Modelos de classificação para estimativa de efluentes gasosos de estações de tratamento de esgoto

Abstract

This study aims to contribute to the knowledge on the application of machine learning techniques in the context of wastewater treatment plants. To accomplish this goal, machine learning methods were applied to predict the emission levels of greenhouse gases CO2 and N2 O in a wastewater treatment plant. Data collected by sensors during the treatment process were utilized, and the employed machine learning methods were Random Forests, Support Vector Machines and Neural Networks. The obtained results demonstrated that machine learning models are capable of handling the complexity of time series data and making predictions for output classification. However, it was observed that factors such as data volume and data trend should be considered for better model performance.