412 Alternative routes to more sustainable acrylonitrile: biosourced acrylonitrile
Bibliographic record
Abstract
Acrylonitrile is an important chemical compound in the chemical industry. It has numerous applications such as in textiles, for carbon fibers, for plastics (e. g., acrylonitrile–butadiene–styrene) and in water treatment (after conversion to acrylamide). The most common process to produce acrylonitrile is the propylene ammoxidation propylene ammoxidation (oxidation of propylene in the presence of ammonia), although recently propane ammoxidation has also been implemented. With the world looking for more sustainable supply of chemical compounds, alternative processes have been looked at to produce acrylonitrile. Alternative sources of propylene are to be considered as they would minimize the technological risks for the current acrylonitrile producers. Propylene can be produced not only from fossil feedstocks and biomass but also from recycled plastics. These alternatives will be considered and addressed. The most promising route for biomass-derived acrylonitrile production remains so far the route using glycerol glycerol as a key intermediate. Glycerol is dehydrated to acrolein (propenaldehyde) which is then reacted with ammonia in the presence of oxygen. This route is currently implemented at the demonstration scale. Glycerol can be produced not only as a coproduct of biodiesel or of the oleochemical industry, but also through selective hydrogenation of sugars, that is, splitting the intermediate sorbitol molecule into two fragments. Acrylonitrile could also be produced through CO 2 gas or sugar fermentation processes, leading to hydroxypropionamide, which can be further dehydrated to lead to acrylonitrile and/or acrylamide. Other processes such as routes through propiolactone, hydroxypropionic or glutamic acids have also been discussed in this chapter.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.020 |
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".