Factors affecting the utilization of lignocellulosic biomass; compaction, handling and storage, and monetary value – a review.
Bibliographic record
Abstract
In recent history, fears of climate change and a possible looming energy crisis due to depleting fossil fuel reserves have stimulated research into the use of lignocellulosic biomass as an alternative energy source. Political and social will has promoted the use of biomass for both heat and electric energy generation. Legislation, particularly European, has been a driving force in promoting the use of biomass. Tax incentives, feed in tariffs (FIT), quota systems, and subsidies have assisted in making the use of biomass economically feasible. A simplified mathematical model to determine the market value of biomass is examined and some of its limitations are discussed. The difference in higher heating value (HHV) and lower heating value (LHV), criteria for rating biomass, is demonstrated using mathematical relationships. Significant differences in composition, quality, and energy values of densified biomass products depend on factors including chemical composition, physical characteristics, the use of binders, and storage and handling conditions. Improper storage conditions increase the risk of life and property loss. Ash contributes to premature equipment failure and lowers the biomass energy value. Biomass sources with high ash content may fail to meet standards for compacted biomass.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".