Framework for consequential life cycle assessment of pyrolysis biorefineries: A case study for the conversion of primary forestry residues
Bibliographic record
Abstract
The development of bioeconomy needs to accelerate in order to get rid of fossil fuels and products. The production of bio-based products and renewable energy sources from the thermochemical conversion of biomass can be a sustainable alternative. Pyrolysis is one of the thermochemical processes that can convert biomass into liquid (bio-oil), solid (biochar) and gaseous (non-condensable gases) products. Process operational parameters and biomass type must be selected appropriately to obtain the desired products yields and properties. Before deciding to apply the technology on a large scale, it is necessary to determine the environmental performance of the process. This can be done through the life cycle assessment (LCA) method. This study presents a consequential LCA framework to quantify the environmental performance of a pyrolysis biorefinery by including in the boundaries the current use of biomass and the marginal technologies that are expected to be replaced by pyrolysis co-products. Results obtained from this method are intended to provide answers to decision-makers towards investments in the low fossil carbon future. The proposed LCA framework was applied to a case study for the use of primary forestry residues (PFR). Results showed that as compared to the reference scenario in which PFR are left on soil to decay, pyrolysing PFR to biocrude oil, wood vinegar, biochar and gas presents trade-offs in six out of the 16 impact categories studied. These results highlighted that the biomass feedstock supply, the pyrolysis technology, the co-products yields, properties and uses, as well as the choice of marginal technologies have an influence on the environmental performance of pyrolysis biorefineries.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".