Enhancing properties of iron and manganese ores as oxygen carriers for chemical looping processes by dry impregnation
Bibliographic record
Abstract
The use of naturally occurring ores as oxygen carriers in CLC processes is attractive because of their relative abundance and low cost. Unfortunately, they typically exhibit lower reactivity and lack the mechanical robustness required, when compared to synthetically produced carriers. Impregnation is a suitable method for enhancing both the reactivity and durability of natural ores when used as oxygen carriers for CLC systems. This investigation uses impregnation to improve the chemical and mechanical properties of a Brazilian manganese ore and a Canadian iron ore. The manganese ore was impregnated with Fe 2 O 3 and the iron ore was impregnated with Mn 2 O 3 with the goal of forming a combined Fe/Mn oxygen carrier. The impregnated ore’s physical characteristics were assessed by SEM, BET and XRD analysis. Measurements of the attrition resistance and crushing strength were used to investigate the mechanical robustness of the oxygen carriers. The impregnated ore’s mechanical and physical properties were clearly enhanced by the impregnation method, with boosts in crushing strength of 11–26% and attrition resistance of 37–31% for the impregnated iron and manganese ores, respectively. Both the unmodified and impregnated ore’s reactivity, for the conversion of gaseous fuel (CH 4 and syngas) and gaseous oxygen release (CLOU potential) were investigated using a bench-scale quartz fluidised-bed reactor. The impregnated iron ore exhibited a greater degree of syngas conversion compared to the other samples examined. Iron ore based oxygen carrier’s syngas conversion increases with the number of oxidation and reduction cycles performed. The impregnated iron ore exhibited gaseous oxygen release over extended periods in an inert atmosphere and remained at a constant 0.2% O 2 concentration by volume at the end of this inert period. This oxygen release would help ensure the efficient use of solid fuels. The impregnated iron ore’s reactivity for CH 4 conversion was similar to the reactivity of its unmodified counterpart. The unmodified manganese ore converted CH 4 to the greatest extent of all the samples tested here, while the impregnated manganese ore exhibited a decrease in reactivity with respect to syngas and CH 4 conversion.
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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) | 0.001 | 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".