Servicios Personalizados
Revista
Articulo
Indicadores
Citado por SciELO
Accesos
Links relacionados
Similares en SciELO
Compartir
Tecnología y ciencias del agua
versión On-line ISSN 2007-2422
Resumen
MORALES, Ana María; RAMIREZ-CABALLERO, Gustavo y BARAJAS-MENESES, Martha. Predicting the aluminum sulfate dosage in water treatment. Tecnol. cienc. agua [online]. 2020, vol.11, n.6, pp.339-367. Epub 15-Jun-2024. ISSN 2007-2422. https://doi.org/10.24850/j-tyca-2020-06-08.
The present study shows the strategies used to improve the treatment of clarification of demineralized water in GENSA S. A. E. S. P., Planta Termopaipa, located in Boyacá, Colombia. Experimental data obtained from jar tests were used to build a model based on neuronal nets. The independent variables were pH, turbidity, electrical conductivity, and color of the raw water along with flocculent dosage. The output variable was the Aluminum Sulfate dosage. A three-layer neural network was chosen as a prediction approach. The model was validated to find ten neurons in the hidden layer. Nonlinear optimization was the tool used to train the neural network. The chi- square value was used to test the model and showed that the model is efficient at 90% confidence level.
Palabras llave : Coagulation; correlation; cross-validation; neural net; water treatment.