Diluted Chemical Shower Delivery System Design
Bibliographic record
Abstract
Team 2 was tasked with the design of a complete chemical delivery system for six decontamination showers at the National Microbiology Lab (NML) in Winnipeg, Manitoba. The NML is involved in disease and pathogen research, lab-based surveillance, emergency response and preparedness, and other specialized services recognized nationally and globally. The showers are containment level 4 (CL4) showers, which designate the highest biosecurity requirements prescribed by the Canadian Biosafety Standard. Staff wear positive pressure hazmat suits to enter the lab, which are decontaminated with a 5% MICRO-CHEM PLUS (MCP) solution upon exit. The NML has tasked Team 2 with the redesign as the current shower setup is inefficient and unreliable, resulting in excessive water usage and frequent pump failures. The client has also indicated that redundancy is important and that a fail-safe be implemented into the system. Reliability issues are encountered due to MCP's corrosive effect on plastics and elastomers. The current system uses around 500L of diluted chemical and water per shower per cycle, with almost monthly pump failures. Team 2 generated 8 possible core concepts focusing on improving water usage and pump reliability. The client selected three concepts: coverage optimization, research and selection of fogging nozzles, and research and selection of a chemical pump. The team conducted the necessary research and selected a nozzle type and a pump based on pressure and flow rate capabilities. The pump chosen out of 4 different categories of pumps was a magnetic drive pump, the HP Mag-Drive Pump Model HP75MD manufactured by Price Pump Co. Four different shower layouts were then presented and a final shower layout was chosen. The layout uses 3 Minifogger III nozzles for spraying MCP and 2 FogJet nozzles for rinsing the suits. Bernoulli's equation in conjunction with Reynolds number calculation to calculate major and minor pipe losses for a series of different pipe diameters and wall thicknesses. To optimize head losses, a 1" pipe with 0.065-inch wall thickness was selected. These pipe dimensions produce laminar fluid flow and combined pipe losses of 0.113 feet or 0.0491 psi. Lastly, the fail-safe pressure and flow rate were analyzed. The new design uses 0.18 gallons of MCP per shower per cycle, and 5.72 gallons of water per shower per cycle. This presents 93.2% of water savings, and 99.5% of MCP savings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".