Geochemical codes

Bourcier, W. L., 1985, Improvements to the solid solution modeling capabilities of the EQ3/6 geochemical code. Lawrence Livermore National Laboratory Report UCID-205 87. 

Pruess, K. 2006. Modelling brine-rock interactions in an enhanced geothermal system deep fractured reservoir at Soultz-Sous-Forets (France) a joint approach using two geochemical codes frachem and toughreact. Lawrence Berkley National Laboratory, University of California. 

Fig. 3. Simulations calculated with the PHREEQC geochemical code (Parkhust Appelo 1999) (a) time-dependent diagram for the pH evolution of the Aspo ground water/bentonite interaction (b) time-dependent diagram for the pe evolution of the Aspo groundwater/bentonite interaction. Curves correspond to different initial partial oxygen pressures. Initial calcite and pyrite contents are 0.3 wt% and 0.01 wt% respectively, except for the curve of log/02 = —0.22 where calcite and pyrite contents are 1.4 wt% and 0.3 wt%, respectively, pe calculated stands for the cases where the oxygen fugacity is obtained from the groundwater redox potential (Bruno et at. 1999).

Fig. 4. Simulations calculated with the PHREEQC geochemical code (Parkhust Appelo 1999) (a) time-dependent evolution of Eh (mV) for a Spanish granite groundwater in contact with FEBEX bentonite (b) time-dependent evolution of pH for a Spanish granite groundwater in contact with FEBEX bentonite (Arcos et al. 2000a).

Initially, Oz diffuses through the bentonite and granitic domains, controlling the redox state of the system. Once 02 is exhausted, granitic groundwater controls the redox state of the system. The results of these calculations performed with the PHREEQC geochemical code (Parkhust Appelo 1999) clearly indicate that there is a substantial variability in pH/pe space along the temporal and spatial evolution of the near field of a repositoiy. This has clear consequences for the subsequent interactions with the Fe canister material and finally with the spent fuel matrix. 

Fig. 9. Experimental solubilities as total uranium concentration in solution for experiments on dissolution of uraninite samples from Oklo and Cigar Lake. Solid lines correspond to the calculated solubilities. Calculations performed with PHREEQC geochemical code (Parkhust Appelo 1999) and uranium database taken from Grenthe et al. (1992) and Bruno Puigdomenech (1989).

Presently, thermodynamic data bases for environmental chemistry are far from being complete. We believe that many built-in data bases of geochemical codes that include an impressive number of data for aqueous species and solid phases for most elements may easily produce incorrect results if used without criticism. Indeed, one of the main lessons learnt during our update exercise is that completeness and reliability of the data are mutually exclusive. On the other hand, reducing the data base to a small number of best thermodynamic data severely limits its field of applicability. Thus, in order to model specific systems of fundamental relevance for radioactive waste disposal we were forced to make compromises and had to include estimated constants. 

Input for aqueous geochemical codes consists of field data (geology, petrology, mineralogy, and... 

One approach to determine the reliability of geochemical codes is to take well-defined input data and compare the output from several different codes. For comparison of speciation results, Nordstrom et al. (1979) compiled a seawater test case and a river-water test case, i.e., seawater and river-water analyses that were used as input to 14 different codes. TTie results were compared and contrasted, demonstrating that the thermodynamic databases, the number of ion pairs and complexes, the form of the activity coefficients, the assumptions made for redox species, and the assumptions made for equilibrium solubilities of mineral phases were prominent factors in the results. Additional arsenic, selenium, and uranium redox test cases were designed for testing of... 

In another example, five test cases were computed by PHREEQE and EQ3/6 and the same thermodynamic database was run for each program (INTERA, 1983) to test for any code differences. The five examples were speciation of seawater with major ions, speciation of seawater with complete analysis, dissolution of microchne in dilute HCl, reduction of hematite and calcite by titration with methane, and dedolomitization with gypsum dissolution and increasing temperature. The results were nearly identical for each test case. Test cases need to become standard practice when using geochemical codes so that the results will have better credibility. A comparison of code computations with experimental data on activity coefficients and mineral solubilities over a range of conditions also will improve credibility (Nordstrom, 1994). 

Criscenti L. J., Laniak G. F., and Erikson R. L. (1996) Propagation of uncertainty through geochemical code calculations. Geochim. Cosmochim. Acta 60, 3551-3568. 

Method 1 In the first method, the initial content (Aq) for DIC was calculated using the measured hydrochemical data (see Table V) by the geochemical code PHREEQE (31,32) and assuming the closed system conditions represented by the following equation ... 

Know how to calculate the solubility of a mineral such as gibbsite, kaolinite, or FefOH) (ferrihydrite) as a function of pH, both by hand and with a geochemical code such as M1NTEQA2. 

At present, there are serious limitations to the use of geochemical codes to study clay mineral solution-equilibria. These include the sparse and often dubious clay mineral stabilities given in program data bases and the fact that water analyses rarely include reliable data for both dissolved Si and Al. Also, dissolved Al is usually below detection above pH 5 to 6. When reported at higher pH s, aluminum is mostly present in suspended form, as suggested by Example 9.3. For such reasons, many researchers still prefer using graphic methods to depict the stabilities and behavior of clay minerals. 

Contrast the applicability and use of phase diagrams and geochemical codes for purposes of evaluating clay mineral equilibria in natural waters. 

The NEA Data Bank maintains a library of computer programs in various areas. This includes geochemical codes such as PHREEQE, EQ3/6, MINEQL, MINTEQ and PHRQPITZ, in which chemical thermodynamic data like those presented in this book are required as the basic input data. These computer codes can be obtained on request from the NEA Data Bank. 

In the second step of the modeling exercise, speciation and surface adsorption in the neutralized water were modeled using the geochemical code minteqa2. Analytical concentrations of Cd, Ni, Be, and U (5.5, 65,4.1, and 22 x 10-6 molL-1, respectively) were used as input concentrations. The surface properties and surface complexation constants were taken from Dzombak and Morel (1990). Due to the lack of experimental data for A1 hydroxides, we assume that all A1 hydroxides have the same sorptive properties as Fe hydroxide and that the mass of A1 hydroxide was added to the Fe hydroxide concentrations. The total sorbent concentration is 5.3 g/L as Fe(OH)3, which is calculated from the first step. minteqa2 was used to calculate the partitioning of these ions between the aqueous phase and ferric iron hydrous oxide (HFO) surfaces. 

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