Predicción de remoción de contaminantes en lixiviados mediante el uso de Redes Neuronales Artificiales

Descargas: 43

Autores/as

  • Saúl Antonio Rivera-González Instituto Tecnológico Superior de Misantla
  • Yamileth Sordel-López Instituto Tecnológico Superior de Misantla
  • Juan Pablo Rodríguez-Miranda Universidad Distrital Francisco José de Caldas
  • Octavio Salcedo-Parra Universidad Distrital Francisco José de Caldas
  • Luis Carlos Sandoval- Herazo Instituto Tecnológico Superior de Misantla

DOI:

https://doi.org/10.56845/terys.v1i1.158

Palabras clave:

RSU, lixiviados, red neuronal artificial, humedal construido

Citas

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Mendoza-Castillo, D. I., Villalobos-Ortega, N., Bonilla-Petriciolet, A., & Tapia-Picazo, J. C. (2015). Neural network modeling of heavy metal sorption on lignocellulosic biomasses: Effect of metallic ion properties and sorbent characteristics. Industrial and Engineering Chemistry Research, 54(1), 443–453. DOI: https://doi.org/10.1021/ie503619j

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Yoo, J. J., Seo, G., Chua, M. R., Park, T. G., Lu, Y., Rotermund, F., ... & Seo, J. (2021). Efficient perovskite solar cells via improved carrier management. Nature, 590(7847), 587-593. DOI: https://doi.org/10.1038/s41586-021-03285-w

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Publicado

2022-12-16

Cómo citar

Rivera-González, S. A., Sordel-López, Y., Rodríguez-Miranda, J. P., Salcedo-Parra, O., & Sandoval- Herazo, L. C. (2022). Predicción de remoción de contaminantes en lixiviados mediante el uso de Redes Neuronales Artificiales. Tendencias En energías Renovables Y Sustentabilidad, 1(1), 59. https://doi.org/10.56845/terys.v1i1.158

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