Abstract
AbstractArtificial 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.
Highlights
Using artificial neural networks (ANNs) is the most widespread machine learning approach for modeling complex phenomena due to their simple formulation, flexibility and robustness [1, 2]
In this paper ANNs were used to model the direct synthesis of dimethyl ether (DME) from syngas over a commercial dual catalyst system at high pressure
The training was conducted for ANNs of different structures and five hidden neurons proved to provide sufficient model complexity to map the available data
Summary
Using artificial neural networks (ANNs) is the most widespread machine learning approach for modeling complex phenomena due to their simple formulation, flexibility and robustness [1, 2]. ANNs have been used for predicting the performance of the liquid phase direct synthesis of DME over CuO/ZnO/Al2O3 and H-ZSM-5 catalysts [9]. We used ANNs to model the direct synthesis of DME from CO2-rich synthesis gas over a mixed catalyst bed of commercial CuO/ZnO/ Al2O3 (CZA) and g-Al2O3 catalysts at high pressure. The ANNs used to model the direct synthesis of DME map the input-output relationships in intrinsic kinetic data taken over a wide range of operating conditions and inlet feed compositions. We conduct simulations within and beyond the model’s validity range to shed light on the ANN’s predictive ability in both operational windows, and report on the ability of simple ANNs in modeling this system in comparison to that of a lumped kinetic model fitted to the same data
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