is an important process for the production of olefins. Applying artificial intelligence helps achieve high-frequency real-time optimization strategy and process control. This work employs an artificial neural network (ANN) model with two sub-networks to simulate the naphtha steam cracking process. In the first feedstock composition ANN, the detailed feedstock compositions are determined from the limited naphtha bulk properties. In the second reactor ANN, the cracking product yields are predicted from the feedstock compositions and operating conditions. The combination of these two sub-networks has the ability to accurately and rapidly predict the product yields directly from naphtha bulk properties. Two different feedstock composition ANN strategies are proposed and compared. The results show that with the special design of dividing the output layer into five groups of PIONA, the prediction accuracy of product yields is significantly improved. The mean absolute error of 11 cracking products is 0.53wt% for 472 test sets. The comparison results show that this indirect feedstock composition ANN has lower product prediction errors, not just the reduction of the total error of the feedstock composition. The critical factor is ensuring that PIONA contents are equal to the actual values. The use of an indirect feedstock composition strategy is a means that can effectively improve the prediction accuracy of the whole ANN model. |
| first_indexed | 2024-04-11T11:47:48Z |
| format | Article |
| id | doaj.art-914ba11bc8fa4089946929c091ce32a6 |
| institution | Directory Open Access Journal |
| issn | 2673-2718 |
| language | English |
| last_indexed | 2024-04-11T11:47:48Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Chemical Engineering |
| spelling | doaj.art-914ba11bc8fa4089946929c091ce32a62022-12-22T04:25:29ZengFrontiers Media S.A.Frontiers in Chemical Engineering2673-27182022-09-01410.3389/fceng.2022.983035983035Direct prediction of steam cracking products from naphtha bulk properties: Application of the two sub-networks ANNYu Ren0Yu Ren1Zuwei Liao2Yao Yang3Yao Yang4Jingyuan Sun5Binbo Jiang6Jingdai Wang7Yongrong Yang8State Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, ChinaZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, ChinaZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, ChinaState Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, ChinaZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, ChinaState Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, ChinaState Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, ChinaState Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, ChinaState Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, ChinaSteam cracking of naphtha is an important process for the production of olefins. Applying artificial intelligence helps achieve high-frequency real-time optimization strategy and process control. This work employs an artificial neural network (ANN) model with two sub-networks to simulate the naphtha steam cracking process. In the first feedstock composition ANN, the detailed feedstock compositions are determined from the limited naphtha bulk properties. In the second reactor ANN, the cracking product yields are predicted from the feedstock compositions and operating conditions. The combination of these two sub-networks has the ability to accurately and rapidly predict the product yields directly from naphtha bulk properties. Two different feedstock composition ANN strategies are proposed and compared. The results show that with the special design of dividing the output layer into five groups of PIONA, the prediction accuracy of product yields is significantly improved. The mean absolute error of 11 cracking products is 0.53wt% for 472 test sets. The comparison results show that this indirect feedstock composition ANN has lower product prediction errors, not just the reduction of the total error of the feedstock composition. The critical factor is ensuring that PIONA contents are equal to the actual values. The use of an indirect feedstock composition strategy is a means that can effectively improve the prediction accuracy of the whole ANN model.https://www.frontiersin.org/articles/10.3389/fceng.2022.983035/fullnaphthasteam crackingartificial neural networkmodelingproduct prediction |
| spellingShingle | Yu Ren Yu Ren Zuwei Liao Yao Yang Yao Yang Jingyuan Sun Binbo Jiang Jingdai Wang Yongrong Yang Direct prediction of steam cracking products from naphtha bulk properties: Application of the two sub-networks ANN Frontiers in Chemical Engineering naphtha steam cracking artificial neural network modeling product prediction |
| title | Direct prediction of steam cracking products from naphtha bulk properties: Application of the two sub-networks ANN |
| title_full | Direct prediction of steam cracking products from naphtha bulk properties: Application of the two sub-networks ANN |
| title_fullStr | Direct prediction of steam cracking products from naphtha bulk properties: Application of the two sub-networks ANN |
| title_full_unstemmed | Direct prediction of steam cracking products from naphtha bulk properties: Application of the two sub-networks ANN |
| title_short | Direct prediction of steam cracking products from naphtha bulk properties: Application of the two sub-networks ANN |
| title_sort | direct prediction of steam cracking products from naphtha bulk properties application of the two sub networks ann |
| topic | naphtha steam cracking artificial neural network modeling product prediction |
| url | https://www.frontiersin.org/articles/10.3389/fceng.2022.983035/full |
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