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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".