Analysis of the Pelletability of Vegetable Crop Foliage Using a Commercial Flat Die Pellet Mill
Bibliographic record
Abstract
Agricultural residues serve as a vast yet underutilized biomass resource with significant potential for bioenergy and biomaterial applications. Converting these residues into densified biomass pellets enhances energy density, handling efficiency, and transportability, offering a sustainable alternative to conventional feedstocks. While extensive research has focused on woody biomass, studies on the pelletization of vegetable crop foliage remain limited. This study examines the pelletability of foliage from corn, soybean, tomato, eggplant, cucumber, and summer squash, assessing their physical properties, bulk durability, bulk density, and energy consumption during pelletization. Results demonstrated that variation in biomass composition significantly influences pellet quality, with lignin content improving durability and ash content affecting moisture uptake and combustion properties. Cucumber had the highest pellet density (691.2 kg/m3) and durability (97.9%), making it suitable for long-term storage and transport. Sawdust exhibited the lowest moisture absorption (16–18% db), which is attributed to its highest lignin content. Pelletization energy requirements varied significantly, with cucumber (21.8 kWh/t) and summer squash (18.7 kWh/t) requiring the lowest energy input, whereas soybean (49.6 kWh/t) and sawdust (47.3 kWh/t) exhibited the highest energy demands due to greater resistance to densification. A predictive model was developed to correlate single pellet density and durability with bulk pellet properties—yielding high predictive accuracy, with R2 = 0.936 for bulk density (BDe) and R2 = 0.861 for bulk durability (BDu)—thereby facilitating process optimization for large-scale pellet production. This study demonstrated that foliage residues from greenhouse crops, such as cucumber and summer squash, can be effectively pelletized with low energy input and high physical integrity. These outcomes suggest that such underutilized agricultural residues hold promise as a densified intermediate feedstock, supporting future applications in bioenergy systems and advancing circular resource use in controlled-environment agriculture.
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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 | 0.001 | 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) | 0.001 | 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".