Technical and economic assessment of bio-syngas production from biomass and its application in natural gas pipelines
Bibliographic record
Abstract
Abstract Biomass frequently constrains its widespread application due to its low economic efficiency. In this project, a new approach of biomass application was explored to reduce bio-syngas production cost. A model for the bio-syngas production from biomass gasification and bio-syngas purification (Selexol and water scrubbing) were studied. Based on the simulation results, we assessed the feasibility and viability of the project. Preliminary results showed that the bio-syngas production cost with Selexol (2.15 $/Nm3) is even higher than biomass used in power generation systems (equal to 1.58–2.21 $/Nm3). However, water scrubber was used as bio-syngas purification, the capital investment cost and bio-syngas production cost (1.34 $/Nm3) was significantly reduced. A sensitivity analysis was conducted on the bio-syngas price, which could be further reduced by various factors: biomass price, carbon taxes, and carbon credits. We assumed that bio-syngas was applied to natural gas pipeline, our analysis showed that the application of bio-syngas (2–10% content) to natural gas had little impact on natural gas properties: combustion value and price. The technology of water scrubber makes the applications of biomass-based fuels competitive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".