Predictive modelling of hydrogen production from agricultural and forestry residues through a thermo-catalytic reforming process
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".