Optimizing production conditions of innovative bio-pellets developed from flax straw
Bibliographic record
Abstract
The increasing global demand for sustainable energy has prompted the utilization of agricultural residues as viable alternatives to traditional fossil fuels . This study specifically focuses on the utilization of flax straw, a substantial by-product in Canadian agriculture , to produce innovative biofuels through the process of pelletization . To enhance the quality of the pellets, lignin extracted from flax straw was introduced as a novel and natural pellet binder. A comprehensive investigation was conducted to analyze effect of various parameters, including flax straw particle size, binder (lignin) content, moisture levels, and pelletization conditions on quality of produced pellets. It was observed that at a compressive force of 4000 N and a die temperature of 95 °C, pellets made from larger flax straw particles (300–355 µm) exhibited a density of 1016 kg/m³ and a mechanical durability of 26.4 %. Subsequently, by reducing the particle size to 75–106 µm, a marked enhancement in both density (1072 kg/m³) and durability (86.1 %, a 2.3-fold increase) was achieved. The incorporation of 10 % lignin into medium-sized flax straw particles (150–212 µm) increased pellet durability by 18.6 % from 73.5 % to 87.2 %. Introducing an optimal moisture content of 14 % to the flax straw resulted in a notable increase in both density (from 1036 to 1154 kg/m³) and durability (from 73.5 % to 90.6 %, a 23.3 % increase). Compressive force and die temperature positively affected pellet quality. Elevating the temperature from 75 to 115 °C in pellets made from 150 to 212 µm flax straw, modified with 10 % lignin and 14 % moisture content, led to a significant improvement in both density (1179 kg/m³) and durability (96.6 %, a 11.9 % increase). Physiochemical characteristics of flax straw, lignin, and the resulting pellets were explored using various techniques such as Fourier transform infrared, thermogravimetric analysis , and differential scanning calorimetry . These comprehensive investigations offer valuable insights for optimizing pellet materials and pelletization conditions and promoting the sustainable utilization of agricultural residues for bioenergy production.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".