In [1]:
import jax.numpy as np
# Defining a process model (M) for the CSTR
def CSTR(u):
v0 = 1
Ca0 = 1
H = 1
R = u[0]
RT = u[1]
volume = np.pi * R **2 * H
k1 = np.exp(-3 / RT)
k2 = np.exp(-10 / RT)
a = volume * k1
b = v0 + volume*k2
c = - v0 * Ca0
Ca = (-b + np.sqrt(b**2 - 4 * a * c)) / (2 * a)
Cb = k1 * Ca**2 * volume / v0
return np.array([Ca, Cb])
In [3]:
from opyrability import multimodel_rep, OI_eval
# AIS Bounds
AIS_bounds = np.array([[0.25, 3.00],
[1.00, 15.00]])
# Discretization Resolution
AIS_resolution = [20, 20]
# Obtain AOS
AOS_region = multimodel_rep(CSTR,
AIS_bounds,
AIS_resolution)
# DOS Bounds and resolution
DOS_bounds = np.array([[0.10, 0.35],
[0.45, 0.65]])
# Obtain Operability Index (OI)
OI = OI_eval(AOS_region, DOS_bounds)
In [4]:
from opyrability import nlp_based_approach
# Initial estimate for inverse mapping, lower/upper bounds.
u0 = np.array([3 , 10])
lb = np.array([0.25, 3])
ub = np.array([50, 50])
# DOS Bounds and resolution
DOS_bounds = np.array([[0.1 , 0.35],
[0.45, 0.65]])
DOS_resolution = [25, 25]
# Obtaining inverse map of the CSTR design
fDIS, fDOS, message = nlp_based_approach(CSTR,
DOS_bounds,
DOS_resolution,
u0,
lb,
ub,
method='ipopt',
plot=True,
ad=True,
warmstart=True)
You have selected automatic differentiation as a method for obtaining higher-order data (Jacobians/Hessian), Make sure your process model is JAX-compatible implementation-wise.
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