Adsorción de metales pesados (Hg2+, Cu2+ y Ni2+) en NTC utilizando redes neuronales Feed forward backprop y Elman backprop



Palabras clave:

Energía de Gibbs, metales pesados, Redes neuronales artificiales, Hyperchem, Gaussian, NTC


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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