Prediction of scale thickness in pipelines using artificial neural network and gamma rays by MCNP6 code

Authors

  • William Luna Salgado Universidade Federal do Rio de Janeiro
  • Roos Sophia de Freitas Dam Universidade Federal do Rio de Janeiro
  • Renato Raoni Werneck Affonso Universidade Federal do Rio de Janeiro
  • César Marques Salgado Instituto de Engenharia Nuclear

Abstract

This report presents a method to study the deposition of scale in pipelines of multiphase systems (oil/water/gas), commonly found in the petroleum industry.

Linha principal do CV-28: lente quadripolar e distribuidor magnético

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Published

2021-08-12

How to Cite

Luna Salgado, W. ., Dam, R. S. de F., Affonso, R. R. W. ., & Salgado, C. M. (2021). Prediction of scale thickness in pipelines using artificial neural network and gamma rays by MCNP6 code. Instituto De Engenharia Nuclear: Progress Report, (4). Retrieved from https://revistas.ien.gov.br/index.php/ienprogressreport/article/view/423

Issue

No. 4 (2021): IEN - Progress Report 2018-2020 n.4

Section

ANTI

License

Copyright (c) 2021 William Luna Salgado, Roos Sophia de Freitas Dam, Renato Raoni Werneck Affonso, César Marques Salgado

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.