Adsorption of heavy metals (Hg2+, Cu2+ y Ni2+) on CNT using Feed forward backprop and Elman backprop neural networks
DOI:
https://doi.org/10.33064/iycuaa2023894207Keywords:
Gibbs energy, heavy metals, Artificial neuronal networks, Hyperchem, Gaussian, CNTAbstract
In the present work, mono and multicomponent adsorption systems of heavy metals (Hg2+, Cu2+ y Ni2+) as adsorbates and carbon nanotubes (CNT) as adsorbents were studied. First, the thermodynamic and QSAR properties at 298.15 and 30815K were determined using computational simulation. Subsequently, Feedforward backprop and Elman backprop artificial neural networks were developed, where the network with the highest precision of the thermodynamic and QSAR properties was the Elman Backprop with the Logsig function using 5 and 3 neurons in the hidden layer at 298.15 and 308.15 K, finally, the networks had an r2 of 0.999, and a mean square error of 0.021, 0.024 and 0.214 respectively.
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