Investigation of pitch modification and its effect on anode properties: Effect of pitch type
Bibliographic record
Abstract
Abstract Aluminium is produced by electrolysis using carbon anodes. These anodes are manufactured with dry aggregate (mainly calcined petroleum coke, butts, and rejected green and baked anodes) and coal tar pitch, which acts as a binder. Utilization of good quality anodes decreases the consumption of carbon and energy, hence the process cost as well as the emission of greenhouse gases (GHG). The interactions between coke and pitch play an important role in determining the anode quality. If they are compatible, pitch can penetrate into the pores of the coke particles as well as into the voids between the particles, resulting in denser anodes. One way to improve these interactions is to modify the chemical composition of pitch using an additive. The objective of this study is to investigate the effect of the pitch type on the effectiveness of pitch modification in improving the anode properties. Two types of pitch with different quinoline insoluble (QI) contents were used: one with high QI (HQI pitch) and the other with low QI (LQI pitch). They were modified using the same additive. The interactions between the pitches and the coke were studied by measuring the wettability of coke by the pitches. The pitch chemical composition was studied using FTIR and XPS. Then, anodes were produced and characterized. Their apparent density, electrical resistivity, air and CO2 reactivities, and permeability were compared. The results showed that the properties of anodes produced using modified HQI pitch were improved. Modifying LQI pitch did not significantly improve the anode properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".