Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm

Hao Li; Guoming Yang; Jing Xin; Ying Wu; Guangting Xue

doi:doi:10.11648/j.ogce.20180602.11

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Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm

Hao Li, Guoming Yang, Jing Xin, Ying Wu, Guangting Xue

Published in International Journal of Oil, Gas and Coal Engineering (Volume 6, Issue 2)

Received: 26 April 2018 Accepted: 22 May 2018 Published: 19 June 2018

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Abstract

The corrosion of low-temperature sections of a company's atmospheric and vacuum distillation unit was analyzed. Corrosion rate prediction model was established using BP neural network based on the corrosion detection data detected in the sewage on top of the tower over a period of time. In this model, the pH value, Cl ion concentration, Fe ion concentration and sulfide concentration of the sewage discharged from the top of the tower are taken as the input data, and the average corrosion rate as the output data, the results show that the prediction error is large. The BP neural network was optimized using the genetic algorithm. The optimized model could accurately predict the corrosion of the atmospheric unit at low temperatures. The corrosion rate prediction model was used to investigate the effect of each variable on the corrosion rate through the single factor change and the results could reflect the relationship between detected corrosion data and corrosion rate in the sewage on top of the atmospheric tower.

Published in	International Journal of Oil, Gas and Coal Engineering (Volume 6, Issue 2)
DOI	10.11648/j.ogce.20180602.11
Page(s)	25-33
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Atmospheric Distillation Tower Corrosion, BP Neural Network, Genetic Algorithm, Corrosion Rate Prediction

References

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APA Style

Hao Li, Guoming Yang, Jing Xin, Ying Wu, Guangting Xue. (2018). Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm. International Journal of Oil, Gas and Coal Engineering, 6(2), 25-33. https://doi.org/10.11648/j.ogce.20180602.11

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ACS Style

Hao Li; Guoming Yang; Jing Xin; Ying Wu; Guangting Xue. Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm. Int. J. Oil Gas Coal Eng. 2018, 6(2), 25-33. doi: 10.11648/j.ogce.20180602.11

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AMA Style

Hao Li, Guoming Yang, Jing Xin, Ying Wu, Guangting Xue. Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm. Int J Oil Gas Coal Eng. 2018;6(2):25-33. doi: 10.11648/j.ogce.20180602.11

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@article{10.11648/j.ogce.20180602.11,
  author = {Hao Li and Guoming Yang and Jing Xin and Ying Wu and Guangting Xue},
  title = {Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm},
  journal = {International Journal of Oil, Gas and Coal Engineering},
  volume = {6},
  number = {2},
  pages = {25-33},
  doi = {10.11648/j.ogce.20180602.11},
  url = {https://doi.org/10.11648/j.ogce.20180602.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ogce.20180602.11},
  abstract = {The corrosion of low-temperature sections of a company's atmospheric and vacuum distillation unit was analyzed. Corrosion rate prediction model was established using BP neural network based on the corrosion detection data detected in the sewage on top of the tower over a period of time. In this model, the pH value, Cl ion concentration, Fe ion concentration and sulfide concentration of the sewage discharged from the top of the tower are taken as the input data, and the average corrosion rate as the output data, the results show that the prediction error is large. The BP neural network was optimized using the genetic algorithm. The optimized model could accurately predict the corrosion of the atmospheric unit at low temperatures. The corrosion rate prediction model was used to investigate the effect of each variable on the corrosion rate through the single factor change and the results could reflect the relationship between detected corrosion data and corrosion rate in the sewage on top of the atmospheric tower.},
 year = {2018}
}

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TY  - JOUR
T1  - Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm
AU  - Hao Li
AU  - Guoming Yang
AU  - Jing Xin
AU  - Ying Wu
AU  - Guangting Xue
Y1  - 2018/06/19
PY  - 2018
N1  - https://doi.org/10.11648/j.ogce.20180602.11
DO  - 10.11648/j.ogce.20180602.11
T2  - International Journal of Oil, Gas and Coal Engineering
JF  - International Journal of Oil, Gas and Coal Engineering
JO  - International Journal of Oil, Gas and Coal Engineering
SP  - 25
EP  - 33
PB  - Science Publishing Group
SN  - 2376-7677
UR  - https://doi.org/10.11648/j.ogce.20180602.11
AB  - The corrosion of low-temperature sections of a company's atmospheric and vacuum distillation unit was analyzed. Corrosion rate prediction model was established using BP neural network based on the corrosion detection data detected in the sewage on top of the tower over a period of time. In this model, the pH value, Cl ion concentration, Fe ion concentration and sulfide concentration of the sewage discharged from the top of the tower are taken as the input data, and the average corrosion rate as the output data, the results show that the prediction error is large. The BP neural network was optimized using the genetic algorithm. The optimized model could accurately predict the corrosion of the atmospheric unit at low temperatures. The corrosion rate prediction model was used to investigate the effect of each variable on the corrosion rate through the single factor change and the results could reflect the relationship between detected corrosion data and corrosion rate in the sewage on top of the atmospheric tower.
VL  - 6
IS  - 2
ER  -

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Author Information

Hao Li

Petroleum Refining Research Institute, China National Offshore Oil Corporation Research, Beijing, China
Guoming Yang

Petroleum Refining Research Institute, China National Offshore Oil Corporation Research, Beijing, China
Jing Xin

Petroleum Refining Research Institute, China National Offshore Oil Corporation Research, Beijing, China
Ying Wu

Petroleum Refining Research Institute, China National Offshore Oil Corporation Research, Beijing, China
Guangting Xue

Petroleum Refining Research Institute, China National Offshore Oil Corporation Research, Beijing, China

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Plain Text BibTeX RIS

APA Style

Hao Li, Guoming Yang, Jing Xin, Ying Wu, Guangting Xue. (2018). Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm. International Journal of Oil, Gas and Coal Engineering, 6(2), 25-33. https://doi.org/10.11648/j.ogce.20180602.11

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ACS Style

Hao Li; Guoming Yang; Jing Xin; Ying Wu; Guangting Xue. Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm. Int. J. Oil Gas Coal Eng. 2018, 6(2), 25-33. doi: 10.11648/j.ogce.20180602.11

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AMA Style

Hao Li, Guoming Yang, Jing Xin, Ying Wu, Guangting Xue. Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm. Int J Oil Gas Coal Eng. 2018;6(2):25-33. doi: 10.11648/j.ogce.20180602.11

Copy | Download

@article{10.11648/j.ogce.20180602.11,
  author = {Hao Li and Guoming Yang and Jing Xin and Ying Wu and Guangting Xue},
  title = {Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm},
  journal = {International Journal of Oil, Gas and Coal Engineering},
  volume = {6},
  number = {2},
  pages = {25-33},
  doi = {10.11648/j.ogce.20180602.11},
  url = {https://doi.org/10.11648/j.ogce.20180602.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ogce.20180602.11},
  abstract = {The corrosion of low-temperature sections of a company's atmospheric and vacuum distillation unit was analyzed. Corrosion rate prediction model was established using BP neural network based on the corrosion detection data detected in the sewage on top of the tower over a period of time. In this model, the pH value, Cl ion concentration, Fe ion concentration and sulfide concentration of the sewage discharged from the top of the tower are taken as the input data, and the average corrosion rate as the output data, the results show that the prediction error is large. The BP neural network was optimized using the genetic algorithm. The optimized model could accurately predict the corrosion of the atmospheric unit at low temperatures. The corrosion rate prediction model was used to investigate the effect of each variable on the corrosion rate through the single factor change and the results could reflect the relationship between detected corrosion data and corrosion rate in the sewage on top of the atmospheric tower.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm
AU  - Hao Li
AU  - Guoming Yang
AU  - Jing Xin
AU  - Ying Wu
AU  - Guangting Xue
Y1  - 2018/06/19
PY  - 2018
N1  - https://doi.org/10.11648/j.ogce.20180602.11
DO  - 10.11648/j.ogce.20180602.11
T2  - International Journal of Oil, Gas and Coal Engineering
JF  - International Journal of Oil, Gas and Coal Engineering
JO  - International Journal of Oil, Gas and Coal Engineering
SP  - 25
EP  - 33
PB  - Science Publishing Group
SN  - 2376-7677
UR  - https://doi.org/10.11648/j.ogce.20180602.11
AB  - The corrosion of low-temperature sections of a company's atmospheric and vacuum distillation unit was analyzed. Corrosion rate prediction model was established using BP neural network based on the corrosion detection data detected in the sewage on top of the tower over a period of time. In this model, the pH value, Cl ion concentration, Fe ion concentration and sulfide concentration of the sewage discharged from the top of the tower are taken as the input data, and the average corrosion rate as the output data, the results show that the prediction error is large. The BP neural network was optimized using the genetic algorithm. The optimized model could accurately predict the corrosion of the atmospheric unit at low temperatures. The corrosion rate prediction model was used to investigate the effect of each variable on the corrosion rate through the single factor change and the results could reflect the relationship between detected corrosion data and corrosion rate in the sewage on top of the atmospheric tower.
VL  - 6
IS  - 2
ER  -

Copy | Download