#=========================================================================
# create template model
builder = TemplateBuilder()
# components
components = dict()
components['AH'] = 0.395555
components['B'] = 0.0351202
components['C'] = 0.0
components['BH+'] = 0.0
components['A-'] = 0.0
components['AC-'] = 0.0
components['P'] = 0.0
builder.add_mixture_component(components)
# add algebraics
algebraics = ['0', '1', '2', '3', '4', '5', 'k4T',
'Temp'] # the indices of the rate rxns
# note the fifth, sixth and seventh components. Which basically work as inputs
builder.add_algebraic_variable(algebraics)
#Load Temp data:
dataDirectory = os.path.abspath(
os.path.join(
os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe()))),
'data_sets'))
Ttraj = os.path.join(dataDirectory, 'Tempvalues.csv')
from kipet.library.TemplateBuilder import *
from kipet.library.CasadiSimulator import *
import matplotlib.pyplot as plt
import sys
if __name__ == "__main__":
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# create template model
builder = TemplateBuilder()
builder.add_mixture_component('A', 1)
builder.add_mixture_component('B', 0)
builder.add_algebraic_variable('ra')
builder.add_parameter('k', 0.01)
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.Y[t, 'ra']
exprs['B'] = m.Y[t, 'ra']
return exprs
builder.set_odes_rule(rule_odes)
def rule_algebraics(m, t):
algebraics = list()
from kipet.library.TemplateBuilder import *
from kipet.library.CasadiSimulator import *
import matplotlib.pyplot as plt
import casadi as ca
import sys
if __name__ == "__main__":
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# create template model
builder = TemplateBuilder()
builder.add_mixture_component('A', 6.7)
builder.add_mixture_component('B', 20.0)
builder.add_mixture_component('C', 0.0)
builder.add_complementary_state_variable('T', 290.0)
builder.add_parameter('k_p', 3.734e7)
# define explicit system of ODEs
def rule_odes(m, t):
r = -m.P['k_p'] * ca.exp(
-15400.0 / (1.987 * m.X[t, 'T'])) * m.Z[t, 'A'] * m.Z[t, 'B']
T1 = 45650.0 * (-r * 0.01) / 28.0
#T2 = ca.if_else(m.X[t,'T']>328.0,0.0,2.0)
T2 = 1 + (328.0 - m.X[t, 'T']) / (
(328.0 - m.X[t, 'T'])**2 + 1e-5**2)**0.5
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# read 500x2 S matrix
wl_span = np.arange(180, 230, 10)
S_parameters = Lorentzian_parameters()
S_frame = generate_absorbance_data(wl_span, S_parameters)
# components
concentrations = {'A': 1, 'B': 0}
# create template model
builder = TemplateBuilder()
builder.add_mixture_component(concentrations)
builder.add_parameter('k', 0.1)
builder.add_absorption_data(S_frame)
builder.add_measurement_times([i for i in range(0, 50, 10)])
dataDirectory = os.path.abspath(
os.path.join(
os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe()))),
'..', 'data_sets'))
# filename = os.path.join(dataDirectory,'Dij_case52a.txt')
filename = os.path.join(dataDirectory, 'Sij_small.txt')
write_absorption_data_to_txt(filename, S_frame)
# define explicit system of ODEs
if int(sys.argv[1]):
with_plots = False
# read 200x500 D matrix
# this defines the measurement points t_i and l_j as well
dataDirectory = os.path.abspath(
os.path.join(
os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe()))),
'..', '..', 'data_sets'))
filename = os.path.join(dataDirectory, 'Slk_sawall.txt')
S_frame = read_absorption_data_from_txt(filename)
# create template model
builder = TemplateBuilder()
builder.add_mixture_component({'A': 1, 'B': 0})
builder.add_parameter('k', 0.01)
# includes spectra data in the template and defines measurement sets
builder.add_absorption_data(S_frame)
builder.add_measurement_times([i for i in range(200)])
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.P['k'] * m.Z[t, 'A']
exprs['B'] = m.P['k'] * m.Z[t, 'A']
return exprs
builder.set_odes_rule(rule_odes)
# create an instance of a casadi model template
# If you have multiple experiments, you need to add your experimental datasets to a dictionary:
datasets = {'Exp1': D_frame1, 'Exp2': D_frame2, 'Exp3': D_frame3}
#=========================================================================
# EXPERIMENT 1
#=========================================================================
#Initial conditions
builder1 = TemplateBuilder()
components1 = dict()
components1['A'] = 1e-3
components1['B'] = 0.0
components1['C'] = 0.0
components1['D'] = 0.0
components1['E'] = 0
builder1.add_mixture_component(components1)
builder1.add_parameter('k1', init=1.5, bounds=(0.001, 10))
builder1.add_parameter('k2', init=0.2, bounds=(0.0001, 5))
builder1.add_parameter('k3', init=0.4, bounds=(0.3, 2))
# Notice that, although we will not have any reaction for D and E, we still add this equation
# This model acts as the main model for all experimental datasets
# define explicit system of ODEs
# DEFINE MODEL FOR ALL EXPERIMENTS
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.P['k1'] * m.Z[t, 'A']
exprs['B'] = m.P['k1'] * m.Z[t, 'A'] - m.P['k2'] * m.Z[t, 'B']
exprs['C'] = m.P['k2'] * m.Z[t, 'B']
#=========================================================================
# Load spectral data from the relevant file location. As described in section 4.3.1
#################################################################################
dataDirectory = os.path.abspath(
os.path.join( os.path.dirname( os.path.abspath( inspect.getfile(
inspect.currentframe() ) ) ), 'data_sets'))
filename = os.path.join(dataDirectory,'new_estim_problem_conc.csv')
D_frame = read_concentration_data_from_csv(filename)
# Then we build dae block for as described in the section 4.2.1. Note the addition
# of the data using .add_concentration_data
#################################################################################
builder = TemplateBuilder()
builder.add_mixture_component('A',0.3)
builder.add_mixture_component('B',0.0)
builder.add_mixture_component('C',0.0)
builder.add_mixture_component('D',0.01)
builder.add_mixture_component('E',0.0)
#Following this we add the kinetic parameters
builder.add_parameter('k1',bounds=(0.1,2))
builder.add_parameter('k2',bounds=(0.0,2))
builder.add_parameter('k3',bounds=(0.0,2))
builder.add_parameter('k4',bounds=(0.0,2))
# define explicit system of ODEs
def rule_odes(m,t):
exprs = dict()
exprs['A'] = -m.P['k1']*m.Z[t,'A']-m.P['k4']*m.Z[t,'A']
exprs['B'] = m.P['k1']*m.Z[t,'A']-m.P['k2']*m.Z[t,'B']-m.P['k3']*m.Z[t,'B']
from kipet.library.TemplateBuilder import *
from kipet.library.PyomoSimulator import *
import matplotlib.pyplot as plt
from pyomo.core import *
import sys
if __name__ == "__main__":
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# create template model
builder = TemplateBuilder()
builder.add_mixture_component('A', 1.0)
builder.add_mixture_component('B', 0.0)
builder.add_mixture_component('C', 0.0)
builder.add_complementary_state_variable('T', 290.0)
builder.add_complementary_state_variable('V', 100.0)
# define explicit system of ODEs
def rule_odes(m, t):
k1 = 1.25 * exp((9500 / 1.987) * (1 / 320.0 - 1 / m.X[t, 'T']))
k2 = 0.08 * exp((7000 / 1.987) * (1 / 290.0 - 1 / m.X[t, 'T']))
ra = -k1 * m.Z[t, 'A']
rb = 0.5 * k1 * m.Z[t, 'A'] - k2 * m.Z[t, 'B']
rc = 3 * k2 * m.Z[t, 'B']
cao = 4.0
vo = 240
