Life cycle GHG emissions and economic viability of two levulinic acid production processes from biomass: A case study of Japan and Canada
Bibliographic record
Abstract
We evaluated CO 2 equivalent (CO 2 eq) greenhouse gas (GHG) emissions and minimum selling price of levulinic acid (LA) produced in two biomass-waste-based processes: the AlCl 3 /choline chloride (ChCl) process, and the formic acid (FA) process, with catalysts recycling. Six scenarios were synthesized to compare the performances of the two processes in Japan and Canada. In the AlCl 3 /ChCl process, the total GHG emission was 11.35–11.56 kg-CO 2 eq/kg-LA and those from the energy input to the pretreatment and ChCl production were 5.22 and 3.90 kg-CO 2 eq/kg-LA, respectively. In the FA process, the total GHG emission was 9.46–9.68 and 22.29–22.51 kg-CO 2 eq/kg-LA for 60 wt% and 80 wt% FA, respectively. The operational emissions for makeup FA input were 7.65 and 20.80 kg-CO 2 eq/kg-LA (60 wt% and 80 wt%, respectively), which accounted for more than 80% in all scenarios. The optimization of the product purge volume, FA concentration in the pretreatment, and FA production using biomass and/or renewable energy are critical parameters to reduce overall environmental impacts of the processes. The liquid content of the solid residue (moisture, water soluble organic matters, and catalyst) had insignificant influences on the GHG emission and minimum selling price. In the FA process, combustion of solid residue can compensate the GHG emissions from the reaction and separation units. • Life-cycle assessment of levulinic acid (LA) production processes was conducted. • AlCl 3 /Choline Chloride or formic acid catalysts were used for LA production. • Operational CO 2 eq emissions of makeup catalysts is dominantly high. • Optimization of product purge volume is important to reduce total CO 2 eq emissions. • Combustion of solid residues can greatly reduce the net CO 2 eq emission.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".