HIERARCHAL INDUCTIVE PROCESS MODELING AND ANALYSIS ...
HIERARCHAL INDUCTIVE PROCESS MODELING AND ANALYSIS ...
HIERARCHAL INDUCTIVE PROCESS MODELING AND ANALYSIS ...
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(<br />
∴ P 0 + a )<br />
13<br />
α − δ Z 0 e −αlt − a 13<br />
α − δ Z 0e −δt ≤ P (t) ≤ P 0 e αut (20)<br />
P (t) > 0 ∈ (0, +∞)<br />
From a biological standpoint α l is the maximum rate of decline and α u is the maximum<br />
rate of growth. Theses bounds give us little information about the model, as<br />
they simply state that Phytoplankton concentration is contained between zero and<br />
infinity.<br />
Next we look at Detritus:<br />
dD<br />
(<br />
) (<br />
{ }} { )<br />
dt = (1 − a 10 )a 11 P + a 11 + (1 − a 12 ) a 13 (1 − e (−a 14P ) ) (1 − a 10 )Z − D(a 15 + a 18 )<br />
(<br />
)<br />
)<br />
≤ (1 − a 10 )a 11 P +<br />
(a 11 + (1 − a 12 )a 13 (1 − a 10 )Z − D(a 15 + a 18 )<br />
(<br />
)<br />
)<br />
≤ (1 − a 10 )a 11 P 0 e<br />
} {{ αut +<br />
(a<br />
} 11 + (1 − a 12 )a 13 (1 − a 10 ) Z 0 e<br />
} {{ −δt<br />
}<br />
(20)<br />
(16)<br />
− D(a 15 + a 18 )<br />
≥a 13<br />
Using (16) and (20)and simplifying a bit we get a more manageable upper bound.<br />
Solving for upper bound D u (t)<br />
dD u<br />
dt<br />
+ (a 15 + a 18 )D u = (1 − a 10 )a 11 P 0 e αut +<br />
(a 11 + (1 − a 12 )a 13<br />
)<br />
(1 − a 10 )Z 0 e −δt<br />
46