A comparative analysis of heat recovery and product stabilization in fluid-bed and ablative pyrolysis systems
Bibliographic record
Abstract
Nearly all existing commercial pyrolysis technologies employ single-step rapid condensation of vapours from 500 oC to 50 oC using sprays of cold bio-oil or liquid hydrocarbon as a quench fluid. This approach produces raw bio-oil, a non-homogenous mixture of hundreds of oxygenated organic compounds including organic acids and water. Single-step quench also results in loss of high quality heat to the ambient. In this work, a novel 3-stage fractional condensation approach has been proposed. The intent is to produce targeted stable products for value added applications as well to enhance the overall efficiency of pyrolysis processes. The first phase of this research involved modelling and simulation of staged condensation of pyrolysis vapours using Pro/2 process software. A comprehensive pyrolysis model with 13 representative compounds was developed and validated. The Pro/2 model is able to simulate complex condensation of lignin and sugar fractions at high temperatures. Multiple cases involving staged condensation in ablative and fluid bed pyrolysis systems were investigated. In each case, there was a trade-off between high-quality heat recovery and early separation of lignin and sugars from organic acids. Results demonstrated that dew point depression adds additional complexity and limits heat recovery. However, judicious selection of condenser temperatures offers opportunity for early isolation of sugars and lignin from acids, thereby improving product stability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".