Aplicação de modelos de aprendizado de máquina para predição de variáveis de processos industriais: um estudo de caso em uma indústria petroquímica

Abstract

This work presents the analysis of machine learning models applied to the prediction of variables in industrial processes. More specifically, different approaches are explored, including Linear Regression, Non-Linear Regression, Decision Tree Regression with the XBoost algorithm, Recurrent Neural Networks and the use of Long Short-Term Memory (LSTM). The research highlights the relevance and usefulness of these models in the context of system identification, providing a comprehensive overview of their characteristics, benefits, and limitations. The work emphasizes the importance of choosing the appropriate model based on the characteristics of the problem at hand and the available information. To validate the effectiveness of these models, detailed comparisons of their performance and results are presented. This comparative analysis allows for a better understanding of the advantages and disadvantages of each approach, thus enabling informed decisions when applying machine learning to system identification.