Next Article in Journal
Key Drivers behind the Adoption of Electric Vehicle in Korea: An Analysis of the Revealed Preferences
Previous Article in Journal
Using the Fuzzy Delphi Method to Study the Construction Needs of an Elementary Campus and Achieve Sustainability
Open AccessArticle

Combined Experimental and Field Data Sources in a Prediction Model for Corrosion Rate under Insulation

1
School of Engineering, Faculty Science and Technology, Quest International University Perak, Ipoh 30250, Perak, Malaysia
2
Mechanical Engineering Department, Universiti Teknologi PETRONAS, Sri Iskandar 31750, Perak, Malaysia
3
Electrical and Electronics Engineering Department, Universiti Teknologi PETRONAS, Sri Iskandar 31750, Perak, Malaysia
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(23), 6853; https://doi.org/10.3390/su11236853
Received: 8 September 2019 / Revised: 26 October 2019 / Accepted: 9 November 2019 / Published: 2 December 2019
(This article belongs to the Section Sustainable Engineering and Science)
Corrosion under insulation (CUI) is one of the increasing industrial problems, especially in chemical plants that have been running for an extended time. Prediction modeling, which is one of the solutions for this issue, has attracted increasing attention and has been considered for several industrial applications. The main objective of this work was to investigate the effect of combined data input in prediction modeling, which could be applied to improve the existing CUI rate prediction model. Experimental data and field historical data were gathered and simulated using an artificial neural network separately. To analyze the effect of data sources on the final corrosion rate under the insulation prediction model, both sources of data from experiment and field data were then combined and simulated again using an artificial neural network. Results exhibited the advantages of combined input data type from the experiment and field in the final prediction model. The model developed clearly shows the occurrence of corrosion by phases, which are uniform corrosion at the early phases and pitting corrosion at the later phases. The prediction model will enable better mitigation actions in preventing loss of containment due to CUI, which in turn will improve overall sustainability of the plant. View Full-Text
Keywords: prediction rate model; artificial neural network; corrosion under insulation; experimental data input and field data input prediction rate model; artificial neural network; corrosion under insulation; experimental data input and field data input
Show Figures

Figure 1

MDPI and ACS Style

Burhani, N.R.A.; Muhammad, M.; Rosli, N.S. Combined Experimental and Field Data Sources in a Prediction Model for Corrosion Rate under Insulation. Sustainability 2019, 11, 6853.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop