Integration of energy and water consumption factors for biomass conversion pathways
Bibliographic record
Abstract
Abstract Water consumption is one of the critical factors for bioenergy production. In this study, six biofuel and six biopower production pathways are integrated with their water requirement to develop a new factor combining water consumption and energy efficiency for each pathway. This integrated factor is defined as water requirement for 1 MJ of net energy value (NEV) of biofuel or biopower. Agriculture‐residue‐based ethanol production pathways consume 51.2–63.6 liters of water per MJ of NEV. These pathways are both water and energy efficient. The biopower production pathways based on agriculture residues consume 27.2–50.6 liters of water per MJ of NEV. Although a switchgrass‐based ethanol production pathway is the most energy efficient, this pathway consumes an average of 130 liters of water per MJ of NEV due to poor water efficiency. Corn‐to‐ethanol and wheat‐to‐ethanol pathways are neither energy efficient nor water efficient and consume an average of 178 liters and 325 liters of water per MJ NEV, respectively. A rapeseed‐to‐biodiesel pathway is less energy intensive and lies between corn‐ and wheat‐grain‐based ethanol pathways and consumes an average of 211 liters of water per MJ of NEV. © 2011 Society of Chemical Industry and John Wiley & Sons, Ltd
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".