Sn-silica catalyzes cheese whey to lactic acid in a fluidized bed reactor
Bibliographic record
Abstract
Worldwide, the dairy industry produces 200 million tons per year of whey. Among the most abundant components of cheese whey is lactose, a low-priced raw material for nutraceuticals, animal feed, and high value products such as lactic acid (LA)., a specialty chemical platform in the pharmaceutical, cosmetic, and in the food industry, and is the precursor to polylactic acid, a biodegradable polymer. Here, we propose a fluidized bed process that atomizes an aqueous lactose solution directly into a catalytic bed operating at 325 ∘ C to produce LA. We synthesized silica supported Sn catalysts by varying the metal loading (from 0 to 0.1 g g −1 of Sn) and analyzed its physicochemical properties. This research highlights the critical role of Sn as an active site to hydrolyse/isomerize/retro-aldol/dehydrate/crack lactose to lactic acid in a gas phase environment. All catalysts achieved complete conversion. SiO 2 impregnated with 0.1 g g −1 Sn catalyst achieved the maximum lactic acid yield of 23 % at 2 h of the reaction. Lactic acid yield declined thereafter due to coke blocking the active catalytic sites. XPS, Raman and CHN analysis highlighted changes in carbon distribution and evidence of coke accumulation in the spent catalyst. This article brings new findings related to significant challenges related to the continuous operation of a fluidized bed reactor in the conversion of lactose and strategies to mitigate agglomeration and coke formation. • A two fluid nozzle atomizes lactose solution into a fluidized bed of Sn-SiO 2 • Sn-silica supported catalysts convert lactose to lactic acid at 23 % yield at 2 h. • Lactic acid yield rises with Sn loading up to 0.1 g g −1 , with full lactose conversion. • Coke deactivates catalyst after 3 h operation confirmed by Raman spectroscopy and XPS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".