Automation Via Robotic Process Automation in Pipeline Integrity Management Towards ALARP Risk Level
Bibliographic record
Abstract
Abstract PETRONAS is operating total of more than 1,200 km in total length of pipeline onshore/offshore transporting processed and semi-process hydrocarbon with total no of >500 nos of pipelines within Peninsular of Malaysia as well as Sabah and Sarawak in Borneo. PETRONAS Group Technical Solutions (GTS) as a Centre of Excellence (COE) is providing services in design engineering as well as integrity solutions to pipeline operators operating assets wholly owned by and partly owned by PETRONAS operating within Malaysia as well as overseas. Records shows that aging facilities in the upwards trending approaching or beyond design life. Thus, for the past 10 years, PETRONAS has adopted ISO/TS 12747 – Recommended practice for pipeline life extension in ascertain current and future integrity of aging pipeline and determine risk-based inspection plan. Other than managing aging facilities, GTS also providing solutions in managing pipeline integrity for all major pipeline threat which requires proactive approach to reduce the risk at ALARP Level. One of the pipeline threats is geohazard due to soil movement along the pipeline right of way. EML Survey was conducted but requires further assessment by pipeline engineers to determine the severity to the pipeline due this soil movement. Each integrity assessment maybe painstaking and repetitive with duration of 1 months to 6 months based on the severity and complexity of inspection records. Taking advantage of PETRONAS inhouse digital platform data icloudbased and to align with Industrial Revolution 4.0, PETRONAS has embarked Robotic Process Automation to leverage on the digital data and to improve on the productivity. PETRONAS Group Digital, were consulted to assist on the development of RPA using commercialised software available in the market. This paper describes the process of RPA PipeRBot - Pipeline Integrity Assessments Virtual Robots to assist our Engineers to perform Integrity Assessments and increase process cycle efficiency. This PiperBothas completed proof of concept and ready to for deployment. With this Automation, we have achieved more than 50% of process efficiency and increase in productivity and more cost saving to end users which is pipeline operators, operating assets wholly owned by and partly owned by PETRONAS operating within Malaysia as well as overseas in PETRONAS Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".