MODELING OF REACTION KINETICS AND CHEMICAL REACTORS...
Transcript of MODELING OF REACTION KINETICS AND CHEMICAL REACTORS...
MODELING OF REACTION KINETICS AND CHEMICAL REACTORS IN TRANSFORMATION OF BIOMASS
Johan Wärnå, Åbo Akademi
Laboratory of Industrial Chemistry and Reaction Engineering, Åbo Akademi
Reactormodelling
Catalysis
Kinetics
Aim with reactor and reaction kinetics modeling
Often the reaction routes, reaction kinetics and kinetic parameters are unknown
Combining Experimental work in
laboratory Mathematical modeling in
order to obtain a simulation tool for the design of reactors for the desired reaction
Scale up
ModelLab
Plant
Why do we need to know reactionkinetic parameters
Design of reactors Reaction rates determine the size or residence times
of the reactants Slow reaction longer residence time , larger
reactor
Biomaterial: Modelling works as beforebut what is new ?
Biomaterial More complex molecules from nature Mixture of molecules Often large molecules Diffusion influence makes a larger role (catalytic
reactions) A lot of properties have to be estimated or measured ?
Model building
Start with Literature studies Physical properties Derive some possible kinetic models and reaction paths
for your reaction A+B C+D 2X+Y Z Set up reactor model dc/dt=r Make experimental plan
Principles of reactor modelling
Kinetics and thermodynamics
Mass and heat-transfer Flow
REACTOR MODEL
Physicalproperties
Steps in reactor modelling
0 20 40 60 80 100 1200
10
20
30
40
50
60
70
A
B
E
F
IJ
Improve model
Moreexperiments
Fit of model toexperimental data
dcdt
rB= ⋅ρ
( )( )22
22
11
'
HHii
HjHAjj cKcK
ccKKkr
++=
∑
Reactor model
Reaction kineticsmodel
SoftwareModel solverParameter estimationMinimize object function
( )∑ −= ijestii wyyQ 2,exp,
Tid Volym ml C_syra mol/l0 427 7.55 426 7.3410 425 7.215 424 6.7830 423 6.3345 422 5.8260 421 5.7120 420 4.74180 419 4.034300 417 3.53360 416 3.38420 415 3.1261380 414 2.9
Experimental data
Reactor models
Homogeneous models One phase Gas or liquid Homogeneous catalyst
Heterogeneous Two or three phases Gas, liquid and solid catalyst
Three phase laboratory reactors
Basic Reactor Models
0 An 1An
Batch reactor
CSTRContinuos Stirred Tank Reactor
PFRPlug Flow Reactor
Batch
CSTR
PFR
ii r
dtdc
=
ii r
dtdc
=
iii rcc=
−τ
0
More complex reactor modelsWhat to include
Reaction anddiffusion
Reaction ?
Reaction ?
Gas-liquid solubilityMasstransfer
Three phase reactor model
GiLi
i
bLii
bGib
Li
kkK
cKcN 1+
−=
psLiv
bLi
R
Li aNaNdV
nd+=
•
vbLi
R
Gi aNdV
nd±=
•
2
2i ei i i
p ip
dc D d c dcs rdt dr r dr
ρε
= + +
Liquid phase
Gas phase
Gas-Liquid masstransfer
Reaction and diffusion incatalyst particle
ODE + PDE system
Experimental dataWhat to collect ?
Time Pressure Temp A B C D E pH 0 43.3 91 29.648 0.097 0.11 0.188 0 7.215 48.7 94 27.987 1.733 1.914 0.143 0 7.3220 48.6 92 22.583 5.353 2.628 0.167 0 7.0840 49.0 90 19.559 7.969 3.963 0.291 0 6.9360 50.0 90 17.847 9.762 3.772 0.066 0 6.79120 48.9 90 15.081 11.038 5.881 0.246 0 6.79180 52.5 90 12.284 12.956 6.31 0.321 0 6.73240 47.6 90 10.696 14.781 7.032 0.253 0 6.7300 48.9 90 8.334 15.558 7.412 0.327 0 6.54
Collect all possible data from your experimentsIt can be used in the model
Software for estimation of reaction kinetic parameters and reactor modelling
Parameter estimation Simulation Sensitivity Optimization
Athena Visual Studio
Modest
Software
Modest Windows or Linux Fortran compiler Intel Visual Fortran Gfortran
2 versions Windows user interface Text input file (Linux and
Windows) MCMC
Additional graphics with Matlab
Athena Visual Studio Windows Fortran compiler Intel Visual Fortran G95
Other optionsMatlabOctave (open source)Process simulator
AspenPro II
The models are not in the programsThe user has to write the program code for the modelsBasic programming skills needed !
Mathematical methodsNumerical solvers in the software
Algebraic model Non linear equation model (NLE) NLE (Non Linear Equation Systems) Newton-Raphson method Ordinary differential equation model (ODE) Backward
difference method PDE solver
Optimization and parameter estimation Simflex and/or Levenberg-Marquardt methods
Estimation of parameters and their distributions in models Markow Chain MonteCarlo MCMC
Estimation resultsk=0.042745285K=3.020598Ea=58593.68
0 500 1000 15002
3
4
5
6
7
8
60 C
70 C
Time
c
Two experiments at60 ºC and 70 ºC
Batch reactordc/dt=mcat*rate
Esterification results
Acid + Alcohol -> Ester + Water
r k e c cK
c ci
ER T T
A Beq
c D
A
medel= −
−−
1 1 1
Results, statisticsTotal SS (corrected for means) 0.8183E+02Residual SS 0.8513E+00Std. Error of estimate 0.1631E+00Explained (%): 98.96
The Hessian:
0.167E+05 0.312E+02 0.189E-030.312E+02 0.619E+00 -0.274E-070.189E-03 -0.274E-07 0.138E-08
Estimated Estimated Est. Relative Parameter/Parameters Std Error Std Error (%) Std. Error
0.453E-01 0.133E-02 2.9 34.10.306E+01 0.218E+00 7.1 14.00.708E+05 0.440E+04 6.2 16.1
The covariances of the parameters:
0.176E-05-0.891E-04 0.475E-01-0.243E+00 0.131E+02 0.193E+08
The correlation matrix of the parameters:
1.000-0.308 1.000-0.042 0.014 1.000
Sensitivity analysis: Did we find the best values for the estimated parameters
Vary one parameterAnd keep the otherParameters at fixed values
Calculate Sum of square error
Esterification example: Modest MCMC (Markow Chain Monte Carlo)
0 500 1000 15002
4
6
8 data set 1
0 500 1000 15002
4
6
8 data set 2
0 500 1000 15002
4
6
8 data set 3
2.5
3
3.5
4
4.5
Keq
k
0 5 10 15 20
x 109
6.6
6.8
7
7.2
7.4
7.6
x 104
Ea
2.5 3 3.5 4 4.5
Keq
0 0.5 1 1.5 2
x 1010
k
2 3 4 5
Keq
6.5 7 7.5 8
x 104
Ea
AEC DRT
i A Beq
c cr ke c cK
− = −
Fit of model to experimentaldata looks good
Optimal values forparameters arenot well identified
Correlation betweenEA and k
Rate equation
Esterification exampleModest MCMC
2.5
3
3.5
4
4.5
Keq
k
0.045 0.05 0.055 0.065
5.5
6
6.5
7
7.5
8
8.5
9
x 104
Ea
2.5 3 3.5 4 4.5
Keq
0.04 0.05 0.06 0.07
k
2 3 4 5
Keq
4 6 8 10
x 104
Ea
0 500 1000 15002
3
4
5
6
7
8Data set 3, yest[1]
0 500 1000 15002
3
4
5
6
7
8Data set 2, yest[1]
0 200 400 600 800 1000 1200 14002
3
4
5
6
7
8Data set 1, yest[1]
1 1AER T T C D
i A Beq
c cr ke c cK
− −
= −
Optimal parameter valuesare now well identifiedNo correlation
Modified rate equation with mean temperature of experiments
MCMCRandomly vary allParameters for theSensitivity analysis
Modeling and Scale-up of Sitosterol Hydrogenation Process: from laboratory slurry reactor to plant scale
Sitosterol hydrogenation reactions
Sitosterol
Sitostanol
Sitostanone
Sitostane
H 2
1
23
Stigmasterol
H 24
H 2
Sitosterolisomer
H 2
H 2
H 25
8
76
Campesterol
Campestanol
Campestanone
Campestane
H 29
1011
Brassicasterol
H 212
H 2
Campesterolisomer
H 2
H 2
H 213
16
1514
Bold raw materialBlue Main productRed By product
Model for Sitosterol hydrogenationIdea
Feedstock from natural sources (always different composition) Set up reaction kinetics model Reactor model for laboratory reactor Reactor model for plant reactor (10 m3)
For every new feedstock make laboratory experiments and estimate reaction kinetic parameters
Simulate the plant reactor with the new parameters
Kinetic model and reactor model
( )( )22
22
11
'
HHii
HjHAjj cKcK
ccKKkr
++=
∑
Based on this mechanistic hypothesis, the rates of the hydrogenation steps become:
DD
ccKKk=r
H
HAHA
2
211
2 D
cKk=r AA22
DD
ccKKk=r
H
HAHA
2
233
2
DD
ccKKk=r
H
HJHJ
2
244
2 DD
ccKKk=r
H
HCHC
2
255
2 DD
ccKKk=r
H
HJHJ
2
266
2
DD
ccKKk=r
H
HAHA
2
277
2 DD
ccKKk=r
H
HKHK
2
288
2 DD
ccKKk=r
H
HEHE
2
299
2
DcKk=r EE10
10 DD
ccKKk=r
H
HEHE
2
21111
2 DD
ccKKk=r
H
HIHI
2
21212
2
DD
ccKKk=r
H
HGHG
2
21313
2 DD
ccKKk=r
H
HIHI
2
21414
2
where D and DH2 are defined as
KKJJIIGG
FFEEDDCCBBAA
cKcKcKcKcKcKcKcKcKcK=D
++++++++++1
2221 HHH cK= D +
ibi rρ=
dtdc
bHvHH ρraN=
dtdc
+
− H
GvlvH c
KRTPak=aN /
ρB catalyst bulk density =mcat/VliqNH flux of hydrogen from gas to liquidK gas-liquid equilibrium constantkl gas-liquid mass transfer coefficientr reaction rate
Liquidphase
• Catalyst 1, Raw material 1– Temperatures 70ºC, 95 ºC and 120 ºC– Pressure 4, 7, 9.5 bar– Catalyst amount 2.9 g– 9 data sets
• Catalyst 2, Raw material 2– Temperatures 70ºC, 95 ºC and 120 ºC– Pressure 15, 30, 45 bar– Catalyst amount 1.25 g– 9 data sets
Experiments performed in laboratory reactor
Experiment 1-9Catalyst 1
Explained 99.82 %
Experiment 10-18Catalyst 2
Explained 99.06 %
Parameter value Std. Error(%) Parameter value Std. Error(%)
k1 (mol/ m3min) 274 13.9 548 34.1
k2 (mol/ m3min) 7.51 14.2 25.2 28.7
k3 (mol/ m3min) 1.93 17.3 57.1 23.9
k6 (mol/ m3min) 15.5 5.8 421 21.2
k7 (mol/ m3min) 2.95 19 1470 30.6
k8 (mol/ m3min) 0.0109 53.1 5.37 19.2
k9 (mol/ m3min) 259 13.9 1660 18.9
k10 (mol/ m3min) 12.2 15.1 39.9 26.8
k11 (mol/ m3min) 0.761 27.7 0.0607 68.5
k14 (mol/ m3min) 44.4 24.8 2970 34.4
ΔEA1 (J/mol) 57000 2.8 74100 5.2
ΔEA2 (J/mol) 95300 3.4 55200 20.9
ΔEA3 (J/mol) 124000 3.8 98000 9.5
ΔEA6 (J/mol) 43700 4.8 80100 6.1
ΔEA7 (J/mol) 99900 10 84400 5.3
ΔEA8 (J/mol) 27500 36.3 41200 5.7
KA (m3/mol) 0.000776 19.6 0.00161 29
KB (m3/mol) 0.718 20.1 6.51 26.2
KJ (m3/mol) 0.0186 12.5 0.0147 26.1
KK (m3/mol) 0.0618 38.2 4.87 21
KH2 (m3/mol) 0.275 11.1 0.0562 15.1
ΔH2 (J/mol) 18600 25.2 3440 58.3
0 20 40 60 80 100 1200
10
20
30
40
50
60
70
A
B
E
F
IJ
0 20 40 60 80 100 120
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
GH
CDK
Dataset 1, exp 5, 7 bar, 70°C
0 5 10 15 20 25 30 35 40 45 50 0 5
10 15 20 25 30 35 40 45 50
Experimental
Est
imat
ed
Sitosterol
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5Sitostanone
Experimental
Est
imat
ed
Fit of model to experiments
0 20 40 60 80 100 1200
10
20
30
40
50
60
70
A
B
E
F
IJ
0 20 40 60 80 100 1200
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
G
H
C
D
K
Exp 22 30 bar, 95°C
0 5 10 15 20 25 30 35 40 45 50 0 5
10 15 20 25 30 35 40 45 50
Experimental
Est
imat
ed
Sitosterol
0 0.5 1 1.5 2 2.5 0
0.5
1
1.5
2
2.5
Experimental
Est
imat
ed
Sitostanone
Fit of model to experiments
100 200 300 400 5000
1
2
3x 10
4
k1 0 5 10 15 20
0
500
1000
1500
2000
k2
0 2 4 60
500
1000
1500
2000
k3 0 10 20 30 40
0
0.5
1
1.5
2x 10
4
k6
Sensitivity plots: Check that parameters are well identified
Use of model for simulation of plant reactor (10 m3)
0 10 20 30 40 50 60 70
0 100 200 300 400 time
Simulation of a plant reactor, 4 bar 70 °C based on laboratory data. Experimental points ‘o’ from plant reactor and simulated lines.
0 2 4 6 8
10 12 14 16
0 100 200 300 400 time
Simulation of plant reactor. Concentration of hydrogen (mol/m3) in the liquid phase as a function of time (min)
Reaction and diffusion: LactoseHow are by products formed Lactose is inexpensive There is a lot of lactose (by-product of cheese manufacturing) Lactose intolerance is common Lactose can be isomerized Lactose can be oxidized Lactose can be hydrogenated Lactitol is a sweetening agent Lactulose is a laxative Lactobionic acid is an organ preserving liquid
Lactulose
Lactobionicacid
SorbitolGalactitol
O O OHOH
OHHO
OHOH
OH
O O OHOH
OHHO
OHOH
OH
Lactulitol
Lactitol
+
Alternative reaction schemes
Alternative 2
Alternative 1
Experiments + linear plots
Effect of catalyst amount Effect of hydrogen pressure
Linear plots rate constant
How are By-products formed
The behaviour of by-products is complicated It can be revealed with yield-conversion plots how
sorbitol and galactitol are formed ?
0 50 100 150 200 250 3000
5
10
15
20
25
30
35
40
45
time (min)
w-%
20 bar
20 bar
70 bar
70 bar
0 50 100 150 200 250 3000
0.1
0.2
0.3
0.4
0.5
time (min)
w-%Preliminary data fit
Main products ok By products bad
Alternative reaction schemes in brief
Alternative 1
Lactose → lactitol → sorbitol+galactitol (1,5)Lactose → lactulose → lactulitol (2,4)Lactose ↔ lactobionic acid (3)
Alternative 2
Lactose → lactitolLactose → lactulose -> lactulitolLactose ↔ lactobionic acidLactose → sorbitol + galactitol
Mass balances, rate equationsand yield
Bjiji r
dtdc ρν∑= ( )( )ll
nHHH
nHHAj
j cKcK
cckR
∑++=
11 22
22
2
AckR 11 '=AckR 22 '=
DA ckckR 333 '' −−=CckR 44 '=BckR 55 '=
)))'''(exp(/ 3210 tkkkcc BAA ρ++−=
))'exp()''))(exp('''/('(/ 5510 tktkkkkcc BBAB ρρ −−−−=))'exp()''))(exp('''/('(/ 4420 tktkkkkcc BBAC ρρ −−−−=
))''exp(1)(''/'(/ 30 tkkkcc BAD ρ−−=))'exp(1)('/1()''exp(1)(''/1))(('''/()''(/ 444410 tkktkkkkkkcc BBAE ρρ −−−−−−=))'exp(1)('/1()''exp(1)(''/1))(('''/()''(/ 555510 tkktkkkkkkcc BBAF ρρ −−−−−−=
)**)1()1)((1/(/ 5510 ααα XXcc AB −−−−=)**)1()1)((1/(/ 4420 ααα XXcc AC −−−−=
Xcc AD 30/ α=)1**)1()(1/(/ 44420 −−+−= αααα XXcc AE)1**)1()(1/(/ 55510 −−+−= αααα XXcc AF
Improved data fitting results
0 50 100 150 200 2500
5
10
15
20
25
30
35
40
Lactose
Time (min)Galactitol & Sorbitol
Lactitol
W-%
The truth is revealed
0 10
0.1
0.2
0.3
0.4
0 10
0.1
0.2
0.3
0.4
0 1 0
0.1
0.2
0.3
0.4
0 1 0
0.1
0.2
0.3
0.4
Sorbitol & Galactitol from lactose(rather bad)
Sorbitol & Galactitol is formedfrom lactitol(very good)
Yield w%
Yield w%
Lactose conversion
Lactose conversion
Particle model
( )
−= −
drrNdrr
dtdc S
iSpip
i ρε 1
∑=j
jjiji aRr ν
Reaction, diffusion and catalyst deactivation in porous particles
Particle model
Rates
Lactose hydrogenation
Concentration profilesInside catalyst particleWith different particle sizes
SurfaceCenter
LactuloseLactulitol
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
1
2
3
4
5
6
7x 10
-3
x
c (m
ol/l)
0.03 mm
0.03 mm
0.3 mm0.3 mm
1.0 mm
1.0 mm
3.0 mm
3.0 mm
0200
400600
0
0.5
10
0.2
0.4
0.6
0.8
1
1.2
1.4
0200
400600
0.5
10
0.2
0.4
0.6
0.8
1
1.2
1.4
Concentration profiles of lactose inside a catalyst particle
No deactivation Deactivation
centerxx
Production of DMC Dimethylcarbonate
•DMC•Methylating and carbonylatingagent•Biodegradable•Gasoline additive – replacingMTBE•Electrolyte in lithium batteries
DMC model
DMC
MeOH CO2
r1=k1*cA*cBr2=k2*cC*cwaterr3=k3*cE*cWaterr4=k4*cC*cFr5=k5*cG*cAr6=k6*cB*cEr7=k7*cE*cAr8=k8*cH*cBr9=k9*cH*cWater! Batch reactor modelds(1)=-2.0d0*r1-2.0d0*r5-r7+r2
+2.0d0*r4 ! metanolds(2)=-r3-r6-r7 ! butylenoxideds(3)= -r1+r2-r8 ! CO2ds(4)= r5+r8-r5+r4 ! But carbonateds(5)= r1-r2+r5-r4 ! DMCds(6)= r7-r8-r9
Wood chip
2D dynamic model Reaction Diffusion Structural changes during
reaction: porosity changes
Most complex model we havemade !
Conclusions
More and more complex molecules and mixtures from the nature enter the arena
Development of new catalyst and reactortechnologies is the key issue for betterprocesses and products
Mathematical modelling should cover allaspects: kinetics, mass transfer, flow modellingto process modelling – in a balanced way
But keep the models as simple as possible
Complex model
Reaction and diffusion in catalyst particle Catalyst mixture with different particle diameters Catalytic and noncatalytic reaction Hydrolysis of alkyl formates
HCOOR + H2O ⇄ HCOOH + ROH (where R = CH3 or C2H5)
Reactor and kinetic model
Catalyst particle Reaction+diffusion
Batch reactor Catalyst particles of
different sizes
Reaction kinetics
2cat,i P eii i i
noncat,i 2 2P P j
rρ DdC d da-1rdtε R dX X dX
C Cε
= + + +
dC 2i a y N xp j ij jdt j
noncatr= +∑
C DA B
eq
c cr k c cK
= −
1 1
0
AER T Tk k e − − =
1 1
0,ref
HR T T
eq eqK k e ∆
− − =
0
2
4
6
8
10
12
14
16
18
58.9
-68.
2
68.2
-79.
1
79.1
-91.
7
91.7
-106
106-
123
123-
143
143-
165
165-
192
192-
222
222-
258
258-
299
299-
346
346-
401
401-
465
465-
539
539-
625
625-
724
724-
840
840-
973
973-
1128
Diameter range (µm)
%
Mathematical model
Ordinary differential model (ODE) Partial Differential Model (PDE)
Backward difference Convert PDE to ODE:s by method of lines
Model code
where (s < 0.0d0 ) s=1.0d-10catrad=catrad/100 ! radius in dm
if (iobs.lt.nobs.and.t.gt.0.0d0) then t1=xdata(1,iobs) ! timet2=xdata(1,iobs+1)Te1=xdata(2,iobs)Te2=xdata(2,iobs+1)Temp=Temp+(Te2-Te1)/(t2-t1)*(t-t1)end ifVolume=Volume-float(iobs-1)*5.0d0
Temp=Temp+273.15d0 do k=1,ndistidist=(k-1)*ncom*npcat
nmax=npcat+1! call discr(npcat,2,4,nmax,discr_points,discr_int,ierr)
call diffusion(poro,tortu,temp,diff)
ap=(shape+1.0d0)*mcat/volume/catrad(k)/rhop
rhob=mcat/volume /1.0d-3! calculate concentration profile inside catalyst particle
nd=maxptsdo i=1,ncom
ix=ncom+1+(i-1)*npcat +idist
ib1 = 2 ! centerib2 = 1 ! surface
ind = 1 !3
index=0call deriv1(ind,ib1,ib2,nd,npcat,ndp2,nmax,index,h,
& s(ix),dc1(ix),dum,ierr)
call rates(conc,ratehet,ratehom,xdata,gpar, ngpar,& lpar, nlpar,nx,nobs,iobs,iset)
xpos=dfloat(j-1)/dfloat(npcat-1)! xpos=discr_points(j)
do i=1,ncom! print *,i,j,conc(i),ratehet(i)
dpr2=diff(i)/poro/catrad(k)/catrad(k)
icat=ncom+j+(i-1)*npcat+idistil=i ! bulk concentration
if (j.eq.npcat) then ! surface of catalyst! ds(icat)=dc1(icat)+kla*catrad(k)/diff(i)*(s(il)-s(icat)) ! slurry reactor
ds(icat)=dc1(icat)+kla*(s(il)-s(icat)) ! slurry reactorelse if (j.eq.1) then ! center of catalyst particle
ds(icat)= 1.0d2*dc1(icat)else
! ds(icat)=dpr2*dc2(icat)+dpr2*shape/xpos* ! & dc1(icat)+rhop/poro*cratehet(i)
ds(icat)=dpr2*dc2(icat)+dpr2*shape/xpos* & dc1(icat)+rhop/poro*ratehet(i)+ratehom(i)
! print *,i,conc(i),ratehet(i),dpr2,diff(i)end if
rate_het_particle(i,j,k)=ratehet(i)+ratehom(i)
end do! pause
end do
end do ! k
if (ierr.ne.0) thenwrite(*,*)'error from deriv ',ierrstop
end if
dum(1)=0.0d0avnlpi(1:ncom)=0.0d0
do i=1,ncomdo k=1,ndist
do j=2,npcat
Complex modelMore time neededTo write model code
Concentration inside catalyst particle for different particle sizes (mm)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 17
7.2
7.4
7.6
7.8
8
8.2
x
c m
ol/d
m3
1.1
0.9
0.79
0.68
0.58
0.50.44
0.370.32
0.15
Catalystparticle
Fit of model to experimental data
0 50 100 1500
0.5
1
1.5
time (min)
mol
1g
0 50 1000
0.5
1
1.5
time (min)
mol
2.5g
0 50 1000
0.5
1
1.5
time (min)
mol
5g
0 50 1000
0.5
1
1.5
time (min)
mol
10g
Kinetic model
1
11 1 9 2
zHMRr k K pH c θ +=
2
12 4 9 2
zHMRr k K pH c θ +=
13 3 HMRr k c=
24 4 HMRr k c=1
5 5 9 2z
MATr k K pH c θ += ( ) ( )1 26 7 8 9 2 1zHMR HMR MATf k c k c c K pHθ θ θ θ= + + + + −
( )( )1 'i i
ffθ
θ θθ+ = +
( ) ( )1 2
16 7 8 9 2' 1 z
HMR HMR MATf k c k c k c z K pHθ θ −= + + + +
Where the coverage is obtained from
The coverage can be solved iteratively with the Newton-Raphson method
The derivative is
ExampleMCMC
Reactions 1 HMR_1 → MAT 2 HMR_2 → MAT 3 HMR_1 → prop_MAT_1 4 HMR_2 → prop_MAT_2 5 MAT → BP
Modest code
do i=1,1000fun=theta+kk*
1 theta**z+sqrt(K9*pH2)*theta-1.0d0funp=1.0d0+kk*z*theta**(z-1.0d0)+sqrt(K9*pH2)
theta1=theta-fun/funp! print *,i,theta,theta1,fun,funp
if (abs(theta1-theta).lt.1.0d-5) then ! convergedtheta=theta1 goto 20
end iftheta=theta1if (i.gt.999) thenprint *,'max iterations reached 1000 ',theta,theta1stop
end if end do ! iterate again
rhoB=mcat/vol/1.0d-3 ! gram/liter
r1=k1*sqrt(K9)*cHMR1*sqrt(pH2)*theta**(z+1.0d0)r2=k2*sqrt(K9)*cHMR2*sqrt(pH2)*theta**(z+1.0d0)r3=k3*cHMR1 r4=k4*cHMR2 r5=k5*sqrt(K9)*cMAT*sqrt(pH2)*theta**(z+1.0d0)
ds(1)=(-r1-r3)*rhoB ! cHMR1ds(2)=(-r2-r4)*rhoB ! cHMR2ds(3)=(r1+r2-r5)*rhoB ! cMATds(4)= r3*rhoB ! cP1ds(5)= r4*rhoB ! cP2ds(6)= r5*rhoB ! cB
Find coverage
Kinetic and reactor model
Estimation results
Total SS (corrected for means) 0.1803E-03Residual SS 0.5421E-05Std. Error of estimate 0.1525E-03
Explained (%): 96.99
Estimated Estimated Est. Relative Parameter/Parameters Std Error Std Error (%) Std. Error
K1 0.131E+04 0.697E+03 53.4 1.9K3 0.591E-03 0.461E-03 78.1 1.3K4 0.686E-03 0.424E-03 61.9 1.6K5 0.311E+02 0.180E+02 58.0 1.7K8 0.971E+06 0.281E+06 28.9 3.5K9 0.508E-01 0.289E-01 56.8 1.8Z 0.135E+02 0.314E+01 23.2 4.3
The correlation matrix of the parameters:
1.000-0.013 1.000-0.146 -0.262 1.0000.935 -0.083 -0.216 1.0000.988 -0.012 -0.149 0.925 1.000
-0.996 0.012 0.149 -0.933 -0.995 1.0000.998 -0.013 -0.150 0.934 0.993 -0.999 1.000
MCMC, sensitivity analysis
0
5
10
15x 10
-4
2
1
0
5
10x 10
-4
3
2
20
40
60
4
3
5
10x 10
5
5
4
0.020.040.060.080.1
6
5
500100015002000250010
15
20
7
0 5 10 15
x 10-4
0 5 10
x 10-4
20 40 60 2 4 6 8 10
x 105
0.020.040.060.08 0.1
6
Did we find the optimal parameter value ?
0 500 1000 1500 2000 2500 30000
1
2
3
4
5
6
7
8x 10
-4 Parameter 1: 1
-2 0 2 4 6 8 10 12 14 16 18
x 10-4
0
500
1000
1500
2000
2500Parameter 2: 2
-2 0 2 4 6 8 10 12
x 10-4
0
500
1000
1500
2000
2500
3000Parameter 3: 3
0 10 20 30 40 50 60 70 800
0.005
0.01
0.015
0.02
0.025
0.03
0.035Parameter 4: 4
1 2 3 4 5 6 7 8 9 10 11
x 105
0
0.5
1
1.5
2
2.5
3x 10
-6 Parameter 5: 5
0 0.02 0.04 0.06 0.08 0.1 0.120
10
20
30
40
50
60
70Parameter 6: 6
Fit of model to experimental data
0 20 40 600
1
2
3
4x 10
-3 data set 1
0 20 40 600
1
2
3
4x 10
-3 data set 2
0 100 200 3000
1
2
3
4x 10
-3 data set 3
0 100 200 3000
1
2
3
4x 10
-3 data set 40 5 10 15 20 25 30
0
0.5
1
1.5
2
2.5
3
3.5x 10
-3 data set 5
0 20 40 60 80 100 1200
0.5
1
1.5
2
2.5
3
3.5
4x 10
-3 data set 6