Hydrogen Production using Catalytic Supercritical Water Gasification of Lignocellulosic Biomass
Bibliographic record
Abstract
Catalytic supercritical water gasification (SCWG) is a promising technology for hydrogen and methane production from wet organic feedstocks at relatively low temperatures (e.g. <500 oC). However, in order to make this process technically and economically viable, solid catalyst with enhanced activity and improved hydrogen selectivity should be developed. In this study, different aspects of catalytic SCWG have been investigated. The performance of several supported-nickel catalysts were examined to identify catalysts that lead to high carbon conversion and high hydrogen yields under near-critical conditions (i.e. near 374 oC). Moreover, for the first time, the effects of several parameters which dominated the activity of the supported nickel catalysts have been systematically investigated. Among the several different catalyst supports evaluated at 5% nickel loading, α-Al2O3, carbon nanotube (CNT), and MgO supports resulted in highest carbon conversions, while SiO2, Y2O3, hydrotalcite, yttria-stabilized zirconia (YSZ), and TiO2 showed modest activities. Comparing the XRD patterns for the support materials before and after the exposure to supercritical water, α-Al2O3, YSZ, and TiO2 were found to be hydrothermally stable among the metal oxide supports. Using the same amount of nickel on α-Al2O3, the methane yield decreased by increasing the nickel to support ratio whereas the carbon conversion was only slightly affected. At a given nickel to support ratio, a threefold increase in methane yield was observed by increasing the temperature from 350 to 410 oC. The catalytic activity also increased by the addition small quantity of potassium. The activity of Ni/γ-Al2O3 catalyst varied based on the affinity of the catalyst to form nickel aluminate spinel. This is also the first report on the role of oxidative pretreatment of the carbon nanotubes by nitric acid on the performance of these catalysts for the supercritical water gasification process. Using different lignocellulosic feeds, it was found that the gasification of glucose, fructose, cellulose, xylan and pulp resulted in comparable gas yields (± 10%) after 60 min, whereas alkali lignin was substantially harder to gasify. Interestingly, gasification yield of bark, which had a high lignin content, was comparable to those of cellulose. In summary, the Ni/α-Al2O3 catalyst had a higher hydrogen selectivity and comparable catalytic activity to the best commercially available catalysts for SCWG of carbohydrates.
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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".