Uniquely designed three biomass-based integrated sustainable energy systems for comparative evaluation
Bibliographic record
Abstract
In this work, three different configurations of an integrated thermal system for hydrogen production utilizing gasification, alkaline electrolysis, and photo-alkaline electrolysis are conceptually developed and thermodynamically analyzed and assessed. Different gasification feedstock options considered include white oak wood, Douglas-fir wood, madrone wood, and pine needles. The Aspen Plus software package is used to conduct corresponding simulations and facilitate the system analysis and performance related calculations. For photo-alkaline electrolysis calculations, the AM1.5G spectrum and the bandgap of copper oxide are also considered. Subsequently, a direct approach is presented for the calculation of the fraction of the solar spectrum and incident irradiance absorbed. The highest hydrogen production is attributed to pine needles at 285 kg/h for a 4500 kg/h biomass feed due to its favorable composition, at efficiencies of 38.07 %, 38.53 %, and a peak of 38.55 % at 17:00 in a diurnal cycle, for gasification, gasification-alkaline electrolysis, and gasification-photo-alkaline-electrolysis system configurations, respectively. • Three systems for hydrogen generation are developed using gasification and electrolysis. • Pine needles achieve the highest hydrogen generation and system efficiency. • Gasification-photoelectrolysis system achieves a peak efficiency of 38.55 %.
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 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".