Investigation of H2 and CO and Modeling of Syngas Conversion by Artificial Neural Network Based on Experimental Data in a Fixed-bed Reactor

In this research, the application of design of experiment and artificial neural network on conversion of H2 and CO were studied based on the experimental data. The experimental data has been collected from five independent variables based on central composite design such as temperature and pressure of reactor, H2/CO feed ratio, and partial pressure of H2 and CO in reactor. The operating conditions are: T = 320-340°C, P = 2-8 barg, H2/CO = 0.8-2.2, PCO = 0.3-2.7 barg, and PH2 = 0.3-2.5 barg. To generate the conversion models, two methods consist of response surface methodology and artificial neural network were used. The capability and sensitivity of both models were evaluated by some statistical parameters including mean square error and absolute average relative deviation. The result of both models were compared with experimental data and show the best results. To evaluate the maximum conversion of (H2 and CO), a hybrid ANN/GA was performed to solve the nonlinear both models. Finally, all quadratic equations and maximum of both models were performed, and the results were concluded. Also, this method can be used to produce the valuable selective production.