A novel hybrid feedstock to liquids and electricity process: Process modeling and exergoeconomic life cycle optimization
Bibliographic record
Abstract
This article proposes a novel hybrid low‐rank coal (LRC)/biomass/natural gas process for producing liquid fuels and electricity. The hybrid process highlights coexistence of indirect and direct liquefaction technologies, cogasification of char and biomass, and corefinery of LRC syncrude and Fischer–Tropsch syncrude. A process simulation based on detailed chemical kinetics is present to illustrate its feasibility. In addition, we propose an exergoeconomic life cycle optimization framework that seeks to maximize the primary exergy saving ratio, primary total overnight cost saving ratio, life cycle waste emissions avoidance ratio, and primary levelized cost saving ratio by comparing the proposed hybrid process to its reference stand‐alone subsystems. From the results, we can determine four optimal designs which yield competitive breakeven oil prices ranging from $1.87/GGE to $2.13/GGE. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3739–3753, 2014
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".