Tese: Predicting dry gas seals reliability with machine learning techniques developed from scarce data
Aluno(a) : Matheus Hoffmann BritoOrientador(a): Helon Ayala
Área de Concentração: Mecânica Aplicada
Data: 19/08/2022
Link para tese/dissertação: https://doi.org/10.17771/PUCRio.acad.61107
Resumo: The correct equipment operation in the Oil and Gas (O&G) industry is essential to mitigate environmental, human, and financial losses. In this scenario, dry gas seals (DGS) of centrifugal compressors were studied, as they are identified as the most critical device due to the extent of the caused damage. In this study, regression models were developed using machine learning (ML) techniques from scarce data to replace numerical simulations in predicting the operational reliability of DGSs. First, a model based on Computational Fluid Dynamics (CFD) simulation was validated to represent the gas flowing between the sealing faces, to enable the calculation of the equipment’s operational reliability. Thus, the open-source CFD software OpenFOAM was used together with the substance database of the software REFPROP, to allow the user to define the gas mixture and the evaluated operational conditions. Then, two case studies were carried out following a proposed generic workflow. The first comprised determining a regression model to estimate the reliability of a DGS whose mixture composition is fixed but its operating conditions can vary. The second consisted of determining a more robust regressive model, where both the mixture composition and the operational conditions can vary. Finally, the feasibility of implementing both models under real operating conditions was evaluated.