Joint Integrity - Bolting Technology Application and Benefits
Bibliographic record
Abstract
Abstract This paper builds upon our previously reported Bolted Joint Integrity Program at a North American upgrader plant with a capacity of 665 KBPD diluted bitumen feed and 300 KBPD production. [1]. The program outlined seven key components necessary to achieve zero leaks in bolted joints. However, issues can arise from incorrect torque application, whether it is over-torqued, under-torqued, or missed entirely, resulting in significant costs. This paper explores the benefits of digitizing the following areas within the plant: • Engineering and analysis. • Code and execution compliance. • Tracking, documentation, and Quality Assurance. • Implementation. Canadian Natural Resources Limited implemented commercial software, which integrates with smart tools (connected hardware) via Wi-Fi, smartphones, or onsite digital tablets to cloud storage and networks and/or computers. This system combines data with Canadian Natural company database, which maintains records for over 100,000 joints. Digital tools and software can enable real-time visibility and tracking of bolting activities, allowing for extensive data analysis of key performance measures. We found a problem being able to verify contractor compliance, especially after the fact, and implemented a tracking and verification program to correct the problem. (Applied to maintenance and turnarounds). Following implementation, plant staff can monitor and record the actual torque applied to each joint in real time using computers, smartphones, or digital tablets, facilitated by broad enhancements. This technology is applicable to all facilities and worksites including maintenance and manufacturing that use bolted joint assemblies. The paper presents the lessons, experience & challenges to save cost and document compliance.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".