## Probelms of Fitting data using CXTFIT

A discussion forum for STANMOD users. STudio of ANalytical MODels is a software package for evaluating solute transport in porous media using analytical solutions of the convection-dispersion solute transport equation.

mohawsh
Posts: 2
Joined: Sat Jul 24, 2010 7:47 am

### Probelms of Fitting data using CXTFIT

Dear Group

I am doing a study on the effect of olive mill waste water on soil transport properties under un saturated condtions. I did a leaching experiment using inert tracer (Br) for 10 days and I took samples to measure Br. I want to draw BTCs and calculate D and V using STANMOD. I tried many times but no good fitting to the original data.

The input file is

0,000267164 20 0,01875
0,000291045 20 0,09375
0,000377612 20 0,16875
0,000501493 20 0,24375
0,00530149 20 0,328125
0,0153776 20 0,421875
0,0232119 20 0,515625
0,021409 20 0,609375
0,0163731 20 0,703125
0,0128821 20 0,796875
0,00954179 20 0,890625
0,00718955 20 0,984375
0,00502537 20 1,07813
0,00322388 20 1,17188
0,00230746 20 1,26563
0,00152537 20 1,35938
0,00119851 20 1,45313
0,000964179 20 1,54688
0,000764179 20 1,64063
0,000628358 20 1,73438
0,000537313 20 1,82813
0,000292537 20 1,92188
0,00135373 20 2,01563
0,000271642 20 2,10938
0,000535821 20 2,20313

the output file is

Model description
=================
Deterministic equilibrium CDE (Mode=1)
Resident concentration (third-type input)
Real time (t), Position(x)
(D,V,mu, and gamma are also dimensional)

Initial values of coefficients
==============================
Name Initial value Fitting Min value Max value
V........ .4500E+01 Y .1000E-01 .1000E+03
D........ .1000E+01 Y .1000E-01 .1000E+03
R........ .1000E+01 N
mu....... .0000E+00 N
Cin...... .1000E+01 N
T2....... .2200E+01 Y .1000E+00 .2500E+01

Boundary, initial, and production conditions
===========================================
<Initial estimate of b.c.>
Single pulse of conc. = 1.0000 & duration = 2.2000
Solute free initial condition
No production term

Parameter estimation mode
=========================
Maximum number of iterations = 20
Duration time, T2, is fitted to the data
.1000 < T2 < 2.5000

Iter SSQ V.... D.... T2...
0 .1888E-02 .450E+01 .100E+01 .220E+01
1 .1874E-02 .100E-01 .100E+03 .220E+01
2 .1874E-02 .100E-01 .100E+03 .220E+01

Covariance matrix for fitted parameters
=======================================
V.... D.... T2...
V.... 1.000
D.... -.948 1.000
T2... .000 .000 .000

RSquare for regression of observed vs predicted =-.56389164
(Coefficeint of determination)

Mean square for error (MSE) = .8517E-04

Non-linear least squares analysis, final results
================================================

95% Confidence limits
Name Value S.E.Coeff. T-Value Lower Upper
V.... .1000E-01 .3044E+00 .3285E-01 -.6213E+00 .6413E+00
D.... .1000E+03 .5388E+04 .1856E-01 -.1107E+05 .1127E+05
T2... .2200E+01 .9229E-32 .2384E+33 .2200E+01 .2200E+01

------------------Ordered by computer input-------------------
Concentration Resi-
No Distance Time Obs Fitted Dual
1 20.0000 .0187 .0003 .0000 .0003
2 20.0000 .0938 .0003 .0000 .0003
3 20.0000 .1688 .0004 .0000 .0004
4 20.0000 .2437 .0005 .0000 .0005
5 20.0000 .3281 .0053 .0000 .0053
6 20.0000 .4219 .0154 .0000 .0154
7 20.0000 .5156 .0232 .0000 .0232
8 20.0000 .6094 .0214 .0000 .0214
9 20.0000 .7031 .0164 .0000 .0163
10 20.0000 .7969 .0129 .0001 .0128
11 20.0000 .8906 .0095 .0001 .0095
12 20.0000 .9844 .0072 .0001 .0071
13 20.0000 1.0781 .0050 .0001 .0049
14 20.0000 1.1719 .0032 .0001 .0031
15 20.0000 1.2656 .0023 .0002 .0021
16 20.0000 1.3594 .0015 .0002 .0013
17 20.0000 1.4531 .0012 .0002 .0010
18 20.0000 1.5469 .0010 .0002 .0007
19 20.0000 1.6406 .0008 .0002 .0005
20 20.0000 1.7344 .0006 .0003 .0004
21 20.0000 1.8281 .0005 .0003 .0002
22 20.0000 1.9219 .0003 .0003 .0000
23 20.0000 2.0156 .0014 .0003 .0010
24 20.0000 2.1094 .0003 .0004 -.0001
25 20.0000 2.2031 .0005 .0004 .0002

Z= 1.0000 (Resident conc. vs. time)
Sum(C*dT) = .0031, Sum(Ct*dT)= .0031
Time C Ct (=R*C)
.0000 .00000E+00 .00000E+00
.1000 .26569E-03 .26569E-03
.2000 .41086E-03 .41086E-03
.3000 .52309E-03 .52309E-03
.4000 .61797E-03 .61797E-03
.5000 .70169E-03 .70169E-03
.6000 .77746E-03 .77746E-03
.7000 .84718E-03 .84718E-03
.8000 .91210E-03 .91210E-03
.9000 .97310E-03 .97310E-03
1.0000 .10308E-02 .10308E-02
1.1000 .10857E-02 .10857E-02
1.2000 .11382E-02 .11382E-02
1.3000 .11885E-02 .11885E-02
1.4000 .12369E-02 .12369E-02
1.5000 .12837E-02 .12837E-02
1.6000 .13289E-02 .13289E-02
1.7000 .13727E-02 .13727E-02
1.8000 .14152E-02 .14152E-02
1.9000 .14566E-02 .14566E-02
2.0000 .14969E-02 .14969E-02
2.1000 .15362E-02 .15362E-02
2.2000 .15746E-02 .15746E-02
2.3000 .13870E-02 .13870E-02
2.4000 .12662E-02 .12662E-02
2.5000 .11846E-02 .11846E-02
2.6000 .11219E-02 .11219E-02
2.7000 .10706E-02 .10706E-02
2.8000 .10272E-02 .10272E-02
2.9000 .98967E-03 .98967E-03
Z= 2.0000 (Resident conc. vs. time)
Sum(C*dT) = .0029, Sum(Ct*dT)= .0029
Time C Ct (=R*C)
.0000 .00000E+00 .00000E+00
.1000 .19192E-03 .19192E-03
.2000 .32962E-03 .32962E-03
.3000 .43847E-03 .43847E-03
.4000 .53133E-03 .53133E-03
.5000 .61367E-03 .61367E-03
.6000 .68841E-03 .68841E-03
.7000 .75733E-03 .75733E-03
.8000 .82161E-03 .82161E-03
.9000 .88208E-03 .88208E-03
1.0000 .93934E-03 .93934E-03
1.1000 .99385E-03 .99385E-03
1.2000 .10460E-02 .10460E-02
1.3000 .10960E-02 .10960E-02
1.4000 .11442E-02 .11442E-02
1.5000 .11907E-02 .11907E-02
1.6000 .12357E-02 .12357E-02
1.7000 .12793E-02 .12793E-02
1.8000 .13217E-02 .13217E-02
1.9000 .13629E-02 .13629E-02
2.0000 .14030E-02 .14030E-02
2.1000 .14422E-02 .14422E-02
2.2000 .14805E-02 .14805E-02
2.3000 .13632E-02 .13632E-02
2.4000 .12519E-02 .12519E-02
2.5000 .11742E-02 .11742E-02
2.6000 .11135E-02 .11135E-02
2.7000 .10637E-02 .10637E-02
2.8000 .10213E-02 .10213E-02
2.9000 .98448E-03 .98448E-03
Z= 3.0000 (Resident conc. vs. time)
Sum(C*dT) = .0027, Sum(Ct*dT)= .0027
Time C Ct (=R*C)
.0000 .00000E+00 .00000E+00
.1000 .13424E-03 .13424E-03
.2000 .26035E-03 .26035E-03
.3000 .36380E-03 .36380E-03
.4000 .45337E-03 .45337E-03
.5000 .53345E-03 .53345E-03
.6000 .60651E-03 .60651E-03
.7000 .67412E-03 .67412E-03
.8000 .73735E-03 .73735E-03
.9000 .79693E-03 .79693E-03
1.0000 .85345E-03 .85345E-03
1.1000 .90732E-03 .90732E-03
1.2000 .95889E-03 .95889E-03
1.3000 .10084E-02 .10084E-02
1.4000 .10562E-02 .10562E-02
1.5000 .11023E-02 .11023E-02
1.6000 .11469E-02 .11469E-02
1.7000 .11902E-02 .11902E-02
1.8000 .12323E-02 .12323E-02
1.9000 .12732E-02 .12732E-02
2.0000 .13131E-02 .13131E-02
2.1000 .13520E-02 .13520E-02
2.2000 .13901E-02 .13901E-02
2.3000 .13254E-02 .13254E-02
2.4000 .12287E-02 .12287E-02
2.5000 .11569E-02 .11569E-02
2.6000 .10998E-02 .10998E-02
2.7000 .10522E-02 .10522E-02
2.8000 .10114E-02 .10114E-02
2.9000 .97585E-03 .97585E-03
Z= 4.0000 (Resident conc. vs. time)
Sum(C*dT) = .0025, Sum(Ct*dT)= .0025
Time C Ct (=R*C)
.0000 .00000E+00 .00000E+00
.1000 .90758E-04 .90758E-04
.2000 .20233E-03 .20233E-03
.3000 .29866E-03 .29866E-03
.4000 .38383E-03 .38383E-03
.5000 .46084E-03 .46084E-03
.6000 .53161E-03 .53161E-03
.7000 .59743E-03 .59743E-03
.8000 .65920E-03 .65920E-03
.9000 .71757E-03 .71757E-03
1.0000 .77306E-03 .77306E-03
1.1000 .82604E-03 .82604E-03
1.2000 .87684E-03 .87684E-03
1.3000 .92569E-03 .92569E-03
1.4000 .97280E-03 .97280E-03
1.5000 .10184E-02 .10184E-02
1.6000 .10625E-02 .10625E-02
1.7000 .11053E-02 .11053E-02
1.8000 .11470E-02 .11470E-02
1.9000 .11876E-02 .11876E-02
2.0000 .12271E-02 .12271E-02
2.1000 .12657E-02 .12657E-02
2.2000 .13035E-02 .13035E-02
2.3000 .12761E-02 .12761E-02
2.4000 .11973E-02 .11973E-02
2.5000 .11334E-02 .11334E-02
2.6000 .10808E-02 .10808E-02
2.7000 .10363E-02 .10363E-02
2.8000 .99778E-03 .99778E-03
2.9000 .96388E-03 .96388E-03
Z= 5.0000 (Resident conc. vs. time)
Sum(C*dT) = .0023, Sum(Ct*dT)= .0023
Time C Ct (=R*C)
.0000 .00000E+00 .00000E+00
.1000 .59227E-04 .59227E-04
.2000 .15461E-03 .15461E-03
.3000 .24252E-03 .24252E-03
.4000 .32235E-03 .32235E-03
.5000 .39559E-03 .39559E-03
.6000 .46352E-03 .46352E-03
.7000 .52709E-03 .52709E-03
.8000 .58703E-03 .58703E-03
.9000 .64389E-03 .64389E-03
1.0000 .69808E-03 .69808E-03
1.1000 .74994E-03 .74994E-03
1.2000 .79976E-03 .79976E-03
1.3000 .84774E-03 .84774E-03
1.4000 .89408E-03 .89408E-03
1.5000 .93893E-03 .93893E-03
1.6000 .98244E-03 .98244E-03
1.7000 .10247E-02 .10247E-02
1.8000 .10658E-02 .10658E-02
1.9000 .11059E-02 .11059E-02
2.0000 .11450E-02 .11450E-02
2.1000 .11832E-02 .11832E-02
2.2000 .12205E-02 .12205E-02
2.3000 .12184E-02 .12184E-02
2.4000 .11588E-02 .11588E-02
2.5000 .11041E-02 .11041E-02
2.6000 .10571E-02 .10571E-02
2.7000 .10163E-02 .10163E-02
2.8000 .98052E-03 .98052E-03
2.9000 .94872E-03 .94872E-03
Z= 6.0000 (Resident conc. vs. time)
Sum(C*dT) = .0021, Sum(Ct*dT)= .0021
Time C Ct (=R*C)
.0000 .00000E+00 .00000E+00
.1000 .37254E-04 .37254E-04
.2000 .11611E-03 .11611E-03
.3000 .19473E-03 .19473E-03
.4000 .26848E-03 .26848E-03
.5000 .33736E-03 .33736E-03
.6000 .40197E-03 .40197E-03
.7000 .46291E-03 .46291E-03
.8000 .52069E-03 .52069E-03
.9000 .57574E-03 .57574E-03
1.0000 .62838E-03 .62838E-03
1.1000 .67891E-03 .67891E-03
1.2000 .72755E-03 .72755E-03
1.3000 .77449E-03 .77449E-03
1.4000 .81990E-03 .81990E-03
1.5000 .86392E-03 .86392E-03
1.6000 .90666E-03 .90666E-03
1.7000 .94823E-03 .94823E-03
1.8000 .98872E-03 .98872E-03
1.9000 .10282E-02 .10282E-02
2.0000 .10668E-02 .10668E-02
2.1000 .11044E-02 .11044E-02
2.2000 .11413E-02 .11413E-02
2.3000 .11553E-02 .11553E-02
2.4000 .11144E-02 .11144E-02
2.5000 .10697E-02 .10697E-02
2.6000 .10289E-02 .10289E-02
2.7000 .99250E-03 .99250E-03
2.8000 .95990E-03 .95990E-03
2.9000 .93055E-03 .93055E-03
Z= 7.0000 (Resident conc. vs. time)
Sum(C*dT) = .0020, Sum(Ct*dT)= .0020
Time C Ct (=R*C)
.0000 .00000E+00 .00000E+00
.1000 .22559E-04 .22559E-04
.2000 .85655E-04 .85655E-04
.3000 .15456E-03 .15456E-03
.4000 .22173E-03 .22173E-03
.5000 .28579E-03 .28579E-03
.6000 .34668E-03 .34668E-03
.7000 .40465E-03 .40465E-03
.8000 .45998E-03 .45998E-03
.9000 .51296E-03 .51296E-03
1.0000 .56384E-03 .56384E-03
1.1000 .61283E-03 .61283E-03
1.2000 .66011E-03 .66011E-03
1.3000 .70585E-03 .70585E-03
1.4000 .75019E-03 .75019E-03
1.5000 .79323E-03 .79323E-03
1.6000 .83509E-03 .83509E-03
1.7000 .87585E-03 .87585E-03
1.8000 .91559E-03 .91559E-03
1.9000 .95440E-03 .95440E-03
2.0000 .99232E-03 .99232E-03
2.1000 .10294E-02 .10294E-02
2.2000 .10657E-02 .10657E-02
2.3000 .10892E-02 .10892E-02
2.4000 .10655E-02 .10655E-02
2.5000 .10311E-02 .10311E-02
2.6000 .99696E-03 .99696E-03
2.7000 .96525E-03 .96525E-03
2.8000 .93619E-03 .93619E-03
2.9000 .90960E-03 .90960E-03
Z= 8.0000 (Resident conc. vs. time)
Sum(C*dT) = .0018, Sum(Ct*dT)= .0018
Time C Ct (=R*C)
.0000 .00000E+00 .00000E+00
.1000 .13135E-04 .13135E-04
.2000 .62037E-04 .62037E-04
.3000 .12124E-03 .12124E-03
.4000 .18153E-03 .18153E-03
.5000 .24045E-03 .24045E-03
.6000 .29732E-03 .29732E-03
.7000 .35203E-03 .35203E-03
.8000 .40467E-03 .40467E-03
.9000 .45536E-03 .45536E-03
1.0000 .50427E-03 .50427E-03
1.1000 .55155E-03 .55155E-03
1.2000 .59732E-03 .59732E-03
1.3000 .64171E-03 .64171E-03
1.4000 .68483E-03 .68483E-03
1.5000 .72678E-03 .72678E-03
1.6000 .76763E-03 .76763E-03
1.7000 .80748E-03 .80748E-03
1.8000 .84639E-03 .84639E-03
1.9000 .88442E-03 .88442E-03
2.0000 .92162E-03 .92162E-03
2.1000 .95805E-03 .95805E-03
2.2000 .99375E-03 .99375E-03
2.3000 .10225E-02 .10225E-02
2.4000 .10133E-02 .10133E-02
2.5000 .98890E-03 .98890E-03
2.6000 .96168E-03 .96168E-03
2.7000 .93497E-03 .93497E-03
2.8000 .90971E-03 .90971E-03
2.9000 .88610E-03 .88610E-03
Z= 9.0000 (Resident conc. vs. time)
Sum(C*dT) = .0017, Sum(Ct*dT)= .0017
Time C Ct (=R*C)
.0000 .00000E+00 .00000E+00
.1000 .73473E-05 .73473E-05
.2000 .44094E-04 .44094E-04
.3000 .93954E-04 .93954E-04
.4000 .14732E-03 .14732E-03
.5000 .20091E-03 .20091E-03
.6000 .25354E-03 .25354E-03
.7000 .30478E-03 .30478E-03
.8000 .35451E-03 .35451E-03
.9000 .40274E-03 .40274E-03
1.0000 .44951E-03 .44951E-03
1.1000 .49491E-03 .49491E-03
1.2000 .53902E-03 .53902E-03
1.3000 .58193E-03 .58193E-03
1.4000 .62372E-03 .62372E-03
1.5000 .66445E-03 .66445E-03
1.6000 .70421E-03 .70421E-03
1.7000 .74305E-03 .74305E-03
1.8000 .78102E-03 .78102E-03
1.9000 .81819E-03 .81819E-03
2.0000 .85459E-03 .85459E-03
2.1000 .89028E-03 .89028E-03
2.2000 .92528E-03 .92528E-03
2.3000 .95656E-03 .95656E-03
2.4000 .95907E-03 .95907E-03
2.5000 .94406E-03 .94406E-03
2.6000 .92368E-03 .92368E-03
2.7000 .90208E-03 .90208E-03
2.8000 .88077E-03 .88077E-03
2.9000 .86031E-03 .86031E-03
Z= 10.0000 (Resident conc. vs. time)
Sum(C*dT) = .0016, Sum(Ct*dT)= .0016
Time C Ct (=R*C)
.0000 .00000E+00 .00000E+00
.1000 .39443E-05 .39443E-05
.2000 .30743E-04 .30743E-04
.3000 .71917E-04 .71917E-04
.4000 .11847E-03 .11847E-03
.5000 .16668E-03 .16668E-03
.6000 .21494E-03 .21494E-03
.7000 .26257E-03 .26257E-03
.8000 .30925E-03 .30925E-03
.9000 .35485E-03 .35485E-03
1.0000 .39934E-03 .39934E-03
1.1000 .44273E-03 .44273E-03
1.2000 .48506E-03 .48506E-03
1.3000 .52638E-03 .52638E-03
1.4000 .56672E-03 .56672E-03
1.5000 .60615E-03 .60615E-03
1.6000 .64471E-03 .64471E-03
1.7000 .68245E-03 .68245E-03
1.8000 .71941E-03 .71941E-03
1.9000 .75563E-03 .75563E-03
2.0000 .79116E-03 .79116E-03
2.1000 .82603E-03 .82603E-03
2.2000 .86027E-03 .86027E-03
2.3000 .89248E-03 .89248E-03
2.4000 .90385E-03 .90385E-03
2.5000 .89733E-03 .89733E-03
2.6000 .88353E-03 .88353E-03
2.7000 .86703E-03 .86703E-03
2.8000 .84975E-03 .84975E-03
2.9000 .83254E-03 .83254E-03

Regards,
osama