Hydrothermal liquefaction of biomass for the production of diluents for bitumen transport
Bibliographic record
Abstract
Abstract This study explores the hydrothermal liquefaction (HTL) of wood chips to biocrude followed by upgrading to diluents, which are used to transport bitumen through pipelines. In this study, we considered a 2000 dry t day−1 plant capacity with two scenarios. The first scenario uses hydrogen for upgrading from the on‐site hydrogen production plant (i.e., the hydrogen production scenario) and the other relies on procuring hydrogen from an external source (i.e., the hydrogen purchase scenario). We developed a data‐intensive process model for HTL and used it to estimate plant capital costs. Project investment costs for the hydrogen production and hydrogen purchase scenarios are 559.67 and 429.13 M $, respectively. The product values (PV) of the diluent from the two scenarios are 0.98 ± 0.03 and 0.79 ± 0.03 $ L−1, respectively, at a 95% confidence interval. The sensitivity analysis shows that diluent yield and internal rate of return (IRR) have the highest impact on the PV of the diluent, followed by capital cost and biomass cost. The optimum plant size at which the cost of production is lowest is 4000 dry t day−1 for PVs of 0.82 $ L−1 and 0.68 $ L−1 for the hydrogen production and purchase scenarios, respectively. This study offers insights into the techno‐economic feasibility of producing diluents from HTL. The results of the study could help in the production of diluents for bitumen transportation for the oil sands industry and help reduce the overall greenhouse gas (GHG) footprint of the oil and gas sector. © 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".