MÉTODOS DE MACHINE LEARNING PARA CLASSIFICAÇÃO DA TEMPERATURA NO PROCESSO DE MISTURA EM UMA PLANTA INDUSTRIAL

Authors

  • Glaucia Maria Bressan Universidade Tecnológica Federal do Paraná https://orcid.org/0000-0001-6996-3129
  • Guilherme da Cunha Universidade Tecnológica Federal do Paraná. Câmpus Cornélio Procópio
  • Wagner Endo Universidade Tecnológica Federal do Paraná. Câmpus Cornélio Procópio https://orcid.org/0000-0002-4985-482X

Abstract

The goal of this paper is the application and analysis of Machine Learning methods to classify the temperature resulting from the liquids mixing process in a didactic industrial plant located in the UTFPR of the Cornélio Procópio city. The applicable methods for this classification are k-nearest neighbors (KNN), Decision Trees, Random Forest and Naive- Bayes. For this task, the input variables considered are the percentage of valve opening, the flow and the opening time; and the output variable is temperature, discretized into 5 classes. The performance of the algorithms is analyzed considering the accuracy and relevant statistical measures and the implementations of the methods are made using the R software. The results show a good performance of the algorithms in the task of classifying the temperature of the liquid mixing process, with accuracy above 90%.

Published

2021-09-02

Issue

Section

Artigos