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using ModelingToolkit | ||
using LinearAlgebra | ||
using DynamicOED | ||
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# Define the system | ||
T_min, T_max = 293.16, 473.16 | ||
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const M = [0.11911, 0.07412, 0.19323, 0.31234, 0.35733, 0.07806] | ||
const rho = [1095.0, 809.0, 1415.0, 1528.0, 1451.0, 1101.0] | ||
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K_REF1 = 5.0E-4 | ||
E_A1 = 35240.0 | ||
K_REF2 = 8.0E-8 | ||
E_A2 = 85000.0 | ||
K_REF4 = 1.0E-8 | ||
E_A4 = 35000.0 | ||
DH_2 = -17031.0 | ||
K_C2 = 0.17 | ||
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R = 8.314; | ||
T1 = 363.16; | ||
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@variables t | ||
@variables V(t)=0.5 [description = "Reaction volume"] | ||
@variables r1(t)=0.0 [description = "Reaction rate 1"] | ||
@variables r2(t)=0.0 [description = "Reaction rate 2"] | ||
@variables r3(t)=0.0 [description = "Reaction rate 3"] | ||
@variables r4(t)=0.0 [description = "Reaction rate 4"] | ||
@variables feed1(t)=0 [description = "State for feed 1"] | ||
@variables feed2(t)=0 [description = "State for feed 2"] | ||
@variables temperature(t)=293.15 [ | ||
bounds = [293.16, 473.16], | ||
description = "State for temperature", | ||
] | ||
@variables n(t)[1:6]=[0.0; 0.0; 0.0; 0.0; 0.0; 0.0] [description = "States"] | ||
@parameters u1 [ | ||
description = "Control feed 1", | ||
bounds = [0, Inf], | ||
input = true, | ||
measurement_rate = 10, | ||
] | ||
@parameters u2 [ | ||
description = "Control feed 2", | ||
bounds = [0, Inf], | ||
input = true, | ||
measurement_rate = 10, | ||
] | ||
@parameters u3 [ | ||
description = "Control temperature", | ||
bounds = [-40, 40], | ||
input = true, | ||
measurement_rate = 10, | ||
] | ||
@variables h₁(t) [description = "Observed", measurement_rate = 20] | ||
@variables h₂(t) [description = "Observed", measurement_rate = 20] | ||
h = vcat(h₁, h₂) | ||
@parameters p[1:6]=[1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0; 1.0] [ | ||
description = "Scaling parameters", | ||
tunable = true, | ||
] | ||
@parameters n0[1:3]=[0.12; 0.0; 0.0] [description = "Initial mole numbers", tunable = false] | ||
D = Differential(t) | ||
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## Define the control function, returns feed1, feed2, T | ||
eqs_ = [V * (r1 - r2 + r3); | ||
V * (r2 - r3); | ||
V * r4] | ||
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subs_ = Dict(r1 => p[1] * K_REF1 * exp(-p[2] * E_A1 / R * (1 / temperature - 1 / T1)) * | ||
n[1] * n[2] / (V * V), | ||
r2 => p[3] * K_REF2 * exp(-p[4] * E_A2 / R * (1 / temperature - 1 / T1)) * n[1] * n[3] / | ||
(V * V), | ||
r3 => p[3] * K_REF2 * exp(-p[4] * E_A2 / R * (1 / temperature - 1 / T1)) * | ||
inv(K_C2 * exp(-(-DH_2 / R) * (1 / temperature - 1 / T1))) * n[4] / V, | ||
r4 => p[5] * K_REF4 * exp(-p[6] * E_A4 / R * (1 / temperature - 1 / T1)) * (n[1] / V)^2) | ||
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eqs = map(Base.Fix2(substitute, subs_), eqs_) | ||
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# Define the eqs | ||
@named urethan = ODESystem([D(feed1) ~ u1; | ||
D(feed2) ~ u2; | ||
D(temperature) ~ u3; | ||
D(n[3]) ~ eqs[1]; #n_C | ||
D(n[4]) ~ eqs[2]; #n_D | ||
D(n[5]) ~ eqs[3]; #n_E | ||
n[1] ~ n0[1] + feed1 - n[3] - 2 * n[4] - 3 * n[5]; #n_A | ||
n[2] ~ n0[2] + feed2 - n[3] - n[4]; #n_B | ||
n[6] ~ n0[3] + feed1 + u2; #n_L | ||
V ~ sum(n .* M ./ rho)], tspan = (0.0, 80.0), | ||
observed = h .~ [100 * n[1] * M[1] / sum([ni .* M[i] for (i, ni) in enumerate(n)]); | ||
#100 * n[3]*M[3]/sum([ni .* M[i] for (i,ni) in enumerate(n)]); | ||
#100 * n[4]*M[4]/sum([ni .* M[i] for (i,ni) in enumerate(n)]); | ||
100 * n[5] * M[5] / sum([ni .* M[i] for (i, ni) in enumerate(n)])]) | ||
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## Build the OED System | ||
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@named urethan_oed = OEDSystem(structural_simplify(urethan)) | ||
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oed_problem = DynamicOED.OEDProblem(urethan_oed, DCriterion()) | ||
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optimization_variables = states(oed_problem) | ||
timegrids = DynamicOED.get_timegrids(oed_problem) |