Solubility of hydrogen sulfide in ionic liquids for gas removal processes using artificial neural networks.
Journal of Environmental Chemical Engineering
| dc.creator | Faúndez-Araya, Claudio Alonso | |
| dc.creator | Fierro-Antipi, Elías Nicolás | |
| dc.creator | Valderrama, José O | |
| dc.date | 2021-08-23T22:51:43Z | |
| dc.date | 2022-07-08T20:30:28Z | |
| dc.date | 2021-08-23T22:51:43Z | |
| dc.date | 2022-07-08T20:30:28Z | |
| dc.date | 2016 | |
| dc.date.accessioned | 2023-08-22T02:40:41Z | |
| dc.date.available | 2023-08-22T02:40:41Z | |
| dc.identifier | 1150802 | |
| dc.identifier | 1150802 | |
| dc.identifier | https://hdl.handle.net/10533/250830 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/8311518 | |
| dc.description | Artificial neural networks have been used for the correlation and prediction of solubility data of hydrogen sulfide in ionic liquids. The solubility of hydrogen sulfide is highly variable for different types of ionic liquids at the same temperature and pressure and its correlation and prediction is of special importance in the removal of hydrogen sulfide from flue gases for which effective and efficient solvents are required. Several network architectures were tested to finally choose a three layer network with 6, 10 and 1 neuron, respectively (6, 10, 1). Twelve binary hydrogen sulfide + ionic liquids mixtures were considered in the study. Solubility data (pressure, temperature, gas concentration in the liquid phase) for these systems were taken from the literature (392 data points for training and 104 data points for testing). The training variables are the temperature and the pressure of the binary systems being the target variable the solubility of hydrogen sulfide in the ionic liquid. Average absolute deviations are lower than 4.0% and the maximum individual absolute deviation in solubility is 12.6%. The proposed neural network model is a good alternative method for the estimation of solubility of hydrogen sulfide in ionic liquids for its use in process analysis, process design and process simulation. | |
| dc.description | Regular 2015 | |
| dc.description | FONDECYT | |
| dc.description | FONDECYT | |
| dc.language | spa | |
| dc.relation | handle/10533/111557 | |
| dc.relation | handle/10533/111541 | |
| dc.relation | handle/10533/108045 | |
| dc.relation | https://doi.org/10.1016/j.jece.2015.11.008 | |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
| dc.rights | info:eu-repo/semantics/article | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.title | Solubility of hydrogen sulfide in ionic liquids for gas removal processes using artificial neural networks. | |
| dc.title | Journal of Environmental Chemical Engineering | |
| dc.type | Articulo | |
| dc.type | info:eu-repo/semantics/publishedVersion |