A techno-economic assessment of biomethane and bioethanol production from crude glycerol through integrated hydrothermal gasification, syngas fermentation and biomethanation
Bibliographic record
Abstract
Bioethanol is widely perceived as a clean fuel that can be used directly as automobile fuel or blended with petrol without engine modification. Similarly, biomethane can be used as an environmentally friendly substitute to natural gas for diverse applications such as transportation, heating, and electricity generation. Recently, there has been a growing interest in the cost-effective and sustainable production of both these biofuels. The present study reveals a conceptual design for biomethane and bioethanol production from crude glycerol obtained from the biodiesel industry. The techno-economic feasibility of three different scenarios was assessed. Scenario 1 is based on bioethanol production by coupling hydrothermal gasification and syngas fermentation. Scenario 2 consists of hydrothermal gasification, syngas fermentation, and CO2 capture unit. In scenario 3, biomethanation and electrolytic unit were added to scenario 2 to convert CO2 to biomethane. The energy efficiency of the scenarios examined ranges from 30.2% to 35.1%. Scenario 3 had the highest energy efficiency of 35.1%. Moreover, the minimum selling price of bioethanol declined in the following order: scenario 2 (USD $1.4 per liter) > scenario 1 (USD $1.32 per liter) > scenario 3 (USD $0.31 per liter). The discounted cash flow analysis results indicated that scenario 3 is the most profitable because of its non-discounted net present value of USD $34.9 million compared to the other case studies. Sensitivity analysis reveals that electricity and glycerol cost had the most effect on the minimum selling price of bioethanol.
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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) | 0.000 | 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) | 0.001 | 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".