ANN and DOE Analysis of Corrosion Resistance Inhibitor for Mild Steel Structures in Iraq
Bibliographic record
Abstract
Modelling of the effect of newly developed mild steel (MS) corrosion inhibitor in Iraq was investigated using artificial neural network (ANN) and Response Surface Methodology Design of Experiment (RSM-DOE) methods. The most significant parameters among the parameters studied and the optimum coating conditions was also investigated. Weight loss method (WLM) as well as Scanning electron microscope (SEM) were used in the experimental work to obtain data for modelling. The inhibitor used was made in the center’s laboratories called N-(3-Nitrobenzylidene)-2-aminobenzothiazole. The MS specimens were tested for different immersion times and corrosive solution temperatures. Different concentrations of the inhibitor from of 0, to 1000 mg/L were used in the study. The results showed that within the concentrations studied, the corrosion inhibition performance increased with increasing N-(3-Nitrobenzylidene)-2-aminobenzothiazole concentration. The ANN model proposed with the Gaussian activation function was accurate for both testing and validation up to 99%. The RSM method used indicated that comparing time and concentration alone, inhibitor concentration was more significant than the immersion time in the corrosive solution. On the other hand, the effect of temperature and time were opposite to one another. While increasing time of immersion increased corrosion rate, temperature effect was the opposite.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".