Adsorción de metales pesados (Hg2+, Cu2+ y Ni2+) en NTC utilizando redes neuronales Feed forward backprop y Elman backprop
DOI:
https://doi.org/10.33064/iycuaa2023894207Palabras clave:
Energía de Gibbs, metales pesados, Redes neuronales artificiales, Hyperchem, Gaussian, NTCResumen
En el presente trabajo se estudiaron sistemas de adsorción mono y multicomponente de metales pesados (Hg2+, Cu2+ y Ni2+) como adsorbatos y nanotubos de carbono (NTC) como adsorbentes. Primero, se determinaron las propiedades termodinámicas y QSAR a 298.15 y 30815K utilizando simulación computacional. Posteriormente, se desarrollaron redes neuronales artificiales Feedforward backprop y Elman backprop, en donde la red con mayor precisión de las propiedades termodinámicas y QSAR fue, la Elman Backprop con la función Logsig utilizando 5 y 3 neuronas en la capa oculta a 298.15 y 308.15 K, por otro lado, las redes tuvieron una r2 de 0.999, y un error cuadrático medio de 0.021, 0.024 y 0.214 respectivamente.
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Derechos de autor 2023 Norma Aurea Rangel-Vázquez, Billy Alberto Ávila-Camacho
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Las obras publicadas en versión electrónica de la revista están bajo la licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
