Evidence map›Paper›PMID 42495383›Full record

ArticleACS omega2026

Modeling and Assessment of Ammonia Direct Reduction for Decarbonizing Iron Production.

Xuesong Lu, Dorcas Tuitoek, Binjian Nie, Aidong Yang

Abstract read
In one paragraph

Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Xuesong LuDepartment of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, U.K.
Dorcas TuitoekDepartment of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, U.K.
Binjian NieDepartment of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, U.K.ORCID https://orcid.org/0000-0003-2831-1196
Aidong YangDepartment of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, U.K.ORCID https://orcid.org/0000-0001-5974-247X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ammonia's favorable properties for transport and storage make it a promising alternative to hydrogen as a low-carbon reductant for green steel, particularly in regions lacking renewable energy. Despite several experimental investigations, the systematic evaluation of iron ore reduction by ammonia in the shaft furnace is still challenging, as there is no comprehensive mathematical model to simulate its chemical and physical performance. In this study, a one-dimensional plug-flow model was first developed to describe the ammonia reduction process in an industrial-scale shaft furnace with counter-current gas and solid flows. The kinetics of chemical conversions, including ammonia decomposition, direct reduction by ammonia and hydrogen, and iron nitridation, were incorporated into the model, along with mass and heat transfer, within the framework of a grain model at the pellet scale. Calibrated by TGA experimental data, the effects of process conditions and gas recycling were systematically investigated through numerical simulation. The results indicate that the process of ammonia-based reduction of iron ore is strongly endothermic, making intensive heat supply essential. A high inlet gas temperature above 900 °C is favorable to the operation of the shaft furnace with a high metallization rate and a low nitridation rate. By introducing a purge stream, excess nitrogen and water vapor can be removed from the system, enabling gas recycling and the reuse of ammonia and hydrogen from the off-gases; this achieves a metallization rate exceeding 0.90 with a nitridation rate below 0.10. By comparing it with hydrogen-based reduction of iron ore, this work provides a deep understanding of ammonia reduction and offers valuable guidance for industrial-scale reactor design and supply chain analysis.

Identifiers

PMID42495383
PMCPMC13393371

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.