Modeling the Direct Synthesis of Dimethyl Ether using Artificial Neural Networks

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.

Automated summary

Artificial neural networks (ANNs) are used to model the direct synthesis of dimethyl ether (DME) from syngas with greater accuracy and faster convergence compared to a lumped model. The ANNs show higher predictive power and are able to provide accurate predictions even under conditions beyond the validity range.