Tratamiento De Agua Potable Basado en Inteligencia Artificial E Internet De Las Cosas

Abstract

Water is one of the essential natural resources for life, found in lakes, lagoons, and other sources. However, for consumption, it needs to be treated, and that is where drinking water treatment plants come into play. These plants can enhance their functionality by using modern tools such as Artificial Intelligence (AI) and the Internet of Things (IoT). The article discusses how these techniques, including machine learning, neural networks, support vector machines, neurofuzzy inference systems, and fuzzy logic, can improve the prediction of water quality and demand, optimize the process of drinking water treatment, and control water quality parameters. The methodology used for this study involved a systematic review of various bibliographic sources, scientific journal databases, and academic articles from platforms such as Scopus, IEEE, ScienceDirect, among others. Based on the review, the study determined the most influential water quality parameters and the most effective techniques of AI and IoT to predict, optimize, and control water quality in a drinking water treatment plant. As a result of the research, a flowchart was developed, representing the proposed methodology for the operation of an intelligent system in a drinking water treatment plant based on the most effective techniques of AI and IoT, achieving the purpose of the article. It is worth noting that some of the results obtained were: 100% water quality classification accuracy (FFNN) with respect to the prediction of water quality data, 99.2% success rate for efficiency improvement in the optimization of treatment processes using genetic algorithms and 97.77% effectiveness for the control of water quality parameters using neural networks. Furthermore, this article can serve as a guide for those interested in implementing an intelligent system in a drinking water treatment plant based on artificial intelligence and the Internet of Things.