Prediction of Methanol Production in a Carbon Dioxide Hydrogenation Plant Using Neural Networks

Daniel Chuquin-Vasco, Francis Parra, Nelson Chuquin-Vasco, Juan Chuquin-Vasco and Vanesa Lo-Iacono-Ferreira
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Daniel Chuquin-Vasco: Grupo de Investigación en Seguridad Ambiente e Ingeniería (GISAI), Escuela Superior Politécnica de Chimborazo, Panamericana Sur Km 1 ½, Riobamba 060106, Ecuador
Francis Parra: SOLMA, Soluciones Mecánicas Avanzadas, Servicios de Ingeniería Mecánica y Construcción, Quito 170110, Ecuador
Nelson Chuquin-Vasco: Grupo de Investigación en Seguridad Ambiente e Ingeniería (GISAI), Escuela Superior Politécnica de Chimborazo, Panamericana Sur Km 1 ½, Riobamba 060106, Ecuador
Juan Chuquin-Vasco: Grupo de Investigación en Seguridad Ambiente e Ingeniería (GISAI), Escuela Superior Politécnica de Chimborazo, Panamericana Sur Km 1 ½, Riobamba 060106, Ecuador
Vanesa Lo-Iacono-Ferreira: Project Management, Innovation and Sustainability Research Center (PRINS), Alcoy Campus, Universitat Politècnica de València, Plaza Ferrándiz y Carbonell, s/n, E-03690 Alcoy, Spain

Energies, 2021, vol. 14, issue 13, 1-18

Abstract: The objective of this research was to design a neural network (ANN) to predict 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 predicted by the ANN. Therefore, the designed ANN can be used to predict the methanol flow at the exit of a dehydrogenation plant and later for the optimization of the system.

Keywords: simulation; DWSIM; hydrogenation of carbon dioxide; ANN (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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