Application of machine learning models for predicting variables of industrial processes: A case study in a petrochemical industry

This work presents the analysis of machine learning models applied to predict industrial process variables. More specifically, different models are explored, including Linear Regression, Nonlinear Regression, and Linear Regression with XGBoost. The research highlights the relevance 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 and, thus, making informed decisions when applying machine learning to system identification.