eprintid: 9714
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/97/14
datestamp: 2023-11-09 16:36:21
lastmod: 2023-11-09 16:36:21
status_changed: 2023-11-09 16:29:38
type: conference_item
metadata_visibility: show
creators_name: Burhani, N.R.A.
creators_name: Muhammad, M.
creators_name: Ismail, M.C.
title: Corrosion under insulation rate prediction model for piping by two stages of artificial neural network
ispublished: pub
note: cited By 4; Conference of 6th International Conference on Production, Energy and Reliability, ICPER 2018 ; Conference Date: 13 August 2018 Through 14 August 2018; Conference Code:141991
abstract: This work proposes an improved quantitative prediction model for corrosion under insulation (CUI) for oil and gas piping in equatorial climate zone, using real-world data and integrated with experimental work using modified two stages of Artificial Neural Network. Investigation into the effect of single type of data source and different type of fitting line analysis on the final CUI model are also discussed with the goal for Risk Based Inspection (RBI) to be more effective. Results from the CUI prediction model exhibits high R2 of 0.9919 and Root Mean Squared Error (RMSE) of 0.0087. © 2018 Author(s).
date: 2018
publisher: American Institute of Physics Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057301685&doi=10.1063%2f1.5075560&partnerID=40&md5=7f0e34b9e68db27f173d36913a8f1205
id_number: 10.1063/1.5075560
full_text_status: none
publication: AIP Conference Proceedings
volume: 2035
refereed: TRUE
isbn: 9780735417618
issn: 0094243X
citation:   Burhani, N.R.A. and Muhammad, M. and Ismail, M.C.  (2018) Corrosion under insulation rate prediction model for piping by two stages of artificial neural network.  In: UNSPECIFIED.