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Pbkp_rediction of methanol production in a carbon dioxide hydrogenation plant using neural networks

Abstract:

The objective of this research was to design a neural network (ANN) to pbkp_redict the methanol flux at the outlet of a carbon dioxide dehydrogenation plant. For the development of the ANN, a database was generated, in the open-source simulation software “DWSIM”, from the validation of a process described in the literature. The sample consists of 133 data pairs with four inputs: reactor pressure and temperature, mass flow of carbon dioxide and hydrogen, and one output: flow of methanol. The ANN was designed using 12 neurons in the hidden layer and it was trained with the Levenberg–Marquardt algorithm. In the training, validation and testing phase, a global mean square (RMSE) value of 0.0085 and a global regression coefficient R of 0.9442 were obtained. The network was validated through an analysis of variance (ANOVA), where the p-value for all cases was greater than 0.05, which indicates that there are no significant differences between the observations and those pbkp_redicted by the ANN. Therefore, the designed ANN can be used to pbkp_redict the methanol flow at the exit of a dehydrogenation plant and later for the optimization of the system.