5. Discussion
5.1 Experiment A Discussion
5.1.4 Mass Balance-Speciation Modelling Discussion- Experiment A
Due to the novel nature of the mass balance-speciation model and its many simplifying assumptions, large differences between the predicted values and the experimental values are expected. As the model gets refined by revisiting the assumptions, these differences are expected to reduce. However, revisiting the assumptions is not part of the scope of this study but appropriate recommendations for future work will be suggested.
107 There are three key reasons that could cause deviations between the experimental data and the model predicted values. The first reason could be that the experimental measurements are not correct or accurate enough to represent the system. The second reason could be that the assumption in the mass balance-chemical speciation model that the system is at equilibrium does not hold. This assumption is that all the aqueous reactions are assumed to be in equilibrium and therefore equilibrium relationships are used to predict the concentrations of all the species in the system.
Inorganic complexation reactions tend to reach equilibrium rapidly. For precipitation, the mass transfer effects of these reactions are ignored and the reaction rates are assumed to be so rapid that the precipitates are in equilibrium with the soluble ions. However, due to kinetic limitations, many precipitation reactions do not reach equilibrium (Fermoso et al., 2009) but are tending towards equilibrium. However, that does not mean to say that precipitates take a long time or do not form at all. Precipitation is a process whereby initially a precipitate nucleus forms and then this grows over time, first into an amorphous substance and later it transforms into a crystalline structure.
Therefore, in the anaerobic bioreactor, the amorphous precipitates are anticipated instead of the crystalline structures. Furthermore, the equilibrium model will most likely over-predict the amount of precipitates that will occur in the reactor since equilibrium has not been reached. This would consequently result in higher soluble metal concentrations (as well as higher counter ions such as sulphide and carbonate ions) in the reactor than the predicted equilibrium amount.
The third reason that could cause deviations between the experimental and the model predicted values is the presence of additional phases that sequester metal ions in the reactor but are not accounted for in the model. These include adsorbed metal ions and organically bound metals which are not currently in the model as it only considers two phases namely, the soluble and the precipitate phase. From the sequential extraction results (Figure 11), the organically bound phase is significant as a substantial portion of metals are sequestered in this phase while the adsorbed metals are a minority, thus validating to some extent the exclusion of adsorption in the model. The exclusion of both these phases would thus also cause the model to over-predict the amount of precipitation since (assuming the precipitate is in equilibrium with the soluble ion concentrations) some of the metal ions predicted to precipitate would be found in other phases.
Although literature indicates that organic complexation reactions are slower than inorganic complexation reactions (Turner and Mawji, 2005), organic matter plays a significant role in metal speciation by complexing and preventing precipitation or by increasing the dissolution rate (Fermoso et al., 2009, Morel and Hering, 1993). The binding of metals to organic matter is controlled by the amount of soluble or free metal ions that are available for the binding sites,
108 amongst other things (EPA, 2007). This is similar to precipitated metal and adsorbed metals as both of these are some function of the soluble metal concentration. Therefore, at equilibrium, the soluble concentration of a metal in the reactor will dictate the amount of metal ions that will be adsorbed (according to some adsorption equilibrium isotherm), the amount of metal ions that would be organically bound and the amount that would be precipitated (according to the Ksp for that precipitate assuming that the required concentration of the counter ion is present). Therefore, assuming that the system is at equilibrium, the model should predict the soluble concentrations fairly well. Any differences between the model predicted soluble concentrations and the experimentally determined values should then be due to kinetic effects, meaning that the system is not in reality at equilibrium and/or due to errors in the experimental data.
The first layer of this study was designed to demonstrate whether a speciation and precipitation approach could be used to describe mechanisms that might influence bioavailability; however, it is acknowledged that this is a significant simplification of the problem since other significant phases (the organically bound phase) have not been included. Attempts should thus be made to further develop and improve the model by revisiting the assumptions such as including the organically bound phase in the next layer of development.
5.1.4.1 Soluble Concentration Changes
From Figures 14, 15 and 16, it is observed that the model predicts that after running the reactors for a long time (about 20 cycles), all the soluble concentrations, with the exception of Mn, reach some kind of steady state value. This model-predicted steady state concentration is equal to the feed concentration if that ion is not predicted to precipitate. For those ions that are predicted to precipitate, the steady state soluble concentration would be some value lower than the feed concentration. This value would be the soluble concentration that the metal ion is in equilibrium with the precipitated species as dictated by the Ksp value for that precipitate at the system conditions (particularly the pH).
In Figure 14, when the predicted soluble concentrations are compared to the soluble concentrations obtained experimentally, the experimental values obtained for both Fe and Mg are higher than the model predicted values. This means that either there are errors with the experimental values or the system has not yet reached equilibrium and the difference between the soluble ions and the model predicted values represents those ions that are yet to precipitate before equilibrium is reached.
Therefore, it is likely that there are mass transfer limitations in the system that the model has not accounted for.
109 In Figure 15, Cu was not detected in the decanted sample nor did the model predict that Cu would be found in the soluble phase. This suggests that the soluble concentration of Cu ions that the Cu precipitates or other phases of Cu are in equilibrium with is extremely small such that it is not detectable in the ICP-AES analysis. Since the model only considers the soluble and the precipitate phases, all the Cu ions are predicted to precipitate, however, in reality, the Cu ions will most likely also occur in the other phases. This is somewhat reflected in Figure 12 where the sequential extraction of Cu shows that Cu ions may be found in the organically bound phase. This is a cautionary statement since the sequential extraction of metals in such small concentrations is not precise.
The Zn soluble ion concentrations have been predicted to reach a steady state value of less than 1 µg/l while the experimental values show that no Zn ions are soluble. This indicates that there is an error in the experimental data. This is not unexpected as the predicted concentration is very small (less than 1 µg/l) and the reported detection limit for ICP-AES analysis of Zn is 1.8 µg/l (Martin et al., 1998). Therefore, any samples with a Zn soluble concentration of less than 1.8 µg/l will not be detected and this will be reflected as a zero value. This result highlights the limitation of experimental analysis when working with metals at concentrations close to their detection limit.
When comparing the model (and reactor) feed concentrations of the Fe, Cu, Zn and Co, to the predicted soluble concentrations (Figures 14, 15 and 16), all four ions are predicted to occur in concentrations less than the feed concentrations. This means that if the system is at equilibrium, these ions are all dosed in excess since dosing concentrations higher than the Ksp concentration would only result in precipitation (assuming that the counter ions are available) that would render the metal ions potentially bioavailable or non-bioavailable. However, since the system may not be at equilibrium but in the process of tending towards equilibrium, dosing a concentration higher than the Ksp concentration may result in a higher soluble and bioavailable concentration. This is observed for Fe where it is dosed at a concentration of 0.52 mg/l, but only 0.25 mg/l is predicted to occur in the soluble phase at equilibrium while the experimental values indicate that there are concentrations of between 0.31 and 0.46 mg/l in the reactor (Figure 14).
For Cu, although the feed concentration is 5 µg/l, the predicted soluble equilibrium concentration in Figure 15 is extremely small (essentially zero). Therefore, attempting to dose a higher concentration of Cu would be fruitless. For such a scenario, the recommended alternative to investigate is to dose Cu with a chelating agent as discussed earlier (section 5.1.2.4). A similar
110 dosing strategy may be investigated for Zn and Co since both have very low predicted soluble equilibrium concentrations (0.73 and 0.77 µg/l for Zn and Co respectively).
5.1.4.2 Precipitate Formation
Figure 17 displays the percentage of ion predicted to be found within precipitates (excluding organic complexes) provides interesting results. An important observation is that there are metals that are completely sequestered in precipitates from the first cycle. These metals, Co and Cu, also have very low Ksp values with their precipitate salts. The HS-1 ion, which is the anion with which the metals form a precipitate salt, is also predicted to be completely precipitated. The metal ions Co and Cu have extremely low Ksp values with their sulphide precipitates. Literature values indicate Ksp values of between 6 x10-32 to 7.6 x10-32 mg/l for CuS and 4.0 x10-17 to 4.6 x10-17 mg/l for CoS (Sohnel & Garside, 1992 and Seely, 2007). Metals also have low Ksp values with their phosphate and carbonate precipitates. Mn was predicted to precipitate with the phosphate ion to form MnHPO4. Although the model did not predict any carbonate precipitates, most likely due to lower Ksp values for sulphide precipitates, the sequential extraction results in Figure 11 show that metal carbonates occur in the anaerobic reactors.
In order to make these metal ions soluble and bioavailable, either their precipitate salts could be removed or chelating agents can be used as recommended earlier (section 5.1.2.4). The difficulty in having such low Ksp values is that it would be near impossible to reduce their precipitate salts to such a low concentration to avoid precipitation; therefore an investigation of the use of chelating agents is recommended. This also highlights that phosphate and, more importantly, bisulphide dosage concentrations need to be optimised. S and P are both macronutrients required by the microorganisms. However, any excess S in an anaerobic system would end up as sulphide since the redox potential is low under anaerobic conditions and the thermodynamic equilibrium valence state is S2-. Furthermore, the reduction of sulphate to sulphide is a biologically catalysed redox reaction such that it is likely to end up near equilibrium and the sulphide ions will sequester any available soluble metal ions, rendering them potentially bioavailable or non-bioavailable.
When observing the change in the percentage of metal found within precipitates (potentially bioavailable and non-bioavailable) in Figure 17, the value for Mn decreases over the cycles modelled. Initially, the model predicted the precipitation of MnHPO4 to increase until that concentration levelled. Thereafter as more cycles were modelled, the concentration of MnHPO4
decreased, increasing the solubility of Mn as shown in Figure 15. This highlights that as the variables in the system change, the bioavailability as predicted by the model changes as well. This
111 also suggests that, during reactor operation, it is possible to have movement of ions between the phases.