Corrosion Simulation and Optimization of Sacrificial Anode Protection of a Buried Pipeline Using Teaching Learning Based Optimization (TLBO) Algorithm
Bibliographic record
Abstract
The permanent presence of metallic pipelines in the soil produces electrochemical reactions, which leads to the corrosion activity and thus, an adequate cathodic protection strategy is required.The purpose of this paper is to assess the effect of inductive coupling between an EHV overhead power line and a buried metallic pipeline in normal operation; and to optimize a sacrificial anode cathodic protection system from corrosion evolution using a new efficient meta-heuristic algorithm of Teaching Learning Based Optimization (TLBO).The results obtained indicate that the induced voltage resulting from the inductive coupling exceeds the limit recommended by the majority of international standards; the calculated value of corrosion current density presents a relevant parameter having a significant effect on the corrosion rate and metal loss.Therefore; the selected optimization algorithm proves to be accurate in determining the parameters associated with the design of the sacrificial anode cathodic protection system and is able to meet the current requirement criterion necessary for the protection implemented and to ensure the stability and optimal performance of the hydrocarbon transportation pipeline system.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".