Risk analysis of Ex-spool 16” mol: an insight of machine learning and experimental result

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.259858

Keywords:

Root-cause-analysis, Wall thinning, Sand Abrasion, Pearson Multicollinear Matrix, Supervised Machine Learning

Abstract

The paper reports the development of a Risk-Based Inspection (RBI)-Machine Learning perspective. The Optical Emission Spectrometry (OES), Tensile and Hardness Test, Scanning Electron Microscope (SEM), Energy Dispersive X-Ray Spectroscopy (EDS), Sulfate Reducing Bacteria Check, and X-Ray Diffraction (XRD) was used to analyze the root cause of the pipeline’s failure. Corrosion attack shows at the cross-section microstructure based on SEM results. Carbon, Manganese, Phosphorous, and sulfur’s chemical composition is dramatically lower than the standard API 5L Grade X42. Siderite and hematite dominate the composition of the corroded area as a result of CO2 dissolving in water. In contrast, hematite is generated due to the pipe and outdoor atmosphere reaction. Severe local wall thinning of the sand abrasion causes the degradation of the material’s mechanical properties and increases the corrosion rate. This result amplifies by the development of Machine Learning (ML) of Pearson Multicollinear Matrix and Supervised ML (Random Forest, Support Vector Machine, and Linear Regression) to estimate the corrosion degradation of the material. The source of datasets provided by ILI inspection includes the calculated PoF Remaining Useful Life (RuL) as input data, while Probability of Failure (PoF) prediction serves as output data. The Random Forest shows superior predictions of 92.18 %, with the lowest validation loss of 0.0316. The modeling result confirms the experimental outcome. This work demonstrates the implementation strategy to reduce the analysis time, minimize human bias, and serve as a reliable reference tool and guideline to maintain the integrity of the subsea pipelines.

Author Biographies

Taufik Aditiyawarman, Universitas Indonesia

Master of Science, Doctoral Degree Student

Department of Metallurgy and Material Engineering

Johny Wahyuadi Soedarsono, Universitas Indonesia

Doctor of Engineering, Professor

Department of Metallurgy and Material Engineering

Agus Paul Setiawan Kaban, Universitas Indonesia

Master of Engineering, Doctoral Degree Student

Prof Johny Wahyuadi Laboratory

Department of Metallurgical and Materials Engineering

Rini Riastuti, Universitas Indonesia

Doctor of Engineering, Senior Lecturer

Prof Johny Wahyuadi Laboratory

Department of Metallurgical and Materials Engineering

Haryo Rahmadani, Universitas Indonesia

Bachelor of Engineering, Surface Facility Engineer

Prof Johny Wahyuadi Laboratory

Department of Metallurgical and Materials Engineering

Mohammad Pribadi, Universitas Indonesia

Master of Science, Senior Engineer

Prof Johny Wahyuadi Laboratory

Department of Metallurgical and Materials Engineering

Rizal Tresna Ramdhani, Universitas Indonesia

Bachelor of Science, Bachelor of Engineering, Master of Engineering, Senior Engineer

Prof Johny Wahyuadi Laboratory

Department of Metallurgical and Materials Engineering

Sidhi Aribowo, Universitas Indonesia

Master of Science, Senior Engineer

Prof Johny Wahyuadi Laboratory

Department of Metallurgical and Materials Engineering

Suryadi Suryadi, Universitas Indonesia

Master of Engineering, Senior Researcher

Center for Materials Processing and Failure Analysis (CMPFA)

Manufacturing Research Center

Faculty of Engineering

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Published

2022-06-30

How to Cite

Aditiyawarman, T., Soedarsono, J. W., Kaban, A. P. S., Riastuti, R., Rahmadani, H., Pribadi, M., Ramdhani, R. T., Aribowo, S., & Suryadi, S. (2022). Risk analysis of Ex-spool 16” mol: an insight of machine learning and experimental result. Eastern-European Journal of Enterprise Technologies, 3(12 (117), 20–33. https://doi.org/10.15587/1729-4061.2022.259858

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Vol. 3 No. 12 (117) (2022): Materials Science

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Materials Science

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Copyright (c) 2022 Taufik Aditiyawarman, Johny Wahyuadi Soedarsono, Agus Paul Setiawan Kaban, Rini Riastuti, Haryo Rahmadani, Mohammad Pribadi, Rizal Tresna Ramdhani, Sidhi Aribowo, Suryadi Suryadi

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