Experimental Evaluation and Empirical Modeling of Cross-Flow Microfiltration for Solids and Ash Removal from Fast Pyrolysis Bio-Oil
Bibliographic record
Abstract
Abstract The presence of suspended char particulate and ash in fast pyrolysis bio-oil produced from the fast pyrolysis of high ash forestry materials poses a significant technical challenge for the direct utilization and/or catalytic upgrading of these low-carbon renewable fuels. Cross-flow microfiltration is a physical upgrading process strategy that can remove suspended solids and ash from the fast pyrolysis bio-oil. To develop data sets on operational characteristics of fast pyrolysis bio-oil cross-flow microfiltration, experimental research was undertaken. Using a variety of filtration media with nominal pore sizes between 1 and 40 μm, typical solids and ash rejection ranged from 80 to 95% and 4–45%, respectively. An empirical modeling procedure was developed to predict the throughput and resistance associated with cross-flow microfiltration of fast pyrolysis bio-oil, which demonstrated good agreement with generated experimental data. Key operating parameters were also studied, and it was found that transmembrane pressures less than 1 bar and fluid preheat temperatures up to 60 °C were ideal for maximizing the pseudo steady-state flux of the process. Furthermore, the use of low viscosity, miscible solvents, and/or initial solids reduction pathways prior to microfiltration offered additional routes to potentially improve the throughput of such a process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".