Prediction of Methanol Production in a Carbon Dioxide Hydrogenation Plant Using Neural Networks
| Autor: | Nelson Chuquin-Vasco, Daniel Chuquin-Vasco, Juan Chuquin-Vasco, Francis Parra, Vanesa G. Lo-Iacono-Ferreira |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Control and Optimization Mean squared error 020209 energy Mass flow Computer Science::Neural and Evolutionary Computation Energy Engineering and Power Technology 02 engineering and technology computer.software_genre DWSIM chemistry.chemical_compound Linear regression 0202 electrical engineering electronic engineering information engineering Dehydrogenation Electrical and Electronic Engineering PROYECTOS DE INGENIERIA Engineering (miscellaneous) Artificial neural network Renewable Energy Sustainability and the Environment Hydrogenation of carbon dioxide hydrogenation of carbon dioxide 021001 nanoscience & nanotechnology simulation Simulation software chemistry Carbon dioxide Environmental science Methanol 0210 nano-technology Biological system ANN computer Simulation Energy (miscellaneous) |
| Zdroj: | Energies, Vol 14, Iss 3965, p 3965 (2021) Energies Volume 14 Issue 13 RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname |
| ISSN: | 1996-1073 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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