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Predictive modelling of hydrogen production from agricultural and forestry residues through a thermo-catalytic reforming process

2025· article· en· W7081991754 on OpenAlexafffund

Bibliographic record

VenueBiomass and Bioenergy · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of Alberta
FundersMinistry of Economic Development and Trade, Government of AlbertaCanada Research ChairsUniversity of Alberta
KeywordsPelletsSyngasBiomass (ecology)Raw materialStrawBioenergyMethaneCarbon dioxideHydrogenRenewable energy

Abstract

fetched live from OpenAlex

Hydrogen produced from renewable sources is crucial for decarbonizing hard-to-abate sectors and achieving net-zero targets. This study examines hydrogen production through the novel thermo-catalytic reforming (TCR) process using agricultural and forestry residues. The research aims to develop and optimize regression models that integrate feedstock properties (ash, hydrogen-to-carbon molar ratio, and lignin) and process parameters (reactor and reformer temperatures) to predict yields of hydrogen (H 2 ), syngas, methane (CH 4 ) and carbon dioxide (CO 2 ). Three biomass feedstocks – softwood pellets (SWPs), hardwood pellets (HWPs), and wheat straw pellets (WSPs) – were analyzed at reactor temperatures of 400–550 °C and reformer temperatures of 500–700 °C. Predictive models for H 2 (R 2 = 0.9642, RMSE = 1.0639) and syngas (R 2 = 0.9894, RMSE = 0.0140) yields show strong agreement and accuracy between the predicted and experimental values. In contrast, the models for CH 4 and CO 2 yields show higher variability in the predictions. Reformer temperature was the most significant parameter influencing the yields of H 2 and syngas. The optimal H 2 yields predicted for the model were obtained for HWPs at 550/700 °C (26.67 g H 2 /kg dry biomass), followed by SWPs at 550/700 °C (24.11 g H 2 /kg dry biomass) and WSPs at 550/685.2 °C (18.78 g H 2 /kg dry biomass). The volumetric syngas yields were highest for HWPs at 550/700 °C (0.831 Nm 3 /kg dry biomass), followed by SWPs (0.777 Nm 3 /kg dry biomass) and WSPs (0.634 Nm 3 /kg dry biomass). This study demonstrates that regression modelling accurately predicts H 2 and syngas yields, which would help to expand the applicability of TCR technology for large-scale hydrogen production, contributing to the decarbonization of the energy sector. • Study explores production of H 2 -rich syngas via thermo-catalytic reforming (TCR). • Reformer temperature has a higher impact on hydrogen and syngas yields in the TCR. • Predicted H 2 yield was maximum (26.67 g H 2 /kg) at 550/700 °C for hardwood pellets. • Predicted V syngas yield reached maximum (0.831 Nm 3 /kg) at 550/700 °C for HWPs. • Model predictions have good agreement with experimental H 2 and V syngas yields.

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 score0.073
Threshold uncertainty score0.281

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 head0.017
GPT teacher head0.218
Teacher spread0.201 · 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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