AN EXPERT MODEL FOR ESTIMATION OF THE PERFORMANCE OF DIRECT DIMETHYL ETHER SYNTHESIS FROM SYNTHESIS GAS

In this work, an artificial neural network (ANN) has been trained and tested for estimation of the performance of direct synthesis of dimethyl ether (DME) from synthesis gas. Yield and selectivity of DME production and also conversion of CO could be predicted when temperature and pressure of reactor and H-2/CO molar ratio in feed have been specified. The results of ANN estimation for yield of DME, selectivity of DME and CO conversion are in very good agreement with experimental values. For this development, database was collected from our previous experiment. The accuracy and trend stability of the trained networks were tested against unseen data. Different training schemes for the back-propagation learning algorithm, such as: Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM), Gradient Descent with Momentum (GDM), variable learning rate Back propagation (GDA) and Resilient back Propagation (RP) methods were used. The SCG algorithm with seven neurons in the hidden layer shows the best suitable algorithm with the minimum average absolute relative error 0.05231.