Corrosion Inhibition of Piperazine for Potash Solution Mining and Processing Plants
Bibliographic record
Abstract
Potash mining and processing plants are prone to severe corrosion due to their operating conditions and potash brine constituents. Such corrosion causes a high expenditure on maintenance and repair of plant equipment and pipelines. To mitigate the corrosion, this work proposed the use of a corrosion inhibitor, namely piperazine (PZ) that is low toxic and commercially available. A series of electrochemical and weight loss corrosion experiments were carried out to examine corrosiveness of carbon steel (CS1018), J55 steel and stainless steel (SS316L) as well as to evaluate inhibition performance of PZ in saturated potash brines under the test conditions of 0 – 3880 rpm rotational speed, 25 – 85oC brine temperature and 1 – 5 bar pressure. Results show that CS1018 and J55 were much more corrosive than SS316L in saturated potash brine environments. Pitting tendency was observed on both CS1018 and J55. A higher rotational speed caused corrosion rates of CS1018 and J55 to increase. Increasing brine temperature caused CS1018 to be slightly less corrosive but caused J55 to become more corrosive. PZ was found to significantly reduce corrosion rate of CS1018 and J55 with the inhibition efficiency of 89.93 ± 0.87% and 87.20 ± 1.87% respectively, without pitting tendency at 85°C, 1 bar and 3880 rpm. Its high inhibition performance could be maintained over long durations (i.e. 21 and 28 days). PZ functioned as the mixed-type corrosion inhibitor retarding anodic and cathodic corrosion reactions. It protected the metal surface by undergoing monolayer chemisorption obeying the Langmuir adsorption isotherm on CS1018 and the Temkin adsorption isotherm on J55. The chemisorption was spontaneous and endothermic. PZ was likely to donate electrons to the metal surface during chemisorption. The presence of PZ did not alter purity and yield of potash products from a crystallization process.
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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 | 0.000 | 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) | 0.003 | 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".