Quick Overview¶
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Note¶
This tutorial serves as a quick primer to introduce the major (application programming interface) APIs/class in QSDsan and show you its capacities, for detailed instructions on these APIs, please follow the corresponding tutorials in the documentation.
[1]:
import qsdsan as qs
print(f'This tutorial was made with qsdsan v{qs.__version__}.')
This tutorial was made with qsdsan v1.2.0.
[2]:
# Major APIs/classes and related packages of QSDsan shown in the simplified unified modeling language (UML) diagram.
from IPython.display import Image
Image(url='https://lucid.app/publicSegments/view/c8de361f-7292-47e3-8870-d6f668691728/image.png', width=800)
[2]:
[3]:
# Thermodynamics and material flows are handled by `Component` and `WasteStream`
cmps = qs.Components.load_default()
qs.set_thermo(cmps)
cmps.show()
CompiledComponents([S_H2, S_CH4, S_CH3OH, S_Ac, S_Prop, S_F, S_U_Inf, S_U_E, C_B_Subst, C_B_BAP, C_B_UAP, C_U_Inf, X_B_Subst, X_OHO_PHA, X_GAO_PHA, X_PAO_PHA, X_GAO_Gly, X_PAO_Gly, X_OHO, X_AOO, X_NOO, X_AMO, X_PAO, X_MEOLO, X_FO, X_ACO, X_HMO, X_PRO, X_U_Inf, X_U_OHO_E, X_U_PAO_E, X_Ig_ISS, X_MgCO3, X_CaCO3, X_MAP, X_HAP, X_HDP, X_FePO4, X_AlPO4, X_AlOH, X_FeOH, X_PAO_PP_Lo, X_PAO_PP_Hi, S_NH4, S_NO2, S_NO3, S_PO4, S_K, S_Ca, S_Mg, S_CO3, S_N2, S_O2, S_CAT, S_AN, H2O])
[4]:
# Each component is asscociated with many properties
cmps.S_CH4.show(chemical_info=True)
Component: S_CH4 (phase_ref='g')
[Names] CAS: 74-82-8
InChI: CH4/h1H4
InChI_key: VNWKTOKETHGBQD-U...
common_name: methane
iupac_name: ('methane',)
pubchemid: 297
smiles: C
formula: CH4
[Groups] Dortmund: <Empty>
UNIFAC: <Empty>
PSRK: <Empty>
NIST: <Empty>
[Data] MW: 16.042 g/mol
Tm: 90.75 K
Tb: 111.65 K
Tt: 90.698 K
Tc: 190.56 K
Pt: 11691 Pa
Pc: 4.599e+06 Pa
Vc: 9.86e-05 m^3/mol
Hf: -74534 J/mol
S0: 186.3 J/K/mol
LHV: 8.0257e+05 J/mol
HHV: 8.9059e+05 J/mol
Hfus: 940 J/mol
Sfus: None
omega: 0.008
dipole: 0 Debye
similarity_variable: 0.31167
iscyclic_aliphatic: 0
combustion: {'CO2': 1, 'O2'...
Component-specific properties:
[Others] measured_as: COD
description: Dissolved Methane
particle_size: Dissolved gas
degradability: Readily
organic: True
i_C: 0.18767 g C/g COD
i_N: 0 g N/g COD
i_P: 0 g P/g COD
i_K: 0 g K/g COD
i_Mg: 0 g Mg/g COD
i_Ca: 0 g Ca/g COD
i_mass: 0.25067 g mass/g COD
i_charge: 0 mol +/g COD
i_COD: 1 g COD/g COD
i_NOD: 0 g NOD/g COD
f_BOD5_COD: 0
f_uBOD_COD: 0
f_Vmass_Totmass: 1
chem_MW: 16.042
[5]:
# Streams records the flows of each component as well as thermodynamic information
ww = qs.WasteStream.codbased_inf_model('ww', flow_tot=100)
ww.show()
WasteStream: ww
phase: 'l', T: 298.15 K, P: 101325 Pa
flow (g/hr): S_F 7.5
S_U_Inf 3.25
C_B_Subst 4
X_B_Subst 22.7
X_U_Inf 5.58
X_Ig_ISS 5.23
S_NH4 2.5
S_PO4 0.8
S_K 2.8
S_Ca 14
S_Mg 5
S_CO3 12
S_N2 1.8
S_CAT 0.3
S_AN 1.2
...
WasteStream-specific properties:
pH : 7.0
Alkalinity : 10.0 mg/L
COD : 430.0 mg/L
BOD : 249.4 mg/L
TC : 265.0 mg/L
TOC : 137.6 mg/L
TN : 40.0 mg/L
TP : 10.0 mg/L
TK : 28.0 mg/L
Component concentrations (mg/L):
S_F 75.0
S_U_Inf 32.5
C_B_Subst 40.0
X_B_Subst 226.7
X_U_Inf 55.8
X_Ig_ISS 52.3
S_NH4 25.0
S_PO4 8.0
S_K 28.0
S_Ca 140.0
S_Mg 50.0
S_CO3 120.0
S_N2 18.0
S_CAT 3.0
S_AN 12.0
...
[6]:
# Unit operations are modeled as `SanUnit` objects with process and desgin algorithms
print('`qsdsan` now has the following embedded unit operations:\n')
for i in dir(qs.sanunits):
if not i.startswith('_'):
print(i)
`qsdsan` now has the following embedded unit operations:
ActivatedSludgeProcess
AnMBR
AnaerobicBaffledReactor
AnaerobicCSTR
AnaerobicDigestion
BeltThickener
BiogasCombustion
BiogenicRefineryCarbonizerBase
BiogenicRefineryControls
BiogenicRefineryGrinder
BiogenicRefineryHHX
BiogenicRefineryHHXdryer
BiogenicRefineryHousing
BiogenicRefineryIonExchange
BiogenicRefineryOHX
BiogenicRefineryPollutionControl
BiogenicRefineryScrewPress
BiogenicRefineryStruvitePrecipitation
CH4E
CHP
CSTR
ComponentSplitter
Copier
CropApplication
Decay
DryingBed
DynamicInfluent
EcoSanAerobic
EcoSanAnaerobic
EcoSanAnoxic
EcoSanBioCost
EcoSanECR
EcoSanMBR
EcoSanPrimary
EcoSanSolar
EcoSanSystem
ElectrochemicalCell
Excretion
FakeSplitter
FlatBottomCircularClarifier
H2E
HXprocess
HXutility
HydraulicDelay
IdealClarifier
InternalCirculationRx
Lagoon
LiquidTreatmentBed
LumpedCost
MURT
MixTank
Mixer
PitLatrine
PolishingFilter
Pump
ReclaimerECR
ReclaimerHousing
ReclaimerIonExchange
ReclaimerSolar
ReclaimerSystem
ReclaimerUltrafiltration
ReversedSplitter
SBR
Sampler
Screening
Sedimentation
SepticTank
SludgeCentrifuge
SludgeDigester
SludgePasteurization
SludgeSeparator
SludgeThickening
Splitter
StorageTank
Tank
Toilet
Trucking
UDDT
WWTpump
wwtpump
[7]:
# Dynamic simulation is realized through the `Process` class
cmps = qs.processes.create_asm1_cmps(set_thermo=True)
asm1 = qs.processes.ASM1(Y_A=0.24, Y_H=0.67, f_P=0.08, i_XB=0.08, i_XP=0.06,
mu_H=4.0, K_S=10.0, K_O_H=0.2, K_NO=0.5, b_H=0.3,
eta_g=0.8, eta_h=0.8, k_h=3.0, K_X=0.1, mu_A=0.5,
K_NH=1.0, b_A=0.05, K_O_A=0.4, k_a=0.05, fr_SS_COD=0.75)
asm1.show()
ASM1([aero_growth_hetero, anox_growth_hetero, aero_growth_auto, decay_hetero, decay_auto, ammonification, hydrolysis, hydrolysis_N])
[8]:
asm1.aero_growth_hetero
Process: aero_growth_hetero
[stoichiometry] S_S: -1.0/Y_H
X_BH: 1.00
S_O: 1.0*(Y_H - 1.0)/Y_H
S_NH: -0.0800
[reference] X_BH
[rate equation] S_NH*S_O*S_S*X_BH*mu_H/((K_N...
[parameters] Y_H: 0.67
Y_A: 0.24
f_P: 0.08
mu_H: 4
K_S: 10
K_O_H: 0.2
K_NO: 0.5
b_H: 0.3
mu_A: 0.5
K_NH: 1
K_O_A: 0.4
b_A: 0.05
eta_g: 0.8
k_a: 0.05
k_h: 3
K_X: 0.1
eta_h: 0.8
[dynamic parameters]
[9]:
print(asm1.rate_equations) # looks nicer in the dark mode in readthedocs than just `asm1.rate_equations`
rate_equation
aero_growth_hetero 4.0*S_NH*S_O*S_S*X_BH/((S_NH + ...
anox_growth_hetero 0.64*S_NH*S_NO*S_S*X_BH/((S_NH ...
aero_growth_auto 0.5*S_NH*S_O*X_BA/((S_NH + 1.0)...
decay_hetero 0.3*X_BH
decay_auto 0.05*X_BA
ammonification 0.05*S_ND*X_BH
hydrolysis 3.0*X_BH*X_S*(0.16*S_NO/((S_NO ...
hydrolysis_N 3.0*X_BH*X_ND*(0.16*S_NO/((S_NO...
[10]:
# Upon simulation, you can easily retrive information related to a specific unit
# `exposan` contains systems developed using `qsdsan`,
# find more about the following exemplary system here:
# https://github.com/QSD-Group/EXPOsan/tree/main/exposan/bwaise
from exposan import bwaise as bw
# Each blue error in the diagram is a stream
bw.load()
[11]:
# The `result` function can show the simulated results
print(bw.A2.results())
Pit latrine Units A2
Design Number of users per toilet 16
Parallel toilets 2.85e+04
Emptying period yr 0.8
Single pit volume m3 3.66
Single pit area m2 0.8
Single pit depth m 4.57
Cement kg 2e+07
Sand kg 9.05e+07
Gravel kg 3.65e+07
Brick kg 6.47e+06
Plastic kg 2.88e+05
Steel kg 9.58e+05
Wood m3 5.42e+03
Excavation m3 1.04e+05
Purchase cost Total toilets USD 1.28e+07
Total purchase cost USD 1.28e+07
Utility cost USD/hr 0
Additional OPEX USD/hr 73.1
[12]:
# You can create systems by linking different units
bw.sysA.diagram()
[13]:
# `TEA` and `LCA` classes are used for techno-economic analysis and life cycle assessment
c = qs.currency
print('For sysA in the Bwaise study:\n')
for attr in ('NPV', 'EAC', 'CAPEX', 'AOC', 'sales', 'net_earnings'):
uom = c if attr in ('NPV', 'CAPEX') else (c+('/yr'))
print(f'{attr} is {getattr(bw.teaA, attr):,.0f} {uom}')
For sysA in the Bwaise study:
NPV is -42,012,332 USD
EAC is 6,706,241 USD/yr
CAPEX is 31,421,918 USD
AOC is 1,844,585 USD/yr
sales is 206,017 USD/yr
net_earnings is -1,638,568 USD/yr
[14]:
bw.lcaA
LCA: sysA (lifetime 8 yr)
Impacts:
Construction Transportation Stream Others Total
GlobalWarming (kg CO2-eq) 3.13e+07 9.57e+05 1.82e+08 5.19e+04 2.14e+08
[15]:
# Finally, uncertainty and sensitivity analyses are handled by `Model`
bw.modelA # below shows the uncertainty parameters
Finally, qsdsan also has a stats module with many global sensitivity analyses and visualization tools to generate quick and nice figures.
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