Energy Recovery from Biowaste and Biomass via Gasification: A Modelling Approach
Bibliographic record
Abstract
The transition toward a circular bioeconomy requires efficient conversion of biogenic wastes and biomass into renewable fuels. This study explores the gasification potential of wastewater sludge (WWS) and food waste (FW), representing high moisture-content biowastes, compared with softwood (SW), a lignocellulosic biomass reference. An Aspen Plus equilibrium model incorporating the drying stage was developed to evaluate the performance of air and steam gasification. The effects of temperature (400–1200 °C), equivalence ratio (ER = 0.1–1), and steam-to-biomass ratio (S/B = 0.1–1) on gas composition and energy efficiency (EE) were examined. Increasing temperature enhanced H2 and CO generation but reduced CH4, resulting in a maximum EE at intermediate temperatures, after which it declined due to the lower heating value of the gases. Although EE followed the order SW > FW > WWS, both biowastes maintained robust efficiencies (60–80%) despite high drying energy requirements. Steam gasification increased H2 content up to 53% (WWS), 54% (FW), and 51% (SW) near S/B = 0.5–0.6, while air gasification achieved 23–27% H2 and 70–80% EE at ER ≈ 0.1–0.2. The results confirm that wet bio-wastes such as WWS and FW can achieve performance comparable to lignocellulosic biomass, highlighting their suitability as sustainable feedstocks for waste-to-syngas conversion and supporting bioenergy integration into waste management systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".