Team:Manchester/Model/Costing
Cost Analysis
Contents
Overview and Motivation Methodology Results ConclusionsOverview and Motivation
During our human practices discussion with the Police it was brought to our attention that not all officers carry a breathalyser device and they are rather bulky: our patch could be an alternative, compact solution that all officers could carry provided it was suitable.
This obviously introduces constraints to the device if is to be suitable for this purpose.
| Constraint Summary | Constraint | Reasoning |
|---|---|---|
| Maximum expression time | The AlcoPatch would need to equally as fast as current methods, if not faster. This would increase the likelihood of uptake as it is an improvement on the current portable methods of blood alcohol detection | Requirement suggested during the discussion |
| Minimum expression amount | Expression needs to be high enough that the result can be seen since the device it designed to be portable and used 'roadside' where lighting conditions may not be ideal | Experimental observation |
While this analysis is regarding Glucose not Alcohol it is included as a proof of concept of the system rather than to provide informative results. Since the majority of the project focused on Alcohol however these constraints were still used as all analyses should be informed and guided by the human practices.
Return to top of pageMethodology
Time independant analysis
The model was run for a range of different enzyme ratios, maintaining a constant total amount of enzyme
$$ [E_{total}] = [GOx] + [HRP]$$All simulations that didn't violate the constraints were recorded
$$[ABTS_{Oxidised}]_{t_{max}} > [ABTS_{Oxidised}]_{min}$$The cost of the simulation was estimated assuming the cost of everything but the enzyme costs are negligible
$$Cost_{total} = Cost_{GOx} [GOx] + Cost_{HRP} [HRP]$$Total Cost vs Fraction of GOx was then plotted
Time dependant analysis
The model was run for a range of different enzyme ratios, maintaining a constant total amount of enzyme
$$ [E_{total}] = [GOx] + [HRP]$$All simulations that didn't violate the constraints were recorded
$$[ABTS_{Oxidised}]_{t} > [ABTS_{Oxidised}]_{min}$$ $$t < t_{max}$$The cost of the simulation was estimated assuming the cost of everything but the enzyme costs are negligible
$$Cost_{total} = Cost_{GOx} [GOx] + Cost_{HRP} [HRP]$$The resulting surface was then filtered to return only the curve associated with the lower bound of the surface, for any given time or enzyme ratio there is only a single cost associated which is the lowest of all generated
Total Cost vs Fraction of GOx vs Time to minimum expression was then plotted
Return to top of pageResults
PLACEHOLDER FOR GRAPHS, 2D and 3D
Return to top of pageConclusions
From the graphs you can clearly see that the minimum cost can be achieved by maximising XXXXX, however it is also shown that at additional cost expense, the time to expression can be reduced if required. In some situations the constraints are not met for example at a GOx fraction of 0 or 1 in all situations, but also some ranges between these dependant on the constraint set.
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