Optimized maintenance plan for oil and gas pipelines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Oil and gas pipelines transport millions of dollars of products every day making the optimization of their long-term maintenance strategies an essential target for practitioners. Thus, this paper aims to optimize the maintenance plan of such pipelines by maximizing their lifetime average condition with the minimum possible cost. This is achieved in three steps: (1) developing a life-cycle cost model for such pipelines to determine their net present value (NPV) based on the different rehabilitation actions applied throughout their lifetime; (2) establishing a strategy to determine the condition index of such pipelines before and after any rehabilitation action applied throughout their lifetime; and (3) formulating the optimization model and applying the elitist non-dominated sorting genetic algorithm (NSGA-II) to determine the optimum maintenance plans for such pipelines. The optimization model is applied to a real-life case study to demonstrate its applicability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it