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Hydrogen production via sorbent-enhanced gasification of hydrothermally carbonized wood residues

2025· article· en· W4413154090 on OpenAlexafffund
Sanusi B. Akintunde, Norbert Onen Rubangakene, Shakirudeen A. Salaudeen

Bibliographic record

VenueBiomass and Bioenergy · 2025
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Natural Resources and Forestry
KeywordsSorbentCarbonizationHydrogen productionMaterials scienceChemical engineeringWaste managementHydrogenPulp and paper industryChemistryOrganic chemistryAdsorptionEngineering

Abstract

fetched live from OpenAlex

Biohydrogen and syngas are promising biofuels to mitigate the environmental challenges of fossil fuel emissions. Biofuels harnessed from biomass resources like spruce-pine-fir (SPF) residues, can sustainably contribute to achieving the global net-zero emission goals. This study investigates the hydrothermal carbonization (HTC) of SPF residue and models sorbent-enhanced steam gasification of SPF and its hydrochars (SPFHC_180, SPFHC_220, SPFHC_260) using calcium oxide as the CO 2 sorbent. The gasification temperature, pressure, steam-to-biomass ratio (SBR) and CaO-to-biomass ratio (CBR) were investigated to maximize syngas' hydrogen and energy content. Hydrochars' energy improved by 52 % (SPFHC_260), 22 % (SPFHC_220), and 9 % (SPFHC_180), with HTC temperature respectively, while reducing the ash content. CaO improved the H 2 content of the syngas up to 74.24 % (SPFHC_260), 82.98 % (SPFHC_220), 89.59 % (SPFHC_180) and 91.34 % (raw SPF gasification) while reducing the CO 2 by 98 % (all feeds). SBR increase raised the CO 2 concentration to 17.11 vol% (SPFHC_260), 17.58 vol% (SPFHC_220), 17.19 vol% (SPFHC_180), and 16.20 vol% (raw SPF). Higher gasification temperature maximized CO to 41.70 vol% (SPFHC_260), 35.41 vol% (SPFHC_220), 32.52 vol% (SPFHC_180), and 31.10 vol% (raw SPF). The rise in gasification pressure improved the syngas’ lower heating value (LHV) to a peak of 20.69 MJ/Nm 3 (SPFHC_260), 19.20 MJ/Nm 3 (SPFHC_220), 18.20 MJ/Nm 3 (SPFHC_180), and 17.95 MJ/Nm 3 (raw SPF). Combining HTC with sorbent-enhanced gasification of biomass has a scalable potential for energy-rich and hydrogen-rich syngas production. • HTC improved hydrochar's energy and carbon content by 52 % and 58 % respectively. • CaO reduced CO 2 content by 98 % in syngas via in-situ adsorption. • CO 2 capture boosted syngas' H 2 content to 74.24 % (hydrochar) and 91.34 % (raw SPF). • Steam-to-biomass ratio significantly influenced CO 2 and CO in syngas composition. • Combined HTC and sorbent-enhanced gasification offer efficient hydrogen production.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.010
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

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

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 head0.005
GPT teacher head0.195
Teacher spread0.190 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2025
Admission routes2
Has abstractyes

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