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Reducing CO <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si69.svg" display="inline" id="d1e4696"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> emissions, energy consumption, and decarbonization costs in manganese production by integrating fuel-assisted solid oxide electrolysis cells in two-stage oxide reduction

2024· article· en· W4402831115 on OpenAlexfundno aff
Anders S. Nielsen, Gonzalo del Alamo, Trygve Lindahl Schanche, Odne Stokke Burheim

Bibliographic record

VenueApplied Energy · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNorges Forskningsråd
KeywordsMathematicsAlgorithm

Abstract

fetched live from OpenAlex

Manganese is one of the most consumed metals due to its use in iron and steel making, which generates between 7%–9% of all CO 2 emissions produced globally per annum. For the first time, this work explores the potential of a novel process concept to reduce CO 2 emissions, energy consumption, and decarbonization costs in manganese production, which involves integrating fuel-assisted solid oxide electrolysis cells (FASOECs) in a two-stage scheme for the reduction of raw manganese ores. In this scheme, higher oxides that are present in raw manganese ores (MnO 2 , Mn 2 O 3 , Mn 3 O 4 ) are pre-reduced using CO, yielding manganese monoxide (MnO) that is further reduced to Mn in a submerged arc furnace (SAF) using coke and electricity. In the proposed FASOEC integration concept, off-gas from the manganese ores after pre-reduction (a mixture of H 2 , CO, and CO 2 ) is supplied to the FASOECs’ anode as fuel, in order to produce high-purity H 2 in the cathode that is directed to the first stage of manganese reduction. H 2 has enhanced reaction kinetics for reducing higher manganese oxides in comparison to CO; therefore, integrating FASOECs can improve manganese oxide conversion rates during pre-reduction, resulting in lower consumption rates of coke and energy in the SAF. The off-gas supplied to the FASOECs’ anode also lowers the amount of energy required for H 2 production in the cathode (can also generate electricity, simultaneously) and produces anode exhaust gas containing only CO 2 and steam. Since steam can be easily condensed from this stream, the integration of FASOECs also enables efficient decarbonization of the manganese production process, thus eliminating the need for a designated CO 2 capture system. The techno-economic analysis performed herein demonstrates that directing the full supply of off-gas produced by the SAF to the FASOECs’ anode at 800 °C reduces the overall energy consumption of manganese production by up to 18% in comparison to conventional processes. This results in decarbonization cost reductions by as much as 3%–15% and a corresponding decarbonization price range of $4-32 per ton of manganese product for plant capacities of 50 and 200 kt, respectively. • Integrates fuel-assisted solid oxide electrolysis cells (FASOECs) in Mn production. • Novel process concept reduces coke and electricity consumption dramatically. • FASOECs generate high-purity CO 2 to eliminate costly CO 2 capture systems. • Integration of FASOECs in Mn production reduces decarbonization costs by 15%. • FASOEC integration in Mn production reduces energy consumption by up to 18%.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.011
GPT teacher head0.254
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2024
Admission routes1
Has abstractyes

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