Constitutive Modeling for Prediction of Optimal Process Parameters in Tribo-corrosion Inhibition of Steel Pipes Carrying Fracking Fluid
Bibliographic record
Abstract
Abstract The present work examines the effect of three propriety corrosion inhibitors (Dynarate, DWP and CalGuard) on the tribo-corrosion behavior of AISI 4715 steels used to carry fracking liquid from their storage pool to the geological formation. The effect of these three additives on the wear and corrosion behavior of AISI 4715 steel was investigated using a reciprocating tribometer integrated with an open circuit potential electrochemical apparatus. Response Surface Methodology (RSM) was applied to statistically model the effects of various concentrations of Dynarate, DWP andCalGuard on the average coefficient of friction (COF) between the steel and a sapphire counter surface, as well as the total wear loss of the steel due to the combined action of wear-corrosion and inhibition efficiency during sliding. The mathematical regression models were derived from the analysis of variance (ANOVA) techniques. Optimization of the individual variables to minimize the friction and wear responses was estimated using RSM. A full quadratic regression model was established and authenticated before the variables were optimized for different responses. The overall results revealed that Dynarate significantly decreased the COF (0.147) and wear rate (0.3 mm/year) with an inhibitionefficiency of 480% at a concentration of 1%. To investigate the effectiveness of the regression model at predictingthe wear rate, the samples were characterized using 3D optical prolometer and scanning probe microscopy to describe the effect of various additive on the surface morphology of steel. The surface topography measurementsindicated the worn regions for the samples where the Dynarate additive was used was smoother compared with those having the DWP and CalGuard additives. This observation was attributed to the formation of protective lm which limits the transfer of aggressive ions transfer to the steel surface and reduced the total wear loss due to wear and corrosion.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".