Is the pipeline hydro‐transport of wheat straw and corn stover to a biorefinery realistic?
Bibliographic record
Abstract
Abstract Pipeline hydro‐transport is an alternative to truck delivery of agricultural residue (lignocellulosic) biomass. Pipeline hydro‐transport benefits from economies of scale, reduces total delivery costs, and enables bio‐based energy facilities to achieve higher capacities. In this study, the empirical correlation based on experimentally developed data for pipeline transport of agricultural residue‐water mixtures (slurry) was used to develop a data‐intensive techno‐economic model to estimate the cost of pipeline hydro‐transport of wheat straw and corn stover to a bioethanol refinery. The total cost of pipeline hydro‐transport was found to be lowest at 8.8% dry matter slurry solid mass content and 2.5 m s−1 slurry velocity. At this biomass slurry solid mass content and velocity, the pipeline hydro‐transport of biomass was found to be economically more viable than truck delivery at capacities of 0.45 M dry t yr−1 or more for a one‐way pipeline and 1.4 M dry t yr−1 or more for a two‐way pipeline (with the return of the carrier liquid). The ability to economically hydro‐transport agricultural residue biomass in pipes offers the opportunity to develop large‐scale bioethanol plants. © 2015 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".