Abstract—The process of coagulation and flocculation is one of the most important operations among the water purification process, but its effectiveness is affected due to the calculation of the coagulant dosage which is performed by the Jar tests or the use of the Streaming Current Detector (SCD),having as main disadvantage that it does not take into account the change of the physiochemical parameters of the water in real time and also the need to obtain an optimal operation point for the equipment. In this paper the optimal dosage of Aluminum Sulfate(Al2(SO4)3 18H2O) is determined using a model of Artificial Neural Network (ANN) that, when faced with real time variations of turbidity is able to calculate an indicated dose of coagulant, with the aim of achieve effective coagulation in the trial water and avoid excessive or insufficient presence of coagulant, minimize the need to make jars test continuously and reduce economic losses due to inadequate spending of coagulant.
The ANN created is able to calculate the dosage based on the value of initial turbidity of the fluid to be treated with a MSE 0 mg/L, achieving removal percentages greater than 93% for most cases.
Index Terms—Aluminum sulfate (Al2(SO4)3 18H2O), artificial neural networks, coagulation, flocculation, optimal dosage of coagulant.
A. J. León-Luque and C. L. Barajas G. are with the Faculty of Environmental Engineer, Santo Tomás Univeristy, Bogotá, Colombia (email: firstname.lastname@example.org, email@example.com).
C. A. Peña-Guzmán is with the Department of Environmental Engineer, Faculty of Engineer, Santo Tomás Univeristy and Universidad Autónoma de Colombia, Bogotá, Colombia (e-mail: firstname.lastname@example.org, email@example.com).
Cite: A. J. León-Luque, C. L. Barajas, and C. A. Peña-Guzmán, "Determination of the Optimal Dosage of Aluminum Sulfate in the Coagulation-Flocculation Process Using an Artificial Neural Network," International Journal of Environmental Science and Development vol. 7, no. 5, pp. 346-350, 2016.