D_frame = read_spectral_data_from_csv(filename)
meas_times = sorted(D_frame.index)
model = builder.create_pyomo_model(0, 600)
#=========================================================================
#USER INPUT SECTION - FE Factory
#=========================================================================
sim = PyomoSimulator(model)
mod = sim.model.clone()
# defines the discrete points wanted in the concentration profile
sim.apply_discretization('dae.collocation',
nfe=50,
ncp=3,
scheme='LAGRANGE-RADAU')
#: we now need to explicitly tell the initial conditions and parameter values
param_name = "P"
param_dict = {}
param_dict["P", "k0"] = 49.7796
param_dict["P", "k1"] = 8.93156
param_dict["P", "k2"] = 1.31765
param_dict["P", "k3"] = 0.310870
param_dict["P", "k4"] = 3.87809
ics_ = dict()
ics_['Z', 'AH'] = 0.395555
ics_['Z', 'B'] = 0.0351202
ics_['Z', 'C'] = 0.0
exprs['C'] = m.P['k2'] * m.Z[t, 'B']
return exprs
builder.set_odes_rule(rule_odes)
# create an instance of a pyomo model template
# the template includes
# - Z variables indexed over time and components names e.g. m.Z[t,'A']
# - P parameters indexed over the parameter names e.g. m.P['k']
pyomo_model = builder.create_pyomo_model(0.0, 10.0)
# create instance of simulator
simulator = PyomoSimulator(pyomo_model)
# defines the discrete points wanted in the concentration profile
simulator.apply_discretization('dae.collocation',
nfe=30,
ncp=2,
scheme='LAGRANGE-RADAU')
# simulate
results_pyomo = simulator.run_sim('ipopt', tee=True)
# display concentration results
if with_plots:
results_pyomo.Z.plot.line(legend=True)
plt.xlabel("time (s)")
plt.ylabel("Concentration (mol/L)")
plt.title("Concentration Profile")
plt.show()