Energy Recovery from Cannabis Residues by Combustion with and Without Steam Explosion Pretreatment in Different Air Coefficients
Bibliographic record
Abstract
Alternative options have been studied to mitigate the negative impact of fossil fuel sources, mainly especially when it comes to alternative energy sources. In this work, cannabis residues have been considered as a potential biomass residues for energy recovery due to their energy content, and the increase in the cannabis market in Canada has created an opportunity niche for treating and valorizing these residues as energy. This study thus aims to investigate the potential of energy recovery from cannabis residue pellets via combustion and the impact of steam explosion on the pellets’ properties as well as combustion behavior. Two batches of pellets were produced namely with and without the steam explosion pretreatment. The properties of the pellets were then compared to those of the CANplus certification. Cannabis pellets were then combusted at 290 °C in a fixed-bed reactor using three different air coefficients (α) ranging from 1 to 1.3 (α = 1.0, α = 1.15, and α = 1.3). Flue gas quantification was performed using gas chromatography combined with a NOx detector. Results showed that the properties of this biomass is comparable to other sources of lignocellulosic biofuels. The steam explosion pretreatment enhanced pellet properties, including higher heating value (HHV), ash content, durability, and fines allowing the product to reach the CANplus requirements. The air coefficients influenced the emission levels, with an optimal value at α = 1.15, that indicated an improved combustion quality. However, steam explosion negatively affected combustion efficiency, resulting in incomplete combustion. Overall, cannabis residues show a strong potential for energy recovery and could offer a sustainable option for bioenergy applications.
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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 | 0.001 |
| 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 | 0.001 |
| 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".