Bibliographic record
Abstract
Abstract Renewable resources have been gaining increasing importance for energy generation in recent years. Lignocellulosic biomass, as an alternative to limited crude oil, can be utilized to produce these chemicals. The upstream process of comminution is a key element in the use of renewable raw materials. However, comminution is a highly energy consumptive process, making it necessary to assess the benefits of fine comminution in terms of reducing energy consumption by an adequate characterization of the products. The key factors to increased energy efficiency are, besides the mill type and the mill operation factors, the species of the renewable resource, in terms of water content and the mechanical properties which are the dominant factors in biomass size reduction. A better understanding of these interdependencies can help to improve the adjustment of particle size distribution and particle shape. As well as the total specific surface area and the energy demand for the comminution process, which impacts the overall efficiency of the supply chain process, disintegration, and the conversion. This work, therefore, focuses on the comminution of woody biomass in a cutting mill. It reports the effects of the influencing parameter of the biomass on the comminution process, the energy consumption, and the physical properties of the products regarding their particle size. Furthermore, selected experiments have been carried out on a swing hammer mill to compare the influence of the type of stressing.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".