def main():
Tr = 750.0
# Experimental variance is known in this problem,
# it can be estimated if not provided
noise = 0.05
# ndata = 10 in publication example
ndata = 30
ns = 9
# Define temperature bounds
Tlo = 500
Tup = 1000
# Define range of inlet concentrations
lb = [0, 0, 0, 0, 0, 1, 0, 0, 1]
ub = [3] * ns
gc = .008314
Temp = np.linspace(Tlo, Tup, ndata)
# Initialize concentration data
# Inlet concentrations are fixed in publication example
# cdata0 = [[.5,0,0,0,0,4.5,0,0,4.5]]*ndata
cdata0 = np.zeros([ndata, ns])
for i in range(ndata):
for j in range(ns):
cdata0[i, j] = random.uniform(lb[j], ub[j])
# Calculate steady-state concentration values from simulator cracsim.py
cdata = cracsim.sim(np.hstack((cdata0, np.expand_dims(Temp, axis=1))))
# In this example, we know the true stoichiometries. Lets define them first for clarity
t_stoich = [[-1, 1, 0, 0, 0, 0, 1, 0, 0], [-1, 0, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, -1, 2, 0, -2, 0, 0], [0, 0, 0, 0, -1, -2, 4, 1, 0]]
# Additional considered stoichiometries are defined
a_stoich = [[-1, 0, 0, 0, 0, -16, 21, 8, 0], [-1, 0, 0, 4, 0, 0, -3, 0, 0],
[0, 0, 0, -1, 0, -4, 6, 2, 0], [-1, 0, 1, 0, 2, 0, -2, 0, 0]]
# Index 0-3 are the true reactions, 4-7 are considered reactions
stoichs = t_stoich + a_stoich
# Define kinetic mechanisms, adsorption parameters must be known a-priori
kco = 35
kst = 1.5
# Mechanisms can be defined for each stoichiometry using a list-of-list
mechs = [[[0, 1, 3, 4, 7], eb_dep], [[0], [t_st_prod, cat_st_prod_t1]],
[[0, 1], cat_ben_prod_t2], [[2, 3], meth_prod_t3],
[[2, 3], ch4_to_co_t4], [[2, 3, 4, 5, 6, 7], [ma_g, ma_h]]]
# Experimental variance is known in this case
sigma = np.multiply(noise**2, cdata)
results = ripe.ripemodel(cdata,
stoich=stoichs,
mechanisms=mechs,
x0=cdata0,
temp=Temp,
sigma=sigma,
tref=Tr)
def main():
spec = ['X']
# Import data from csv
data = np.genfromtxt('clc.csv', delimiter=',')
t = data[:, 0]
xdata = data[:, 1]
stoich = [1]
# User pre-defined clc rate forms found in RIPE
mechs = ripe.clcforms
# Identify optimal kinetic mechanism
results = ripe.ripemodel(xdata,
stoichiometry=stoich,
mechanisms=mechs,
time=t)
def main():
spec = ['X']
# Import data from csv
data = np.genfromtxt('clc.csv', delimiter=',')
t = data[:, 0]
xdata = data[:, 1]
stoich = [1]
# User pre-defined clc rate forms found in RIPE
# mechs = ripe.clcforms
clc_mechs = [
mechs.powerlawp5, mechs.powerlaw2, mechs.powerlaw3, mechs.powerlaw4,
mechs.avrami2, mechs.avrami3, mechs.avrami4, mechs.avrami5,
mechs.randomnuc, mechs.ptompkins, mechs.jander, mechs.antijander,
mechs.valensi, mechs.parabolic, mechs.gb3d, mechs.zlt, mechs.grain
]
# Identify optimal kinetic mechanism
results = ripe.ripemodel(xdata,
stoichiometry=stoich,
mechanisms=clc_mechs,
time=t)
def main():
#ndata = 100
noise = 0.1
ns = 5
lb_conc = [0, 0, 0, 0, 0]
ub_conc = [10, 10, 0, 0, 0]
# Initialize concentration arrays
# initial concentrations - only 2 data points at bounds
cdata0 = [[1, 1, 0, 0, 0], [10, 10, 0, 0, 0]]
cdata = isotsim.sim(cdata0)
nd = len(cdata0)
# Considered reaction stoichiometries
stoich = [[-1, -1, 1, 0, 0], [0, -1, -1, 1, 0], [-1, 0, 0, -1, 1],
[-1, -2, 0, 1, 0], [-2, -2, 0, 0, 1], [-1, -1, -1, 0, 1],
[-2, -1, 1, -1, 1]]
# IRIPE internal mass action kinetics are specified
rxn_mechs = [['all', 'massact']]
# Use expected variance - estimated from data if not provided
sigma = np.multiply(noise**2, np.array(cdata))
# Call to RIPE
results = ripe.ripemodel(cdata,
stoich=stoich,
mechanisms=rxn_mechs,
x0=cdata0,
hide_output=False,
sigma=sigma,
deltaterm=0,
expand_output=True)
# Adaptive experimental design using error maximization sampling
[new_points, err] = ripe.ems(results,
isotsim.sim,
lb_conc,
ub_conc,
5,
x=cdata,
x0=cdata0)
# Implement EMS as described in the RIPE publication
new_res = isotsim.sim(new_points)[0]
ite = 0
# print 'maximum allowable tolerances : ', [noise*s for s in new_res]
while any(err > [2 * noise * s for s in new_res]):
# print 'Which concentrations violate error (True=violation) : ', err > [noise*s for s in new_res]
results = {}
ite += 1
# Data updated explicitly so RBFopt subroutines produce consistent results
new_cdata0 = np.zeros([nd + ite, ns])
new_cdata = np.zeros([nd + ite, ns])
new_cdata0[:-1][:] = cdata0[:][:]
new_cdata[:-1][:] = cdata[:][:]
new_cdata0[-1][:] = new_points[:]
res = isotsim.sim(new_points)[0]
for j in range(len(res)):
new_cdata[-1][j] = res[j]
#Update weight parameters
sigma = np.multiply(noise**2, np.array(new_cdata))
# Build updated RIPE model
results = ripe.ripemodel(new_cdata,
stoich=stoich,
mechanisms=rxn_mechs,
x0=new_cdata0,
sigma=sigma,
expand_output=True)
# Another call to EMS
[new_points, err] = ripe.ems(results,
isotsim.sim,
lb_conc,
ub_conc,
5,
x=cdata,
x0=cdata0)
# Update results
new_res = isotsim.sim(new_points)[0]
cdata0 = new_cdata0
cdata = new_cdata
# Final call to RIPE to get concise output
results = ripe.ripemodel(cdata,
stoich=stoich,
mechanisms=rxn_mechs,
x0=cdata0,
sigma=sigma,
expand_output=False)