Energy Recovery Based on Gasification of Residues for Decarbonisation of the Agriculture Sector
Bibliographic record
Abstract
Global energy demand is rapidly increasing, with about 80% still met by fossil fuels, contributing to resource depletion and climate change.As a renewable alternative, agricultural biomass waste, specifically olive kernels (OK) and olive tree cuttings (OTC), appears to be promising for clean energy production.This study proposes a numerical model in Aspen Plus to simulate combined heat and power (CHP) generation via air gasification of OK and OTC.The gasification model is calibrated and validated by using experimental data available in the literature related to five different operating conditions, obtaining an average deviation of predicted syngas composition from experimental outcomes in the range of 1.23% to 13.26%.The developed model is then used to identify optimal gasification conditions, finding a temperature of 950℃ for OK and 900℃ for OTC at an equivalence ratio of at least 0.2.If globally available, OK and OTC were utilized this way in 2024, they could produce 20,375 MWh of electricity and 38,829 MWh of thermal energy, potentially cutting CO₂ emissions by 16 Mt/yr compared to the use of conventional energy sources.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".