Some Natural Products as Green Corrosion Inhibitors
Bibliographic record
Abstract
Abstract Pipelines are the safest and most economical means of transporting oil and gas in offshore and onshore production facilities. Corrosion inhibitors continue to play a significant role in protecting the pipelines from internal corrosion. A number of corrosion inhibitors have been developed with low environmental impact without compromising on their inhibitor efficiency. Recently geographical location specific-regulations for several regions have been implemented. The most prominent of these are the environmental regulations for the North Sea (UK, Norway, Denmark, The Netherlands), US Gulf Coast, Eastern Canada etc. This paper describes the investigations using aqueous extracts of leaves of medicinal plants A, B and C; and root of plant D as environmentally friendly corrosion inhibitors of mild steel in synthetic ocean water by weight loss method. These natural products were designed for application in Indian oil and gas industry and other environmentally sensitive platforms; exploiting their low toxicity-as medicinal plants and ease of biodegradation-as water soluble extracts. Aqueous extracts of plant materials A, B, C and D were investigated for Bioaccumulation. All the extracts exhibited low bioaccumulation and good corrosion protection. These preliminary investigations conducted to select green inhibitors revealed that aqueous extracts of C and D exhibited better corrosion performance than A and B. Weight loss studies results at 50ppm and 100ppm in the absence and presence of Hydrogen sulfide further support this fact. This paper also presents a brief overview of the principal criteria the inhibitor developers must follow to determine whether a given corrosion inhibitor is environmentally acceptable in a given region. It also presents the criteria for green corrosion inhibitors for Indian oil and gas industry pertinent to the characteristic marine biodiversity, local priority issues like major sources of pollution, demography, the nature of regional and international environmental agreements etc.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".