Adaptive Corrosion Protection System Using Continuous Corrosion Measurement, Parameter Extraction, and Corrective Loop
Bibliographic record
Abstract
A simple current-sourced adaptive corrosion protection system (ACPS) along with a technology to extract the protection current from the Tafel plot is presented. For reliable protection of the target metal, first, the Tafel plot of the target metal is obtained. Subsequently, a novel technique proposed in this paper is used to extract the protection current from the Tafel plot. This extracted protection current is fed to the target metal to protect the metal in the existing corrosive environment. This three-part system is adaptively used to update the required protection current to effectively protect the target metal continuously. All these functionalities are integrated in a stand-alone ACPS that effectively diagnoses the corrosion status and updates the protection parameters without any manual interaction or physical modification of the set-up to offer modularity, reliability, and cost saving. To validate the technique, a laboratory scale system is realized and tested using various metal samples and various corrosive mediums. Using the experimental system, A36 metal coupons are effectively protected with protection (inhibition) efficiency of 40–100 % in different corrosive mediums that can extend the life expectancy of the target metal from ~2 times to more than 100 times for the tested corrosive mediums.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".