Indirect estimate of in loco moisture via normative mineralogy calculation with correction of seasonal effect on itabirite (banded iron formation) of the Pico complex, Quadrilátero Ferrífero, Minas Gerais, Brazil
Bibliographic record
Abstract
Abstract Moisture is a critical variable in iron-ore processing, handling and transportation. During beneficiation, excessive moisture may lead to screen and chute clogging. In transportation, moisture values above transportable moisture limit may cause cargo instabilities, especially in regard to vessels. Moisture is a non-stationary variable that depends on spatial and time distributions. Therefore, classical estimate methods such as ordinary kriging are not appropriate to calculate moisture values. Here, we present an extension of the Normative Mineralogy Calculation to indirectly estimate moisture, considering seasonal influence. This study in based on three iron-ore mines, Galinheiro, Pico and Sapecado. They are located in the Quadrilátero Ferrífero of Minas Gerais, Brazil, a world-class iron-ore district. The method proposed herein provides useful information that can be applied elsewhere. Our results indicate that compact ores show low moisture values with little seasonal influence, while soft ores and canga (iron-rich duricrust) are strongly influenced seasonally due to higher porosity and greater capacity of retaining water in the crystal structure of minerals, such as goethite. Moisture variations may exceed 2% along the year. Such variations are enough to preclude the beneficiation of certain iron ores during the rainy season. For this reason, moisture has been regarded as an essential variable in short-term mining. Article Highlights Moisture is a critical variable in iron-ore processing, handling and transportation. Moisture depends on spatial and time distributions; hence classical methods are not appropriate to quantitatively estimate it. This study proposes an indirectly estimate of moisture considering seasonal influence. Compact iron ores are little influenced seasonally, while soft iron ores and canga (duricrust) are strongly affected by the rainy season due to their higher porosity and greater capacity of retaining water. The seasonal effect on moisture is an essential variable that must be consider to better effectiveness of iron-ore mining sequencing and beneficiation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".