Hybrid modeling of ethylene to ethylene oxide heterogeneous reactor
In this research a dynamic grey box model (GBM) of ethylene oxide (EO) fixed bed reactor has been presented. In the first step of the study, kinetic model of the existing reactions was obtained using artificial neural network (ANN) approach. In order to build the ANN model industrial data of a typic...
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my.utm.291522020-10-22T04:04:16Z http://eprints.utm.my/id/eprint/29152/ Hybrid modeling of ethylene to ethylene oxide heterogeneous reactor Zahedi, Gholamreza Lohi, A. Mahdi, K. A. QD Chemistry In this research a dynamic grey box model (GBM) of ethylene oxide (EO) fixed bed reactor has been presented. In the first step of the study, kinetic model of the existing reactions was obtained using artificial neural network (ANN) approach. In order to build the ANN model industrial data of a typical EO reactor were employed. Time, C 2H 4, C 2H 4O, CO 2, H 2O and O 2 mole fractions were network inputs and the multiplication of reaction rate and catalyst deactivation (r* a)was ANN output. From 164 data, 109 data were employed to train ANN. After employing different training algorithms, it was found that, the radial basis function network (RBFN) training algorithm provides the best estimations of the data. This best obtained network was tested against fifty five unseen data. The network estimations were close to unseen data which confirmed generalization capability of the obtained network. In the next step of study, (r* a) was estimated with ANN and then the hybrid model of the reactor was solved. Simulation results were compared with EO mechanistic model and also with plant industrial data. It was found that GBM is 8.437 times more accurate than the mechanistic model. Elsevier B.V. 2011-09 Article PeerReviewed Zahedi, Gholamreza and Lohi, A. and Mahdi, K. A. (2011) Hybrid modeling of ethylene to ethylene oxide heterogeneous reactor. Fuel Processing Technology, 92 (9). pp. 1725-1732. ISSN 0378-3820 http://dx.doi.org/10.1016/j.fuproc.2011.04.022 DOI:10.1016/j.fuproc.2011.04.022 |
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QD Chemistry Zahedi, Gholamreza Lohi, A. Mahdi, K. A. Hybrid modeling of ethylene to ethylene oxide heterogeneous reactor |
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In this research a dynamic grey box model (GBM) of ethylene oxide (EO) fixed bed reactor has been presented. In the first step of the study, kinetic model of the existing reactions was obtained using artificial neural network (ANN) approach. In order to build the ANN model industrial data of a typical EO reactor were employed. Time, C 2H 4, C 2H 4O, CO 2, H 2O and O 2 mole fractions were network inputs and the multiplication of reaction rate and catalyst deactivation (r* a)was ANN output. From 164 data, 109 data were employed to train ANN. After employing different training algorithms, it was found that, the radial basis function network (RBFN) training algorithm provides the best estimations of the data. This best obtained network was tested against fifty five unseen data. The network estimations were close to unseen data which confirmed generalization capability of the obtained network. In the next step of study, (r* a) was estimated with ANN and then the hybrid model of the reactor was solved. Simulation results were compared with EO mechanistic model and also with plant industrial data. It was found that GBM is 8.437 times more accurate than the mechanistic model. |
| format |
Article |
| author |
Zahedi, Gholamreza Lohi, A. Mahdi, K. A. |
| author_facet |
Zahedi, Gholamreza Lohi, A. Mahdi, K. A. |
| author_sort |
Zahedi, Gholamreza |
| title |
Hybrid modeling of ethylene to ethylene oxide heterogeneous reactor |
| title_short |
Hybrid modeling of ethylene to ethylene oxide heterogeneous reactor |
| title_full |
Hybrid modeling of ethylene to ethylene oxide heterogeneous reactor |
| title_fullStr |
Hybrid modeling of ethylene to ethylene oxide heterogeneous reactor |
| title_full_unstemmed |
Hybrid modeling of ethylene to ethylene oxide heterogeneous reactor |
| title_sort |
hybrid modeling of ethylene to ethylene oxide heterogeneous reactor |
| publisher |
Elsevier B.V. |
| publishDate |
2011 |
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http://eprints.utm.my/id/eprint/29152/ http://dx.doi.org/10.1016/j.fuproc.2011.04.022 |
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