SOLUTION_SPREAD -units mmol/l Number temp pH pe Al Ca Fe K Mg Na S(-2) C(4) S(6) As Br 1 10.8 6.94 7.6 0.008895478 1.848802395 0.006803939 0.104859335 0.662278898 0.701609395 7.80E-05 1.606994172 1.509479531 0.003336893 0 2 11.1 6.83 8.8 0.002965159 0.696107784 0.003043868 0.062148338 0.246400658 0.678990866 7.80E-05 0.782681099 0.499689776 0.004271223 1.0 3 11.1 6.83 8.8 0.002965159 0.696107784 0.003043868 0.062148338 0.246400658 0.678990866 7.80E-05 0.782681099 0.499689776 0.004271223 0.6END INVERSE_MODELING 1 -solutions 1 2 3 -uncertainty 0.2 1.0 0.2 -balances Al Ca Fe K Mg Na C S As Br 0.05 0.05 0.05 pH 0.05 1 0.05 -phases Fe(OH)3(a) Scorodite Magnesite Calcite Dolomite Gypsum Al(OH)3(a) Gibbsite Siderite Jarosite(ss) Jarosite-K -minimal END
First, the charge balances on your solutions are not great 10 to 17 percent error, so you are starting out with a lot of uncertainty.Second, unless you have more constraints, there is no way to sort out unknown solution composition from mineral reaction.Here is a calculation that uses solution 1, the unknown solution 2, to make solution 3, with mixing fractions approximately 0.4 and 0.6. Br was added to produce the mixing fractions. Solution 2 was given the composition of solution 3, but was given 100% uncertainties. Thus, concentrations could range anywhere from twice solution 3 to zero. Inverse modeling will then pick concentrations for solution 2 that allow for mixing of solution 1 with solution 2, while producing charge balance in solution 2.I am not sure if this calculation is helpful, but maybe it gives you some ideas on how to proceed. There are too many degrees of freedom to make any definitive statements.Code: [Select]SOLUTION_SPREAD -units mmol/l Number temp pH pe Al Ca Fe K Mg Na S(-2) C(4) S(6) As Br 1 10.8 6.94 7.6 0.008895478 1.848802395 0.006803939 0.104859335 0.662278898 0.701609395 7.80E-05 1.606994172 1.509479531 0.003336893 0 2 11.1 6.83 8.8 0.002965159 0.696107784 0.003043868 0.062148338 0.246400658 0.678990866 7.80E-05 0.782681099 0.499689776 0.004271223 1.0 3 11.1 6.83 8.8 0.002965159 0.696107784 0.003043868 0.062148338 0.246400658 0.678990866 7.80E-05 0.782681099 0.499689776 0.004271223 0.6END INVERSE_MODELING 1 -solutions 1 2 3 -uncertainty 0.2 1.0 0.2 -balances Al Ca Fe K Mg Na C S As Br 0.05 0.05 0.05 pH 0.05 1 0.05 -phases Fe(OH)3(a) Scorodite Magnesite Calcite Dolomite Gypsum Al(OH)3(a) Gibbsite Siderite Jarosite(ss) Jarosite-K -minimal END
TITLE composition of the seeped water between Gesenk 2. Sohle and Mundloch TSS #wateq4f.dat #KNOBS #solution 1, unknown solution 2 and solution 3 with mixing fractions app. 0.4 and 0.6 (Br added to produce the fractions). Solution 2 given the composition od solution 3 but with 1.0 uncertainty (concentrations could range anywhere from twice solution 3 to zero. inverse modeling will pick the concentration for solution 2 that allow for mixing of solution 1 with solution 2 while producing charge balance in solution 2. #-uncertainity default (0.05) wenn charge balance error um die 10%, uncertainty 0.1 wählen # the "minimal" indicator gives fewer mnodels at the cost of greater "sums of residuals" … sums of residuals - how much the data has been fudged; residual 1.0 means that one analytical datum has been changed by ist maximum uncertainty #alle Werte von AG BBW gemessen #<LOD-Werte =0.5 LOD gesetzt #TIC als C(4) #S(6) aus Photometrie #Fluorwerte mit Fit-Funktion berechnet #Cu, Ni, S(-2) aus dem Input gelöscht SOLUTION_SPREAD units mmol/L Number temp pH pe Al Ba Ca Fe K Li Mg Mn Na Si Sr Zn Cl C(4) S(6) N(5) As F 1 10.80 6.94 7.60 0.00889548 0.00014565 1.85 0.00680394 0.10485934 0.01008646 0.6622789 0.0076447 0.7016094 0.22961908 0.00182607 0.00290609 0.79541929 1.61 1.51 0.01784822 0.00333689 0.48952521 2 11.10 6.83 8.80 0.00296516 0.00021847 0.69610778 0.00304387 0.06214834 0.00288184 0.24640066 0.00091008 0.67899087 0.17230331 0.00102716 0.00038238 0.75028912 0.7826811 0.49968978 0.17348469 0.00427122 0.10527424 3 11.10 6.83 8.80 0.00296516 0.00021847 0.69610778 0.00304387 0.06214834 0.00288184 0.24640066 0.00091008 0.67899087 0.17230331 0.00102716 0.00038238 0.75028912 0.7826811 0.49968978 0.17348469 0.00427122 0.10527424 INVERSE_MODELING 1 -solutions 1 2 3 -uncertainty 0.2 1 0.2 -balances Al Ba Ca Fe K Li Mg Mn Na Si Sr Zn Cl C(4) S(6) N(5) As F Br 0.05 0.05 0.05 pH 0.05 1 0.05 -phases Fe(OH)3(a) Scorodite Magnesite Calcite Dolomite Gypsum Al(OH)3(a) Gibbsite Siderite Jarosite(ss) Jarosite-K CaX2 MgX2 NaX SELECTED_OUTPUT -file zusitzendes wasser inverse modeling.xls -inverse_modeling true END