Modeling the Direct Synthesis of Dimethyl Ether using Artificial Neural Networks |
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Authors: | Nirvana Delgado Otalvaro Pembe Gül Bilir Karla Herrera Delgado Stephan Pitter Jörg Sauer |
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Affiliation: | Karlsruhe Institute of Technology, Institute of Catalysis Research and Technology, Hermann-von-Helmholtz-Platz, 76344 Eggenstein-Leopoldshafen, Germany.m |
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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. |
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Keywords: | Artificial neural network Dimethyl ether Kinetics Modeling |
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