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All Journal Syntax Literate: Jurnal Ilmiah Indonesia Journal of Materials Exploration and Findings
Hartoyo, Fernanda
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Humanities Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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The Optimization Of Failure Risk Estimation On The Uniform Corrosion Rate With A Non-Linear Function Hartoyo, Fernanda; Fatriansyah, Jaka Fajar; Mas'ud, Imam Abdillah; Digita, Farhan Rama; Ovelia, Hanna; Asral, D. Rizal
Journal of Materials Exploration and Findings Vol. 1, No. 1
Publisher : UI Scholars Hub

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Abstract

Failures in the oil and gas pipeline system are conditions that must be avoided and anticipated because the losses due to the failures can occur at a very high level. Internal corrosion is one of the significant causes of the failures in pipeline systems. In addition, this type of corrosion is due to the high content of carbon dioxide and other corrosive substances in crude oil and natural gas. Therefore, an optimal inspection scheduling system is required to prevent the possibility of pipeline failures due to corrosion and to avoid any overspending on the budget due to excessive inspection scheduling. Risk-based testing (RBI) is one of the best methods to define a test planning system by using an optimal risk assessment. In this article, a Monte Carlo random number generator is applied by using a huge number of random iterations to approximate the actual risk value of a pipeline system with a limited sample at the scene. The nonlinear corrosion rate function is used for comparison with the commonly used linear corrosion rate function based on ASTM G-16 95. Once a risk value is estimated, the value is monitored based on an assessment of the risk matrix for each corrosion rate function by using the RBI method. The results show that the nonlinear corrosion rate function provides a more accurate approach to estimating the actual risk value and ultimately leads to an optimal inspection planning system.
DYNAMIC RBI WITH CENTRAL DIFFERENCE METHOD APPROACH IN CALCULATION OF UNIFORM CORROSION RATE: A CASESTUDY ON GAS PIPELINES Alviansyah, M.Riefqi Dwi; Hartoyo, Fernanda; Nurullia, Zahra Nadia; Kurniawan, Ari
Journal of Materials Exploration and Findings Vol. 1, No. 2
Publisher : UI Scholars Hub

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Abstract

The oil and gas industry generally uses a piping system to drain fluids. Even though the pipes used have been well designed, the use of pipes as a means of fluid transportation still provides the possibility of failure that can occur at any time, one of which is due to uniform corrosion. The use of standard Risk Based Inspection (RBI) according to the API RBI 581 document has been widely used to anticipate potential failures to pipe components. The use of standard RBI can reduce the risk of failure significantly. Because the standard RBI considers the component risk value to be constant, it causes an error in the component status assessment. It is unfortunate happen, if an industry fails due to an error in the inspection results, causing financial losses. This research will design dynamic RBI using thickness data of 12 PT.X inspection points in 5 inspection time intervals. The results showed that the dynamic RBI design that was compiled could provide real-time component condition status, capture fluctuations in the corrosion rate that occurred, and provide an accurate description of the actual component condition. RBI design makes inspection and maintenance planning more precise by reducing the frequency of redundant inspections and the possibility of inspection planning errors.
Perancangan Program Pengestimasi Probabilitas Kegagalan Peralatan Penukar Panas Akibat Korosi Seragam Berbasis Deep Neural Network Fatriansyah, Jaka Fajar; Dhaneswara, Donanta; Hanifa, Muthia; Hartoyo, Fernanda; Pradana, Agrin Febrian; Anis, Muhammad; Fauzi, Andrian
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (345.943 KB) | DOI: 10.36418/syntax-literate.v8i3.11486

Abstract

Meningkatnya standar keamanan dan ketatnya persaingan antar perusahaan meningkatkan kebutuhan bagi suatu perusahan untuk mengendalikan kegagalan pada peralatan. Inspeksi secara teratur dilakukan sebagai bagian dari rangkaian pemeliharaan dan manajemen integritas peralatan. Dalam merencanakan dan melakukan inspeksi, diperlukan strategi yang tepat agar inspeksi yang dilakukan tepat sasaran dan sesuai dengan kebutuhan. Risk-based inspection merupakan teknik pengambilan keputusan dalam perencanaan pemeliharaan yang berdasar pada risiko. Pada saat ini, penggunaan metode-metode kecerdasan buatan untuk kegiatan penilaian risiko, pemodelan konsekuensi, dan perencanaan pemeliharaan telah dilakukan. Penelitian ini bertujuan untuk mengembangkan suatu program yang memanfaatkan pembelajaran mesin dan kecerdasan buatan untuk melakukan penilaian salah satu komponen risiko yaitu probabilitas kegagalan (Probability of Failure, PoF) pada bagian cangkang dalam peralatan penukar panas menggunakan deep learning. Model ini dapat membantu operator yang bekerja di bidang minyak dan gas untuk menentukan tingkatan risiko sehingga inspeksi dapat dilakukan dengan lebih efisien dan terarah. Penelitian ini menghasilkan sebuah program dan disain program pembelajaran mesin berbasis deep learning yang digunakan untuk memprediksi risiko kegagalan akibat korosi seragam pada peralatan sisi dalam cangkang peralatan penukar panas cangkang dan buluh (shell-and-tube heat exchanger) berdasarkan standar API 581 dengan akurasi sebesar 89% yang didapatkan dengan parameter-parameter diantaranya learning rate sebesar 0.001, epoch sebesar 150, random state sebesar 60, tiga hidden layer, dan test size sebesar 0.2.
Co-Authors Alviansyah, M.Riefqi Dwi ARI KURNIAWAN Asral, D. Rizal Dhaneswara, Donanta Digita, Farhan Rama Fatriansyah, Jaka Fajar Fauzi, Andrian Hanifa, Muthia Mas'ud, Imam Abdillah Muhammad Anis, Muhammad Nurullia, Zahra Nadia Ovelia, Hanna Pradana, Agrin Febrian