Estudo das aplicações de data science e machine learning na engenharia química
Resumen
Today, with the advancement of technology and the ability of computers to store huge
amounts of data, companies and industries have sought out professionals trained to deal with
the challenge of organizing and extracting meaningful insights from those data and conferring
competitive advantages such as demand prediction, fault detection and exploitation of
standards. The area that deals with this branch is called Data Science, and employs a
combination of statistical, mathematical and computational resources to make inferences
about patterns found at the data, which would hardly be detected only by human analysis.
Combined with it, the use of algorithms capable of learning and predicting patterns called
Machine Learning are driving the industry by introducing artificial intelligence to solve this
type of problem. The advancement of computing, as well as the popularization of
programming and the emergence of complete mathematical libraries has made the use of
these tools increasingly everyday for chemical engineers, especially in the areas of process
analysis and optimization. In this context, the work presented here aimed to perform a
bibliographic and theoretical review of the current context of data in chemical engineering to
elucidate the theory behind supervised machine learning algorithms, their advantages,
limitations and applications before 3 case studies relevant to chemical engineering. The
results showed that the accuracy of the models studied vary according to the application
given to them, having, in general, more flexible algorithms (Tree Models and Artificial
Neural Networks) performed better for nonlinear and classification problems, while more
rigid algorithms (Simple and Multiple Linear Regression) performed better in experiments in
which the nature of the relationship between the input and output variables was already
known to be linear. The knowledge of physical-chemical relationships of the experiments
under analysis also contributed to the construction of more accurate models.
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