Techno-Economic Analysis of a Biomass-Gas-and-Nuclear-to-Liquid Polygeneration Plant
Bibliographic record
Abstract
Due to the advancement of global warming internationally, increasing emphasis is being placed on the environmental accountability of everyone from countries to processes. This study presents novel research on the environmental impacts and economic trade-offs for a processes co-producing electricity, methanol, dimethyl ether (DME) and Fischer Tropsch (FT) fuels from different feedstock ratios of biomass, natural gas, and nuclear hydrogen generated through a CuCl cycle are analyzed for operation in Canada to produce transportation fuels. This study also considers the use of carbon capture and sequestration (CCS), the location of the plant in either Ontario and Alberta, and the input ratio of the feedstocks. This combination of carbonless heat and a “carbon neutral” biomass feedstock would contribute to the net reduction of greenhouse gas (GHG) emissions. In Part I of this work, the model for this BGNTL process was developed. This work expands on the model and evaluates the economics and environmental impacts this plant would have in both Ontario and Alberta based on their local costs, resource availability, and current electricity grid contributions. The analysis investigates the effectiveness of the emission reduction of the products and processes when compared to their cost. It is shown that an increase in the ratio of biomass to natural gas in feedstock, the use of a solid oxide fuel cell (SOFC), and the production of additional electricity while reducing the emissions of the process, increases the cost of CO2e avoided. The results show that the BGNTL concept can be an economically attractive way of reducing net transportation sector GHG emissions in both Ontario and Alberta in meaningful quantities. Optimal cases for both biofuel and FT fuel production contain a single output fuel production process, produce fuels over electricity where possible, and use a gas turbine (GT) for the electricity production that occurs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".