Influences of alkali fluxes on direct reduction of chromite for ferrochrome production
Bibliographic record
Abstract
Conventional smelting processes are energyintensive (Riekkola-Vanhanen, 1999) with the energy requirements greater than 4 MWh/t (Naiker and Riley, 2006;Beukes, van Zyl, and Neizel, 2015), and the greenhouse gas emissions can be significant, exceeding 10.5 t CO 2 per ton Cr in ferrochrome produced (International Chromium Development Association, 2016).These include emissions occurring on-site (smelter), emissions due to electricity production, and emissions due to upstream processes.Overall energy requirements are influenced by the degree of prereduction and smelter charge temperature.Significant reduction in electricity consumption can be realized if the charge is prereduced before feeding to the submerged arc furnace (Niayesh and Fletcher, 1986).This consideration has led to the development of several prereduction technologies, with the most important example being the Premus process.In comparison to the conventional smelting processes, this prereduction process lowers the overall energy consumption and greenhouse gas emissions by about one-third (Naiker, 2007).Prereduction of chromite with the use of various fluxes or additives has been the topic of many studies over at least three decades.The additives tested since 1986 include borates, NaCl, NaF, and CaF 2 (Katayama, Tokuda, and Ohtani, 1986), CaF 2 and NaF (Dawson and Edwards, 1986), K 2 CO 3 , CaO, SiO 2 , Al 2 O 3 , and MgO (van Deventer, 1988), granite and CaF 2 (Nunnington and Barcza, 1989), SiO 2 (Weber and Eric, 1992; Lekatou and Walker, 1997), Portland cement, lime, and SiO 2 (Takano et al., 2007), CaO and SiO 2 (McCullough et al., 2010), CaCO 3 (Neizel et al., 2013), and MgO, CaO, SiO 2 and Al 2 O 3 (Wang, Wang, and Chou, 2015).Since the filing of the patent application on the use of several alkalis as the accelerants (Winter, 2015; Barnes, Muinonen, and Lavigne, 2015), we have been performing systematic studies to improve our understanding of the roles of various fluxes.Our studies involved using NaCl, NaOH, Na 2 CO 3 , CaCl 2 , Ni, and a metallurgical waste product as the fluxes in accelerating the reduction of chromite and developing a fundamental understanding of the kinetics and mechanisms of reduction and metallization (Sokhanvaran and Paktunc, 2017, 2018;Sokhanvaran, Paktunc, and Barnes, 2018;Yu and Paktunc, 2017; Yu and Paktunc, 2018a, 2018b, 2018c).These studies formed parts of the broader research into improving and optimizing the conventional smelting processes as well as developing new reduction technologies to reduce energy demands and greenhouse gas emissions, and evaluating the Influences of alkali fluxes on direct reduction of chromite for ferrochrome production by
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".