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- Title
Modeling the Direct Synthesis of Dimethyl Ether using Artificial Neural Networks.
- Authors
Delgado Otalvaro, Nirvana; Gül Bilir, Pembe; Herrera Delgado, Karla; Pitter, Stephan; Sauer, Jörg
- Abstract
Artificial neural networks (ANNs) are designed and implemented to model the direct synthesis of dimethyl ether (DME) from syngas over a commercial catalyst system. The predictive power of the ANNs is assessed by comparison with the predictions of a lumped model parameterized to fit the same data used for ANN training. The ANN training converges much faster than the parameter estimation of the lumped model, and the predictions show a higher degree of accuracy under all conditions. Furthermore, the simulations show that the ANN predictions are also accurate even at some conditions beyond the validity range.
- Subjects
METHYL ether; ARTIFICIAL neural networks; ETHER synthesis; PARAMETER estimation
- Publication
Chemie Ingenieur Technik (CIT), 2021, Vol 93, Issue 5, p754
- ISSN
0009-286X
- Publication type
Article
- DOI
10.1002/cite.202000226