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dc.contributor.authorAnand, V.
dc.contributor.authorSalasi, Mobin
dc.contributor.authorRisbud, Mandar
dc.contributor.authorGubner, Rolf
dc.contributor.editorN/A
dc.date.accessioned2017-01-30T15:32:34Z
dc.date.available2017-01-30T15:32:34Z
dc.date.created2015-05-22T08:32:25Z
dc.date.issued2014
dc.identifier.citationAnand, V. and Salasi, M. and Risbud, M. and Gubner, R. 2014. Low cost development of flowlines - Selection criteria of corrosion resistant alloys flowlines, in Corrosion 2014: Collaborate, Educate, Innovate, Mitigate, Mar 9 2014. San Antonio, United States: National Association of Corrosion Engineers International.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/47324
dc.description.abstract

Corrosion resistant alloys (CRA) are often used for well-head equipment and the first length of flowlines, until the application of corrosion inhibited carbon steel becomes a viable choice. The objective of this research is to develop a cost effective and reliable material selection model based on experimental data with the help of Artificial Neural Network (ANN). Experiments were carried out in a jet impingement cell based on the Taguchi's orthogonal array (OA). Each steel specimen was subjected to specific conditions involving a pH range of 3-5, chloride concentrations between 1 wt% and 12 wt%, acetic acid range 50-600 ppm, temperatures in the range 100° C - 175° C and partial pressure of CO2 was 10bar. Pitting potentials (Epit) were extracted from cyclic polarization tests. The ANN was used to process the experimental results and to predict pitting potentials for various operational conditions. A good correlation between the experimental results and predicted data was found. Additional experiments were conducted to validate the predicted values. The developed ANN was used to simulate pitting potential of 316L as a function of pH, chloride concentration, acetic acid concentration, temperature and the resulting corrosion domain diagrams are presented.

dc.publisherNational Assoc. of Corrosion Engineers International
dc.subjectDomain diagram
dc.subjectTaguchi orthogonal array
dc.subjectPitting potential
dc.subjectArtificial neural network
dc.titleLow cost development of flowlines - Selection criteria of corrosion resistant alloys flowlines
dc.typeConference Paper
dcterms.source.issn03614409
dcterms.source.titleCorrosion Conference and Expo 2014: Collaborate. Educate. Innovate. Mitigate.
dcterms.source.seriesCorrosion Conference and Expo 2014: Collaborate. Educate. Innovate. Mitigate.
dcterms.source.conferenceCorrosion 2014: Collaborate. Educate. Innovate. Mitigate
dcterms.source.conference-start-dateMar 9 2014
dcterms.source.conferencelocationSan Antonio; United States
dcterms.source.placeUSA
curtin.departmentSchool of Chemical and Petroleum Engineering
curtin.accessStatusFulltext not available


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