'''
MIT License
Copyright (c) [2022] [Temitope Ajayi]
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''
[docs]
class BIODG(object):
"""Process specification"""
[docs]
def __init__(self, imonod, bfac, sw1, sw2, wea, wsub, processes, biomass, icflag=0):
"""Initialization of Parameters
Parameters
-----------
imonod : int
Selects between multiplicative and minimum Monod model for the substrate
degradation rate equation
bfac : float
Reduction factor criterion for local Newton-Raphson iteration in BIOREACT
subroutine to reduce substrate residual
sw1 : float
Lower limit of aqueous phase saturation considered in the saturation inhibition
function (if =0, the default value is 0.02)
sw2 : float
Upper limit of aqueous phase saturation considered in the saturation inhibition
function (SW1 < SW2 ≤ 1)
wea : float
Weighting factor for the linear interpolation of electron acceptor and nutrients
concentrations to be used in the substrate degradation rate equation (0 < WEA ≤
1). Default value is WEA = 0.5. WEA = 1 corresponds to using the concentration
evaluated at the end of the time step
wsub : float
weighting factor for the linear interpolation of substrate concentration to be used
in the substrate degradation rate equation (0 < WSUB ≤ 1). Default value is
WSUB = 0.5. WSUB=1 corresponds to using the concentration evaluated at the
end of the time step
processes: Process
List of Processes making use of this biodegradation configuration
biomass: Biomass
Biomass class list with all properties of the biomass
icflag: int
Selects how to consider the competitive and Haldane inhibition terms in the
Monod model
Returns
--------
"""
self.biomass = biomass
self.icflag = icflag
self.imonod = imonod
self.processes = processes
self.wsub = wsub
self.wea = wea
self.sw2 = sw2
self.sw1 = sw1
self.bfac = bfac
self.null = " "
[docs]
def get_first_set(self):
""" Function that gets the first line of information
Parameters
-----------
Returns
--------
bio_numerical_parameters : list
List of numerical parameters for biodegradation
"""
bio_numerical_parameters = [self.imonod, self.icflag, self.bfac, self.null, self.sw1, self.sw2,
self.wea, self.wsub]
return bio_numerical_parameters
[docs]
def get_number_of_biomasses(self):
""" Function that gets the number of biomasses
Parameters
-----------
Returns
--------
biomass_number : int
Number of biomasses
"""
biomasses = []
for process in self.processes:
biomasses.append(process.biomass)
return len(set(biomasses))
[docs]
def get_base_parameter_and_index(self, process):
""" Function that retrieves base parameter and index
Parameters
-----------
process : bio.Process
the particular process of investigation
Returns
--------
index : int
Index and Base Parameter
"""
print(len(process.all_processes))
for i in range(len(process.all_processes)):
first = process.all_processes[i]
keys = list(first.keys())
values = list(first.values())
if values[0][1] is not None:
print(keys[0].index)
[docs]
class Process(object):
"""Process specification"""
[docs]
def __init__(self, biomass, number_of_components, mumax, yield_mass, enthalpy, total_component=0,
number_of_haldane=0, number_of_non_competiting=0,
number_competiting=0, component_params=None, gas_params=None, water_params=None):
"""Initialization of Parameters
Parameters
-----------
biomass : Biomass
Biomass class with all properties of the biomass
number_of_components : int
Number of mass components responsible for competitive inhibition in process
mumax: float
Maximum specific substrate degradation rate
yield_max: float
Yield coefficient for the growth of biomass due to the degradation of unit mass of
substrate in process IP (kg biomass / kg substrate)
enthalpy: float
Heat of reaction for the degradation of substrate in process (J/kg substrate)
total_component: int
Number of mass components controlling the substrate degradation rate in process
number_of_haldane: int
Number of mass components responsible for Haldane inhibition in process
number_of_non_competiting: int
Number of mass components responsible for non-competitive inhibition in process
component_params : list
List of chemical components involved in the process
water_params : list
List of water components involved in the process
gas_params : list
List of gas components involved in the process
Returns
--------
"""
self.enthalpy = enthalpy
self.numberOfComponents = number_of_components
self.yield_mass = yield_mass
self.mumax = mumax
self.biomass = biomass
self.water_params = water_params
self.gas_params = gas_params
self.component_params = component_params
self.all_processes = []
self.number_competiting = number_competiting
self.number_of_non_competiting = number_of_non_competiting
self.number_of_haldane = number_of_haldane
self.totalComp = total_component
[docs]
def get_number_of_competiting(self):
""" Function that retrieves number of competiting specie
Parameters
-----------
Returns
--------
num_of_competiting : int
Number of Competiting species
"""
self.component_params.values()
[docs]
def get_uptake(self):
""" Function that retrieves uptake information
Parameters
-----------
Returns
--------
output : list
list of uptake parameters
"""
output = []
for process in self.all_processes:
uptake_values = list(process.values())[0][0]
output.append(uptake_values)
return output
[docs]
def get_ks(self):
""" Function that retrieves Ks information
Parameters
-----------
Returns
--------
output : list
list of Ks parameters
"""
dict_output = {}
output = []
for i in reversed(range(len(self.all_processes))):
uptake_values = list(self.all_processes[i].values())[0][1]
dict_output[i + 1] = uptake_values
if uptake_values is not None:
output.append(dict_output)
dict_output = {}
output.reverse()
return output
[docs]
def get_kc(self):
""" Function that retrieves competitive inhibition information
Parameters
-----------
Returns
--------
output : list
list of competitive inhibition parameters
"""
dict_output = {}
output = []
for i in range(len(self.all_processes)):
uptake_values = list(self.all_processes[i].values())[0][2]
dict_output[i + 1] = uptake_values
if uptake_values is not None:
output.append(dict_output)
dict_output = {}
return output
[docs]
def get_knc(self):
""" Function that retrieves non competitive inhibition information
Parameters
-----------
Returns
--------
output : list
list of non competitive inhibition parameters
"""
dict_output = {}
output = []
for i in range(len(self.all_processes)):
uptake_values = list(self.all_processes[i].values())[0][3]
dict_output[i + 1] = uptake_values
if uptake_values is not None:
output.append(dict_output)
dict_output = {}
return output
[docs]
def get_kh(self):
""" Function that retrieves haldane inhibition information
Parameters
-----------
Returns
--------
output : list
list of haldane inhibition parameters
"""
dict_output = {}
output = []
for i in range(len(self.all_processes)):
uptake_values = list(self.all_processes[i].values())[0][4]
dict_output[i + 1] = uptake_values
if uptake_values is not None:
output.append(dict_output)
dict_output = {}
return output