Identification and Analysis of Corrosion Mechanisms for Ground Pipelines with Hanging Rings
Bibliographic record
Abstract
Recently, corrosion perforation has been frequently seen in surface pipelines in the oil and gas industry, resulting in operational and environmental challenges. Due to the complex characteristics and mechanisms of such corrosion, a new and pragmatic method has been designed to identify and evaluate the corrosion phenomenon via a hanging ring installed in a surface pipeline. In addition to respectively analyzing the ions of water samples with chemical titration, ion chromatography, and mass spectrometry, the micro-surface morphology of the corroded hanging rings was observed and evaluated by using a scanning electron microscope (SEM) equipped with energy dispersive spectroscopy (EDS), and the surface composition of the corroded hanging rings was analyzed by using X-ray diffraction (XRD). The water ions of each selected position were found to mainly contain Ca2+, Ba2+, SO42−, and HCO3−, while the barium scale and calcium carbonate scale were formed in situ. In addition to the common corrosion induced by CO2, corrosion induced by both CO2 and H2S leads to extremely serious corrosion and scaling in surface pipelines. In addition, the injection dose of corrosion inhibitor was also evaluated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".