Influence of liquid and vapourized solvents on explosibility of pharmaceutical excipient dusts
Bibliographic record
Abstract
Hybrid mixtures of a combustible dust and flammable gas are found in many industrial processes. Such fuel systems are often encountered in the pharmaceutical industry when excipient (nonpharmaceutically active ingredient) powders undergo transfer in either a dry or solvent prewetted state into an environment possibly containing a flammable gas. The research described in this article simulated the conditions of the above scenarios with microcrystalline cellulose and lactose as excipients, and methanol, ethanol, and isopropanol as solvents. Standardized dust explosibility test equipment (Siwek 20‐L explosion chamber, MIKE 3 apparatus, and BAM oven) and ASTM test protocols were used to determine the following explosibility parameters: maximum explosion pressure (Pmax), volume‐normalized maximum rate of pressure rise (KSt), minimum explosible concentration (MEC), minimum ignition energy (MIE), and minimum ignition temperature (MIT). The experimental results demonstrate the significant enhancements in explosion likelihood and explosion severity brought about by solvent admixture in either mode. The extent of solvent influence was found to be specific to the given excipient and method of solvent addition. Solvent burning velocity considerations help to account for some of the experimental observations but for others, a more rigorous evaluation of solvent and excipient physical property data is needed. © 2014 American Institute of Chemical Engineers Process Saf Prog 33: 374–379, 2014
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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) | 0.000 | 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) | 0.001 | 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".