Application of Physical Sustainability Metrics to Water Treatment Technology Comparisons.

Transcription

1 Application of Physical Sustainability Metrics to Water Treatment Technology Comparisons. Marc. F Muller Prof Slav Hermanowicz Civil and Environmental Department University of California Berkeley,

2 Contents A. Introduction... 3 Previous research... 3 B. Methodology Drinking water treatment processes Mass conservation calculations Energy calculation Entropy Calulations... 9 C. Results Global Results Process breakdown Temperature Effect D. Discussion Inflow water parameters System definition E. Conclusion F. Sources

3 Application of Physical Sustainability Metrics to Water Treatment Technology Comparisons. A. Introduction In the context of today s increasing importance of global warming, the concept of sustainable development has become more and more popular, and might potentially become an influential argument in natural resource management decisions in the future. As a matter of fact, given the increasing complexity and costs embedded in the management of such a vital and scarce resource as drinking water in today s geopolitical and demographical context, it cannot be afforded not to take sustainability into account. Yet, deep paradoxes and ethical issues underlie the very concept of sustainable development [1] as originally defined by the Brundtland [2] report, and it is a fact that the very meaning of that concept is clearly not a universal consensus nowadays. The solution to this issue might not be trivial at all. There might indeed never be a possible philosophical consensus to the real meaning of sustainable development. Yet, in the well-defined context of drinking water technology, there might be a consensus to fall on. As a matter of fact, if sustainability ever is to play an effective role in drinking water management decision, there has to be a consensual, universal definition of sustainability. Such a consensus on such a vague concept is easiest met when the idea can be measured, and decisions evaluated through a rational methodology, hence the importance of sustainability metrics and efficient sustainability indicators. The challenge is to develop indicators that are simple and intuitive enough to be efficient decisional and communicational tools, but complex enough to take into account all the relevant aspects of the contextualized process in one single figure. Moreover, the indicator must ideally be as little data intensive as possible to facilitate and cheapen its assessment, but must be based on enough data to allow a detailed process breakdown to the small scale processes. Finally, as a comparison means, indicators must ideally be (process) additive and volume intensive variables, i.e the global value is the sum of the process breakdown value and does not depend on the processes physical capacity. This report aims to take part to this quest by focusing on the physical and thermodynamic aspect of sustainability. Specifically, on the basis of previous research [3], a physical sustainability indicator based on the bottom up aggregation of entropy change and energy flux of fundamental processes will be put into application and observed. Previous research The present paper follows and is based on [3], which principal results are presented in the following section. Based on a bottom up approach and the aggregation of fundamental chemical transformations and mass transport processes by terms of their entropy difference and energy flux, a physical sustainability indicator has been developed and will be applied in this work. The indicator aims to represent the environmental impact (in Joules per incoming material flow volume), in terms of the sum of the required energy flow and the total resulting entropy difference multiplied by the temperature. The indicator is to decrease if the sustainability is to increase. Ω = Pr ocess E i + TΔS i 3

4 The rationale for this approach is that a smaller engineered energy flux would have a smaller environmental impact in terms of the resources necessary to produce this evoluated energy form. Given the impact of this energy flow, the environmental impact of the global process will be largest if it leads to an entropy increase and smaller as the entropy decreases. The indicator was applied to the comparison of two basic treatment methods for drinking water: membrane filtration and flocculation. The treatments were taken alone, without consideration to waste management. A comparison was made on the basis of standard natural water input and standard operation parameters for the two processes. The considered parameters, as well as the calculations results are given in the following figures. Fig. 1. Standard calculation parameters as used in [3] Fig 2. Calculation results [3] 4

5 The main points to be noticed are: - In both processes, the entropy decrease induced by the actual separation of pollutants from water seem to almost have an insignificant in the indicator. - In opposition, side effects seem to be decisive: o In the flocculation case, the chemical entropy gain due to the alum flocs precipitation seems to play the principal role in the indicator. o The membrane filtration process is highly energy intensive. The high energy flow needed to sustain the transmembrane pressure dominates entirely the environmental impact indicator, which is then higher than in the flocculation case. The membrane filtration process with a higher environmental impact indicator is thus described as less sustainable than the flocculation process. These results being mentioned, the present work aims to apply the same methodology to a greater portion of the material cycle, including treatment waste management. Doing so, interesting issues including the robustness of the new indicator to larger system boundary settings (i.e the question whether and how the subjective act of setting boundaries to the assessed system affects the objectivity of the indicator) can be touched on. B. Methodology 1. Drinking water treatment processes As previously mentioned, the considered processes include the main separation processes, taken into account in [3], as well as a range of other necessary processes to polish the main treatment, or to manage and treat the waste generated by the settled flocs or the membrane backwash. The considered treatment processes for both the membrane filtration technology and the coagulation-flocculation technology are given in the following figures. Fig 3. Coagulation/Floculation (CF) process route 5

6 Fig 4. Membrane filtration (MF) process route 2. Mass conservation calculations The first step is to calculate the volume and solid concentration of the material flows, knowing the inlet flow and the principal assumptions linked to the processes and summarized in the following table. Inflow water (CF and MF) Inflow SS Al dose (CF only) Inflow volume Inflow dissolved C Flocculation-settling (CF) Decanted water suspended solids Flocculation sludge solid fraction Granular filter (CF) or membrane (MF) Filtered water suspended solids Filter recovery rate Granular filter (CF) or membrane backwash (MF) settling Decanted water suspended solids Settling sludge solid fraction 30mg/l 50mg/l as Al 1000ml 0mg/l 10mg/l 2wt% 0 mg/l 90vol% 20mg/l 2wt% Drying Beds (CF and MF) Evaporated water solid content 0mg/l Sludge solid fraction 30wt% Tab. 1: Mass balance assumptions A numerical (MSExcel) solver was used to solve the global mass conservation equations, where i is the material species (i.e solids, water or alum) and j the material flow. In 6

7 all the considered process steps except the CF drying bed, there is one material inflow and two outflows. N _ in flows ( C i *V H 2 O) = C j i *V H 2 O j=1 M _ out flows j=1 ( ) j For the MF route, two equations were considered for each process step, namely the suspended solid conservation equation, where C i is the suspended solid mass concentration of the flow, and the mass conservation of water, where C i actually represents the density of water and remains constant. In the case of the CF route, a third conservation equation was added, namely the conservation of Aluminium, where C i is the Al as Al mass concentration in the flow. A special care has to be taken to take into account the Al(OH) 2 floc in the suspended solid mass conservation equation. The Al as Al mass concentration must be computed to Al(OH) 2 mass concentration 1 which must be added to the original suspended solid mass concentration. The material flow results at the process end nodes are given in fig 3 and 4. A volume efficiency yield as defined by the ratio of treated drinking water to untreated inflow water can be defined. This yields to a 89% volume efficiency for the CF route and 90% for the MF route. 3. Energy calculation Similarly, the energy flow calculation of each process step was performed, based on the mass flow results (for the water volumes) and on the following assumptions and formulas: Membrane Transmembrane pressure (filtration and 1 atm backwash) Energy calculations E = VΔP Mechanical Mixing (CF) Fast mixing pulse Gf 800/s Slow missing pulse Gs 30/s Fast mixing HRT: HRTf 10s Slow mixing HRT: HRTs 900s Viscosity ν 10-6 Pas Energy calculation E = µ G 2 f HRT f + G 2 s HRT s Flocculation or Backwash Settling Settling basin level difference Solid density Total headloss Energy calculation ( ) 3m 3g/cm3 1m E = (Δh + h hdloss ) *V H 2 O * g* ρ sludge Granular filter Filtering media height Total filter height 1m 2m 1 By multiplying by the Al(OH) 2 /Al molecular weight ratio 78/27 7

8 Filtering media permeability 0.4 Granulates density 2.5 g/cm3 Filtering headloss 0.5m Backwash headloss Δh bwash.loss = h filt.med. (1 κ) ρ gran ρ h2 O Energy calculation E = (Δh + h hdloss ) *V H 2 O * g* ρ sludge Evaporation Latent evaporation heat of water 2272 J/g Energy calculation E = L *V * ρ H 2 O Surface water evacuation Level difference to natural water 0m Level difference to drinking water storage 0m Headloss to natural water 0.5m Headloss to drinking water storage 0.5m Tab. 2 Energy calculations assumptions The following figures are level profiles of the two different process routes, with indicated energy sinks. ρ h2 O Fig 5 Flocculation route level profile Fig 6 Membrane filtration route level profile 8

9 Remarks - Solar energy, necessary to evaporate water from the drying bed sludge will not be taken into account in the environmental impact indicator calculation. It is justified by the fact, that solar is a natural renewable energy that did not require any environmental resource to be transformed in order to be useable. Yet, on the other hand, one could argue, that the total drying bed area could be replaced by solar panels, which potential electricity production is thus loss by devoting the land to drying beds. The issue still is an open issue. - We also notice, that the choice of zero level difference between the treatment plant and the natural water or drinking water storage facility is arbitrary. It is easy to imagine cases, where the pumping cost or energy loss due to conveying the water from the plant to the consumption location is significant. 4. Entropy Calculation The next step is the calculation of the entropy difference due to the process. In the considered processes the entropy difference can either have chemical or physical origins. Chemical entropy If a chemical reaction occurs, the chemical entropy difference due to the formation of the new compound is to be taken into account. The only process where a chemical reaction occurs is during the mechanical mixing of the CF route. The considered reaction is given as following (per mole of Al): The same calculation process as in [3] is used. Soluble Al formations and other side reactions are neglected, and the considered reaction is considered to fully occur. The actual molar entropy is first calculated from the standard molar entropy of the species and their activity, according to the following assumptions and formulas: Al input dose (as Al) 50mg/l Buffer ph 6 Actual molar entropy The resulting actual molar entropies are given in the following table. The reaction molar entropy is then calculated by adding the product entropies and subtracting the reactants, all the terms being multiplied by their stoechionomotric factor. Finally, the total chemical entropy difference per inflow liter is obtained by multiplying the result by the molar concentration of the added Al (here, 50mg/l corresponds to mol/l Al). ( ) 3 H 2 O Al( OH) 3 H + 2 SO 4 Species Al 2 SO 4 Molar ΔS [J/(K*mol)] 9

10 Mass separation entropy The idea of basing material flow management decision on the entropy difference induced by the processes on the flowing material has already previously been applied [4]. The separation process efficiency was evaluated by their ability to decrease the statistical entropy of the flows. The same calculation technique as in [4] will here be applied. Given a process with N inflows j and M outflows j, with each flows containing P material species i, the resulting entropy difference per inflow liter for each specie i, due to its concentration change though the process is given by: Where M i is the molar mass of the species i, V j the volume of flow j, and C ij the concentration of species i in the flow j. Both data are obtained from the mass conservation calculations. If the considered species is H 2 O, which is also the solvent water density ρ is to be used instead of C ij Adding the P species present in the material flow, we have the total entropy difference per process step due to concentration change: Δ mix S process = P i Δ mix S i Total entropy difference The total entropy difference for the whole process route is given by summing up the entropy differences for the individual processes, without omitting the chemical entropy calculated above: Δ mix S TOTAL = ΔS chem + Qprocesses x Δ mix S processx Knowing that entropy is a state variable, the total entropy difference of the whole route calculated by solely considering initial and final material flow (a.k.a black box approach) is equal to the sum of the process-steps entropy difference as calculated above. Yet, the entropy difference of each single process step was still calculated to check the accuracy of the numerical calculation on one side, and to be able to breakdown and observe the entropy differences and the sustainability indicator per process step. C. Results Δ mix S i = R % M N ( V j C ij lnc ij V j C ij lnc ij M ' * i & j j ) 1. Global Results The following table gives the total results for each process route, in terms of entropy difference, energy flow and environmental impact indicator (omega). The considered ambient temperature is 298K. In order to take into account the mass efficiency of the process, all values are given per inflow liter and per produced drinking water liter. Taking into account the mass yield of the process route in the definition of the indicator makes sense if the processes are compared on the production/cost point of view, as opposed to the resource consumption point of view 2. 2 However, the philosophical distinction lying behind this doctrinal choice are beyond the scope of this work. 10

11 Membrane Floculation Unit Volume Yield (drinking water/inflow) Energy flow/inflow liter J/l Energy flow/treated liter J/l Entropy/inflow liter -1.76E E-02 J/l*K Entropy/treated liter -1.95E E-02 J/l*K OMEGA/inflow liter (J/L) J/l OMEGA/treated liter (J/L) J/l Tab 4. Total results We notice that although both processes have a positive damageable environmental impact in terms of thermodynamics, the CF route has a smaller omega value and is thus described as more sustainable by the indicator. 2. Process breakdown The next step is to break the results down by process step, in order observe each step s contribution to the total values. The process step breakdowns for energy flow, S*T and omega, for both process routes are displayed in the figures here under. We notice for the MF route, that the whole route is strongly dominated by the energy flow required to sustain the trans-membrane pressure. The resulting entropy decrease, however is highest in the backwash settling phase, which is very little energy consuming. We also notice, that in opposition the results in [3], the entropy decrease seem to play an overall significant role in omega. This point will be discussed further. The CF route seems to be dominated by the entropy increase due chemical entropy of formation of the flocs in the flocculation step. A surprisingly high entropy decrease is also to be noted for the drying bed, which is thus the overall most efficient step in terms of entropy decrease per energy flow unit. Yet, the observation of the total column shows is to still be dominated by the energy flow needed to operate the granular filter. MF route process step breakdown Joules/inflow liter Omega S*T Energy flow Membrane filtration Backwash settling Drying Bed (w/o solar energy) Total Fig 7. Results breakdown by process step, MF route 11

12 CF route process step breakdown Joules/inflow liter Omega S*T Energy flow Floculation/settling Gran filter Backwash settling Drying bed (w/o solar energy) Total Fig 8. Results breakdown by process step, CF route 3. Temperature Effect Finally, a sensitivity study on omega for temperature variations has been conducted, in order to observe the influence of this arbitrary environment parameter on the indicator. The temperature evolution of omega is given for both routes in the following chart. Omega evlolution with T Omega [J/L] MF CF T [ C] Fig 9. Temperature sensitivity study We notice, that in an operational temperature range (i.e. a temperature range that allows liquid water), the temperature does not have an effect on the sustainability comparison of the two process routes. Yet, the environmental impact indicator seems to decrease with the temperature, leaving open the issue whether these treatment processes conducted at an extremely high temperature would have a zero or negative (i.e. beneficial) environmental impact indicator 12

13 D. Discussion 1. Inflow water parameters As mentioned in the previous section, entropy seems to play a significant role in the environmental impact indicator for both routes. This fact contrasts with the results presented in [3], where entropy seemed almost insignificant, especially in the case of the MF route. This difference is linked to a basic assumption difference on the molecular weight of the suspended solids. I assumed single carbon atoms with a 12a.u molecular weight, while a a.u molecular weight polymer is assumed in [3]. The fact that a greater molecular weight leads to a smaller entropy decrease during the treatment process can be demonstrated through statistics. Yet, it leads to the fact, that the environmental impact of the treatment process seems to be strongly correlated to the characteristics (i.e. molecular weight) of the inflow water contaminant. This correlation is to substantially complicate the potential application of the indicator in real systems, where the timely variable and highly heterogeneous inflow water would require to be perfectly characterized. Moreover, it is also important to notice, that the inflow water as modeled in this work is highly idealized. The multiple chemical reactions, occurring in real inflow water, and the resulting entropy and enthalpy differences may have to be taken into account. Moreover, the entropy difference due to the solidification of dissolved matter during treatment may also need to be taken into account in real life applications of the indicator System definition An essential requirement to an efficient application of the indicator is to have a relevant system definition. Specifically, several parameters are of primary importance. The first important parameter that is explicitly appearing in the indicator definition, is the temperature. In the case of a decreasing entropy (i.e. separation process), the environmental impact indicator decreases with the temperature increase. If several processes are to be compared, it is thus important that they be assessed at the same temperature. Moreover, in the studied cases, the indicator sensitivity to temperature variations did not have an impact on the ordinal results of the comparison. Yet, if the entropy differences of the compared processes are significantly different, the temperature choice may have a significant impact on the comparison results. Secondly, the system boundaries are to be carefully set. As a matter of fact, we notice that the process stages of the various routes have various contributions to the total indicator. Namely, the most significant step in the MF route is the membrane filtration step itself, whereas the drying bed step seems to have the biggest impact on the CF route. An indicator calculation solely focusing on the principal process steps as performed in [3] would have totally left a side this important contribution. The choice of the process steps to take or not into account in the indicator calculation can thus have a capital effect on the indicator value. A tradeoff must be met, between taking enough steps to include all the significant ones, and limiting the system s extent to limit the data assessment complexity. In the event of a widespread use of the indicator, perhaps a standard method or a database system can be developed to identify à priori the significant process steps to be taken into account. 3 This issue is discussed in [4] 13

14 Finally, the fact that the total environmental impact indicator is an extensive variable and thus depends on the plant capacity and volume yield, may be an issue if two technologies are to be compared. A path to render the indicator intensive would be to define a reference frame, whereby the minimal energy needed to decrease the mixing entropy of one liter inflow water could be defined for given parameters (such as a targeted concentration difference and a given temperature). The intensive environmental impact indicator could then be defined as the ratio of the actual calculated omega, to the perfect idealistic omega for the given inflow parameters. The existence of a perfect omega has not yet been proven, but a similarity could be expected with the Carnot cycle for thermal processes. E. Conclusion In this paper, a physical sustainability indicator based on the energy flow and entropy difference has been applied to compare two drinking water treatment process routes. The main points observed are that the application of the indicator in real life comparisons requires an accurate and detailed characterization of the inflow water, as well as cleverly chosen system boundaries and temperature. At the closing of this work, several issues are left open for future research. As already mentioned in the discussion, the definition of an optimal omega reference, or of the inclusion of solar energy flux in the calculations are interesting paths. Furthermore, the necessity to take into account the toxicity of the waste will also arise to be a necessary milestone in the path of developing an efficient and complete environmental impact indicator for real life water treatment technology decisions. F. Sources [1] Hermanowicz S. Sustainable Development: Physical and Moral Issues, Water Resource Center Archieves, UC Berkeley, 2006 [2] World Commission on Environmental Development. Our Common Future, University Press, Oxford [3] Hermanowicz S. Entropy and Energy, Towards a Definition of Physical Sustainability, Water Resource Center Archieves, UC Berkeley, 2005 [4] Rechberger H, Brunner P. A New, Entropy Based Method to Support Waste and Resource Management Decisions, Environ Sci Technol,