Techno‐economic assessment of biogas to liquid fuels conversion technology via Fischer‐Tropsch synthesis
Bibliographic record
Abstract
Abstract Biogas derived from anaerobic digestion of organic wastes, and lignocellulosic biomass can be used to produce drop‐in diesel fuel via Fischer‐Tropsch (FT) synthesis. In this study, we developed a process‐based simulation model for the biogas to liquid fuel (BgTL) plant to conduct mass and energy balances and to evaluate techno‐economic assessment of producing drop‐in FT fuels. The BgTL plant operations consisted of biogas cleaning, biomethane reforming, FT synthesis of syngas and hydrocracking and final distillation to produce drop‐in liquid fuels. The unconverted syngas and syncrude were utilized to generate steam and electricity to meet internal plant demand, while the excess power was sold to the grid. The base case BgTL plant (2 000 Nm3/h) produced about 4.6 million gallons per annum of total FT fuels which consisted of 62% diesel, 32% gasoline, 6% LPG with an overall biogas conversion of 54%. A discounted cash flow rate of return (DCFOROR) approach for the Nth plant was used to estimate the capital and operating costs with the minimum selling price for the FT drop‐in fuels of about $5.67/gal ($5.29/GGE). The increase in plant feed capacity to 20 000 Nm3/h decreased the minimum selling price to $2.06/gal ($1.92/GGE). The sensitivity analysis conducted on the base case plant demonstrated that the internal rate of return (IRR), FT conversion rate, plant operating hours, and biogas cost were the most sensitivity parameters to the minimum selling price. Overall, the BgTL technology is deemed to be economically feasible to meet US biofuels demand. © 2017 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.000 |
| 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".