Neural network-based pH and coagulation adjustment system in water treatment

Oscar Ivan Vargas Mora, Daiam Camilo Parrado Nieto, Jairo David Cuero Ortega, Javier Eduardo Martinez Baquero, Robinson Jimenez Moreno


This document presents a machine learning model development as a tool to improve chemical dosing procedure in ariari regional aqueduct (ARA). The supervised learning model has been addressed starting from the knowledge of data color, turbidity and pH at the water inlet to the aqueduct and the dosing results of type A aluminum sulfate and calcium oxide (lime) obtained through jar tests. The construction of the automatic learning model had a comprehensive implementation and improvement field through continuous system training, which allowed an optimal dosage of Aluminum Sulfate and Lime to generate an outlet pH less than 7.5 and outlet turbidity less than 8 nephelometric turbidity unit (NTU). Those outlet water parameters meet the ministry of social protection criteria in Colombia. Also, a virtual jar test was created to reduce the time required to obtain chemical dosing values to less than a minute. In contrast, a laboratory test takes approximately a half-hour to displays results.


Artificial intelligence; Chemical dosing; Machine learning; Supervised; Water treatment

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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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