Equações Diferenciais Estocásticas na Engenharia Química

Ardson dos S. Vianna Jr.; Christian Junior Oliveira

doi:10.14295/vetor.v30i2.12971

Authors

Ardson dos S. Vianna Jr. USP https://orcid.org/0000-0003-0192-7839
Christian Junior Oliveira USP

DOI:

https://doi.org/10.14295/vetor.v30i2.12971

Keywords:

Sample paths, Python, Modeling and Simulation

Abstract

The generation process of a mathematical model should present as a result the simulation, which must represent the experimental data. Several phenomena in Nature show erratic fluctuations that are phenomenological. An expressive example is a path that pollen develops through a river surface, the Brownian movement. Chemical Engineering encompasses several issues that are evaluated considering stochastic processes, such as process optimization and control, diffusion, and chemical kinetics. In the present work, fundamental concepts that are related to stochastic differential equations (SDE) are exposed, as well as some classic examples and one in Chemical Engineering. An open-source tool by Python was used, viewing to generate the sample paths, the Python open tool, specifically the PyPI sdeint algorithm.

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