Sustainability of gasification-based cogeneration with agri-food residues and heat recovery technologies: Techno-economic and life cycle analyses
Bibliographic record
Abstract
The valorization of agri-food waste through biomass gasification integrated with heat recovery technologies is a promising option for sustainable renewable energy. Comprehensive evaluation of such energy systems requires both life cycle assessments (LCAs) and techno-economic analyses (TEAs). This study investigates the potential of three agri-food residues—almond hulls, exhausted olive pomace (EOP), and date palm fronds (DP)—as biomass fuels for gasification from a sustainability standpoint. Two configurations for combined production of electricity and heat in the form of hot water are evaluated: one using a cleaning and cooling unit coupled to an internal combustion engine (ICE), and another using an externally fired gas turbine combined with an organic Rankine cycle bottoming unit (EFGT_ORC). Results reveal that the EFGT_ORC cogeneration system consistently requires lower biomass input than the ICE cogeneration unit, with DP fronds demanding the highest biomass input in the ICE configuration at 36 g/s, followed by almond hulls at 32 g/s, and EOP at 28 g/s. ICE cogeneration contributes to higher climate change environmental impact, with emissions around 2.95 × 10 −2 kg CO 2 eq. for all fuels. In terms of human health, DP fronds have a greater impact in EFGT_ORC cogeneration than in ICE. Almond hulls exhibit slightly better economic performance compared to EOP and DP fronds. However, regardless of the biomass fuel, biomass and electricity price variations significantly affect system sustainability. The ICE system offers faster returns on investment, but is more vulnerable to increasing biomass prices, whereas the EFGT-ORC system demonstrates more resilience to biomass price fluctuations. • Assessment of almond hulls, exhausted olive pomace, and date palm fronds as gasification fuels. • Gasification-based ICE vs. EFGT-ORC cogeneration: EFGT_ORC requires less biomass input. • LCAs show EFGT_ORC cogeneration has higher environmental impacts than ICE, especially for DP fuel. • ICE cogeneration favored in technoeconomic analysis, with almond hulls performing best economically. • Biomass, electricity, and heat prices significantly affect cogeneration financial metrics, especially for EOP.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".