Effect of coagulation conditions on the dewatering properties of sludges produced in drinking water treatment

Colloids and Surfaces A: Physicochem. Eng. Aspects 348 (2009) 14–23

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Effect of coagulation conditions on the dewatering properties of sludges produced in drinking water treatment David I. Verrelli, David R. Dixon, Peter J. Scales ∗ Particulate Fluids Processing Centre, Department of Chemical and Biomolecular Engineering, The University of Melbourne, Victoria 3010, Australia

a r t i c l e

i n f o

Article history: Received 11 March 2009 Received in revised form 12 June 2009 Accepted 12 June 2009 Available online 21 June 2009 Keywords: Aluminium sulfate Coagulation Dewatering Ferric chloride Sludge Water treatment

a b s t r a c t The coagulation and flocculation processes in conventional drinking water treatment generate aggregates which settle to form a sludge waste. This sludge can be dewatered further by thickening, centrifugation and filtration operations in order to recover water and minimise the volume of the waste stream. A range of water treatment sludges generated in the laboratory were characterised according to a phenomenological method that is valid from the dilute free-settling regime to the concentrated cake compression stages. These were compared with plant samples. Experimental results show that raw water natural organic matter (NOM), coagulant dose and coagulation pH affected both the rate and potential extent of dewatering. Similar effects were observed for both aluminium sulfate and ferric chloride. These results suggest that increasing dose or pH leads to an increase in the proportion of rapidly precipitated material in the sludge or flocs, which form looser aggregates and hence exhibit inferior dewatering properties. © 2009 Elsevier B.V. All rights reserved.

1. Introduction The production of potable water is conventionally carried out by coagulation with a hydrolysing metal salt such as aluminium sulfate (‘alum’) or ferric chloride (‘ferric’). This process is effective at removing turbidity, colour, and micro-organisms, but also results in a waste by-product as the coagulant precipitates into particles that aggregate to form ‘flocs’. Settling of these flocs results in a sludge that can be thickened, centrifuged or filtered prior to ultimate disposal. These dewatering procedures reduce the volume of the waste stream, with both environmental and financial benefits. Separation of the solid aggregates from the water is influenced by numerous factors, including structural configuration and density differences between the solid and liquid, depending upon the dewatering mechanism employed. However, from an operational perspective, it is useful to gain an understanding of how such practical parameters as coagulant dose and coagulation pH affect dewatering performance, or ‘dewaterability’. A phenomenological theory developed by Landman, White and others provides a rigorous approach to modelling the dewatering behaviour of compressible materials, and has been adopted to model various dewatering unit operations [1–3]. In this approach the dewaterability is characterised in terms of an equilibrium term, the compressive yield stress, py , and a kinetic term, the hindered

∗ Corresponding author. Tel.: +61 3 8344 6480; fax: +61 3 8344 4153. E-mail address: [email protected] (P.J. Scales). 0927-7757/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.colsurfa.2009.06.013

settling function, R, which both depend only on the volume fraction of solids, , for a given material. The compressive yield stress can be defined as the maximum stress that a given material is able to withstand without undergoing consolidation. Formally the hindered settling function is obtained from a force balance as a factor accounting for the enhancement of hydrodynamic retardation beyond that expected for a single particle in an infinite medium [1,4]. R can also be expressed in terms of related hydrodynamic parameters [5], such as the Darcian permeability, KD , 2

R=

(1 − ) L , KD 

in which L is the fluid viscosity. Knowledge of these parameters allows prediction and optimisation of dewatering operations; conversely, monitoring dewatering performance allows the computation of these parameters. This method is valid for any suspension or particulate cake, and its application to water treatment plant (WTP) sludges was introduced by Aziz and co-workers [6–8]. This early work reported the solids diffusivity, D(), which is a summary parameter combining some information from both the py () and R() curves, useful for quick comparisons. However, D() does not capture all of the information [9]: some unit operations are permeability limited, while others are limited more by the equilibrium dewaterability, and so the ability to separately assess the two constraints R() and py () is advantageous. Additionally the computation of D() is less robust, normally depending upon numerical differentiation of either py or the permeate flux rate with respect to .

D.I. Verrelli et al. / Colloids and Surfaces A: Physicochem. Eng. Aspects 348 (2009) 14–23

In order to obtain , the density of the solid phase, S , must generally be used. It is important to use an accurate value for S , to ensure the py () and R() curves are not inappropriately shifted. A recent extensive survey of the literature in this area [10] shows that a reliable single value for S is not available. Nominal values of 2000–3000 kg/m3 have been quoted for alum sludges and 1400–3400 kg/m3 for ferric sludges. Published measurements show almost as much variation, with estimates of 1900–2800 kg/m3 for alum sludges and 1900–2860 kg/m3 for ferric sludges. The wide ranges are indicative of substantial uncertainty, and could yield large errors if applied to the present materials, which is why we chose to make our own measurements. On a fundamental level the sludge dewaterability is controlled by the chemical composition and physical configuration of the aggregates or flocs that make up the sludge. It is hypothesised that the coagulation conditions will determine these parameters and hence determine sludge dewaterability. Important variables in the coagulation process are the coagulant dose, coagulation pH and the raw water quality – for example, how much natural organic and inorganic material it carries. These variables will dictate the composition of the sludge, for example the proportion of natural organic matter (NOM) and the phase of coagulant precipitate formed. The proportional composition of the aggregates in the sludge was demonstrated to affect dewatering behaviour in an earlier study of synthetic sludge [11] and will be quantified in more detail here. It is anticipated that the size and physical structure of the aggregates will also be affected by the coagulation conditions. A given mass of aggregated primary particles could take on a ‘streamlined’, compact form, which would provide little resistance to either settling or permeation (the same physical process, with different frames of reference) and allow efficient packing. Alternatively, a porous structure with the same mass would have a greater resistance, and would achieve a lower equilibrium solid volume fraction even when close packed, unless the structure were crushed. Much published research in the area of drinking water coagulation and aggregate formation uses the fractal dimension to characterise aggregate structure [12] and it is often interpreted as a measure of aggregate compactness. For real aggregates there are numerous limitations to the application of fractal theory [10]. Nevertheless, estimates of the fractal dimension provide an indication of structure from which inferences may be drawn regarding behaviour in settling, up to the gel point, g , at which concentration the particles form a space-filling network. Inferences about behaviour at higher concentrations, such as in filtration, are less justified due to the extreme structural changes, which progressively destroy the fractal particle arrangements. A common application of fractal theory distinguishes the result of rapid (diffusion-limited) aggregation and slow (reaction-limited) aggregation [13–15]. The latter is known to generally favour the formation of more compact aggregate structures (of higher fractal dimension). Rapid reaction may be expected when the concentration of particles is high, which may be from high turbidity, but more likely from a large amount of precipitate generated in ‘sweep’ or enhanced coagulation from the dosed chemicals. Clearly, a larger amount of precipitate will be generated from a larger dose. However, we also expect that pH will influence the rate. The overall solubility of the metal is pH-dependent. In theory a decrease in solubility will lead to an increase in the ‘driving force’ for precipitation. In practice, however, the coagulant doses tend to be at least an order of magnitude greater than the solubility, so that this effect is negligible. Thus the most important pH effect is purely based on the concentration of OH− ions that drives the progressive hydrolysis of Al3+ and Fe3+ . The rate of precipitation and coagulation of ferric salts has been related to the molar hydrolysis

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ratio, [OH− ]added /[Fe], and the equivalent for Al [16–21]. It has been reported that there exists a threshold value of [OH− ]added /[Fe] in the range 2.7–2.8, above which rapid polymerisation occurs, yielding a poorly ordered precipitate [19,20]. The hydrolysis ratio is less commonly used for Al, but an indicative threshold for the onset of rapid precipitation and coagulation of about 2.6–2.8 seems reasonable [16,22,23], similar to the value for Fe. The prevailing pH also affects the surface charge of existing species, such as NOM, thereby influencing the interaction of these species with the precipitate (or ions). A pioneering systematic investigation by Knocke et al. [24] found that the dewaterability of both alum and ferric sludges improved as the coagulation pH decreased from ∼8 to ∼6 – across settling and filtration, in both rate and extent. Larger aggregates formed at high pH, but these exhibited poorer dewatering because of their lower density (which dominated any enhancement due to size) [24]. Tambo and Watanabe [25] showed that decreasing the pH from 8.0 to 6.5 led to a decrease in fractal dimension at low alum doses, but had little effect at higher alum doses. This is consistent with the solubility of aluminium, which is minimum at about pH 6 [26]; however the difference in solubilities would only be important for low total Al concentrations. Bottero et al. [17] reported increases in aggregate fractal dimension as the hydrolysis of aluminium (from aluminium chloride) was increased (i.e. increasing neutralisation) for a given pH, as well as for decreases in pH from 7.5 to 4.5 with a given hydrolysis ratio. For turbid raw waters the sludge properties can also be affected by the proportion of precipitated coagulant to naturally present particles; arguably because the natural suspended solids are larger, and so denser, sludge produced from turbid water at low coagulant doses is widely reported to dewater faster and further [27–31]. (The ‘primary particles’ formed by precipitating coagulant may be as small as ∼2 nm [17–19,32].) Tambo and Watanabe [25] measured a decrease in aggregate density as alum dose was increased in their coagulation of kaolinite clay suspensions. This is also consistent with the trend found by Dixon et al. [11] for py () to increase with greater proportions of alum in the sludge. Some researchers have observed a maximum in aggregate strength at intermediate coagulant doses [33–36], perhaps coinciding with a maximum in the bulk density of the sludge [37]. Strength was also observed to decrease with increasing pH [33]. Most of the literature comparing alum and iron sludges reports that the ferric sludges dewater further [29,38–40] and faster [30,41,42] than alum sludges [43]. Yet the observations are often not quantified. Aggregates generated by coagulation of kaolinite clay from both alum and ferric “at the optimum coagulation conditions” did not show any significant differences in fractal dimension [25]. It seems that aggregates formed in turbid waters may have a structure similar to that formed by the precipitation of coagulant in pure water [17]. Natural fluctuations in raw water quality can cause large changes in the consistency of WTP sludge produced [40,44,45] through changes in the size, morphology, and strength of the underlying aggregate or floc structure [46]. However there is disagreement in the literature over whether NOM is beneficial to sludge quality [47,48], detrimental [11,39,44,49–53], or has no significant effect [54]. This discrepancy can be attributed to differences and limitations in the characterisation techniques employed, as well as differences in sludge concentrations and inherent dewatering properties. Though not conclusive, the data of Tambo and Watanabe [25] suggest a decrease in aggregate density with increasing proportion of coloured organics relative to mineral (clay) content in the raw water. This is consistent with the trend found by Dixon et al. [11] for ‘model’ alum sludges, in which py () increased in the presence of humic substances.

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In contrast, a detailed investigation by Bottero et al. [17] indicated that aggregate density increased with increasing organic content, especially in the case of strongly chelating or bridging species, which was attributed to competition with OH− ions for hydrolysis sites, which would otherwise form hydroxo bonds. Also, the presence of NOM has been reported to significantly increase the fractal dimension of aggregates formed from ferric chloride [47]. In conditions typical of conventional water treatment, the fractal dimension was estimated at 2.4 (pH 5.5) and 2.3 (pH 7.5) for a turbid, low TOC river water, and 2.9 (pH 5.5) and ∼2.1 (pH 7.5) for a non-turbid lake water of moderate TOC [47]. The decrease in fractal dimension at higher pH was attributed to adoption of less dense conformations by the NOM due to increased electrostatic repulsion arising from enhanced deprotonation [47]. This work seeks to better understand the relationship between clarification conditions and the dewaterability of the resulting sludge by focussing on exercising careful control over the sludge generation and handling procedures. Particular attention has been paid to obtain characterisation of both equilibrium and dynamic parameters across the full range of solid volume fraction of interest. The objective is the first quantitative characterisation of WTP sludge dewaterability across a broad range of systematically varied treatment conditions following from the analysis of experimental measurements according to a rigorous phenomenological theory. 2. Materials and methods Sludge was prepared in the laboratory by addition of AR grade chemicals to 48 L batches of raw water obtained from a reservoir supplying Melbourne, Australia. Aluminium sulfate was added at 1 g(Al)/L, or 10 g(Al)/L for the highest doses. Ferric chloride was added at 2 g(Fe)/L, or 20 g(Fe)/L for the highest doses. Sodium hydroxide was added at 1.0 M, or 0.2 M for the lowest doses. These stock solutions were made up with deionised, RO water (Elix 3, Millipore), which was also used as a pure water blank. Typically the stock solutions used for sludge generation were used after 1–7 days. The doses were of order 0.1–0.5 L. It is highly unlikely that significant precipitate formation occurred within the stock solutions, considering the extremely slow rate of formation even at a hydrolysis ratio of 0.35 (e.g. years) [55] and that no added base or natural alkalinity was present [16]. The stock solutions remained clear and no influence of their concentration was observed in the results. Some batches of raw water were spiked to 1.25% with highlycoloured eluate from a magnetic ion exchange (MIEX) water treatment process. This was first dialysed to reduce its salt content (measured as conductivity) to about 70 mS/m. One sample, at 84 mg(Al)/L and pH 6.1, was prepared from tapwater. Indicative properties of the source waters are presented in Table 1. The mixing tank was constructed in the “standard configuration” of Holland and Chapman [56], enabling the characteristic velocity gradient, G, to be estimated [57,58]. The standard addition sequence is described in Table 2. The chemical doses were determined in advance using a 6beaker ‘jar testing’ rig, with 1.6 L volumes of raw water. Mixing intensities on this rig were scaled to hold G constant [59]. Table 2 shows that the mixing was always turbulent. The small-scale jar testing also enabled the optimum dose to be found for a given pH, yielding satisfactory supernatant clarity, and beyond which addi-

Table 2 Standard dosing and mixing sequence. Time (min:s)

Chemical added

Velocity gradient, G (1/s)

Reynolds number, Re (−)

<0:00 0:00 1:00 2:00 >22:00

– Al2 (SO4 )3 or FeCl3 NaOH – –

254 254 254 13 ∼0

41000 41000 41000 6000 ∼0

tional true colour removal was negligible. It is recognised that a stoichiometric approach to coagulant dose selection, employing a titrimetric technique, can be used to establish very low doses in the charge neutralisation regime, but the prevailing trend in water treatment is for larger coagulant doses to be used, especially where removal of both colour and turbidity is important. The aggregates generated were allowed to settle to form a watery sludge. This sludge was decanted to a smaller vessel, and again allowed to settle; if necessary, this was repeated once or twice more. For the lowest doses, up to 11 batches (532 L) were combined in order to collect sufficient settled sludge (minimum 500 mL, approximately 5 g of dry solids) for characterisation. In addition to the laboratory sludges, some alum sludges were obtained from a pilot plant treating water from a reservoir near Adelaide, Australia. These sludges all had polymer flocculant added. Finally, a number of samples of both alum and ferric sludges were obtained from full-scale plants. The alum sludge was collected from the same site as the raw water used in this work. The ferric sludge was sourced from a treatment plant serving Sydney, Australia. All of the plant sludges had polymer flocculant added. The dewatering behaviour was characterised in terms of equilibrium properties (py ()) and dynamic properties (R()) in two distinct ranges of solid volume fraction, . Characterisation of each sample at low values of  was carried out through batch settling under gravity until the system attains steady-state (negligible movement after one week or more), and analysing the fall of the interface, h, as a function of time, t, [60]. In summary, py () is obtained by solving the static equation balancing py at each elevation with the superimposed buoyant self-weight of the solids. The analysis is completed by implementing a mathematically analytic inverse transform of the h(t) data to obtain R(), which is valid for a well-defined domain of  below the gel point. This produced a large array of data points for each parameter – only the terminal points are plotted as distinct points in the results that follow. For the py () curves one point is plotted at the arbitrary low stress of 10−5 kPa, where  ≈ g , and the other is plotted at py = 0 h0 S g (product of initial solid volume fraction, 0 , initial sample height, h0 , buoyant solid phase density, S , and gravity, g), corresponding to the equilibrium stress at the base of the settling cylinder. For the R() curves the first terminal point is determined by 0 , the initial solids concentration, and the upper point is based on the estimate of g . Characterisation of the samples at higher values of  was carried out using a dead-end filtration rig with stepwise incrementing of the stress in two separate runs according to the method described by de Kretser and co-workers [4,61]. Extraction of py () is straightforward, following from direct measurement of the applied load and the equilibrium solid volume fraction. The hindered settling

Table 1 Indicative source water characteristics. Source water

True colour (mg/L Pt units)

Turbidity (NTU)

Absorbance at 254 nm (10/m)

Dissolved organic carbon (mg(C)/L)

Raw water Spiked water Tap water

7–19 (average ∼10) 39 1

1–2 1.3 0.2

0.6–0.9 (average ∼0.7) 2.5 0.2

∼2 10 –

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function is obtained by considering the initial cake formation rate for various applied loads and calibrating according to the solid volume fraction. In order to yield a sufficiently thick cake after compressing at the highest stress, samples were taken in general from the lower portion of the settled material, with no supernatant. Occasionally it was necessary to centrifuge the samples to thicken them prior to filtration; in such cases accelerations up to 540 g m/s2 were applied for a period of up to 3 h (duplicate runs indicated that this process did not compromise results). For one sample, py () at intermediate  was obtained by a centrifugation technique wherein the sludge was spun to an equilibrium height at a nominal acceleration of 192 g m/s2 (at the base of the sample-holder), followed by dissection of the cake according to an established method [62,63]. Solid phase densities were obtained through pycnometric measurement (25 mL density bottle) and a calibrated 4-decimal place analytical balance. Samples were oven dried at 100 ◦ C. One of the greatest limitations on accuracy was the solid volume fraction at which the samples were assessed: larger  implies increased accuracy. Therefore samples were centrifuged in 50 plastic tubes at up to 190 g m/s2 for periods of 1 h and the supernatant syringed off. If necessary, the thickened sludge from multiple tubes was combined and the process repeated. This was then liquefied by agitation. As with , contributions due to dissolved solids were excluded in the analysis. 3. Results and discussion 3.1. Sludge composition and solid phase density As noted, knowledge of the solid phase density is necessary to accurately compute the solid volume fraction, which is required to ensure a valid basis for comparison of dewatering properties. The sludge is made up of precipitated coagulant, in the form of a metal oxide or hydroxide, and material removed from the raw water. Material removed from the raw water is mostly either suspended particles such as clay or sand, or dissolved natural organic matter – other matter such as protozoa or activated carbon either contribute little mass or are not present at most WTP’s. Typical inorganic components have a density of around 2600 kg/m3 [64]. The solid phase density of the NOM is uncertain, although the upper limit must be approximately 1700 kg/m3 [65–67]. The density of precipitated coagulant is difficult to predict, because amorphous or paracrystalline phases tend to form in preference to the better-known crystalline phases. Powder X-ray diffraction measurements indicated our alum sludges to be composed of pseudoboehmite (or ‘poorly crystalline boehmite’), while the ferric sludges comprised predominantly 2-line ferrihydrite, with goethite detected in some samples. The samples were air-dried in a desiccator over silica gel. Fig. 1 shows sludge solid phase density, S , as a function of coagulant dose, along with two model curves (see below) representing the ‘average’ for each case. A diverse range of sludges were characterised: laboratory alum sludges of pH 6.0–7.9; plant and pilot-plant alum sludges of pH 6.1; laboratory ferric sludges of pH 5.6–9.0; and a plant ferric sludge of pH 8.7. The solid phase density, S , was found to vary according to the type of coagulant added, and the dose. When very high doses of coagulant were added, of the order 80 mg/L as the metal, the precipitated coagulant dominated S ; conversely, at low doses the material removed from the raw water strongly influenced S . The alum sludge data shows an average density of approximately 2500 kg/m3 , irrespective of source, dose or pH, with scatter in the data of ±300 kg/m3 . A large number of measurements were made on the pH 7.9 laboratory alum sludge, however these tended to be at lower volume fractions of solids, and so less accurate, reflected in greater scatter and wider confidence intervals. For the ferric sludges

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Fig. 1. Variation in solid phase density of WTP sludges according to coagulant type, dose, and coagulation pH. Error bars represent estimates of the 95% confidence limits calculated on individual points.

S increases strongly with dose, from about 2700 kg/m3 to about 4000 kg/m3 . For the high-dose ferric sludges S appears to exhibit a dependence upon pH, with higher pH yielding lower densities. The model curves were obtained by computing overall densities based on: • the mass and density of material removed from the raw water; • the mass and density of precipitated coagulant as a function of dose. The simplifying assumption was made that the mass of suspended and dissolved solids removed from the raw water is fixed at 15 mg/L, independent of dose. It was assumed that both of the model curves should extend back to the same ‘average’ S intercept, and a value of 2500 kg/m3 gives a reasonable fit. Also, S should asymptote to the density of pure coagulant precipitate at high doses; the model assumes that the precipitate phase is not affected by coagulant dose. This high-dose asymptote was taken as 2500 kg/m3 for the alum sludges, and 4260 kg/m3 for the ferric sludges. Experimental results indicated that coagulation with alum yielded a mass of sludge 4.0 times greater than the dose as Al, slightly more than a previous report of 3.73 obtained by assuming complete precipitation as a hypothetical Al(OH)3 ·1.25H2 O species [28]. For coagulation with ferric, a mass of sludge 2.0 times greater than the dose as Fe was obtained, slightly less than the value of 2.3 obtained making the same assumptions as for alum [68]. The most detailed previous study of sludge density was made by Hossain and Bache [36]. These researchers reported S estimates from 2400 to 2600 kg/m3 , consistent with the present work. A possible trend with pH was revealed (in the absence of NOM and turbidity elements), with maximum S observed at the intermediate pH of 5.5. Hossain and Bache [36] also estimated the average density of the suspended, colloidal and dissolved material removed from a coloured, low-turbidity raw water as being in the range 1540–1750 kg/m3 . Discrepancies between those estimates and the present work could be due to artefacts caused by temperature or dissolved solids effects. Alternatively, the discrepancies could be determined by differences in the nature of the respective raw water constituents.

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Fig. 2. Compressive yield stress for several alum sludges, differing in coagulation dose and pH. The smaller circles at intermediate values were obtained from centrifugation.

3.2. Alum sludges Knowing S , it was possible to correctly analyse the settling and filtration data obtained for a number of alum sludges where the dose and pH were varied. 3.2.1. Dose effects Fig. 2 shows a plot of the compressive yield stress, py , as a function of solid volume fraction, , for a number of laboratory and plant alum sludge samples. Additional samples were characterised by Verrelli [10]. It is observed that py () deteriorates as the coagulant dose increases, meaning that the maximum potential volume fraction of solids at a given applied stress (or bed height) decreases. Equivalently, the stress required to yield a given equilibrium solid volume fraction is increased. In filtration the behaviour of all alum sludge produced at a given pH was practically the same for doses of 10 mg(Al)/L or more, suggesting that at these doses the precipitated coagulant is dominating the filtration behaviour. The high- and low- portions of the curve are connected by the model interpolation in the case of the compressive yield stress. For this parameter the two enclosing data sets are strongly suggestive of the location of the intermediate data. Furthermore, experimental results in this region of intermediate  have validated the interpolation to acceptable accuracy. An example of such validation is shown in Fig. 2, where data obtained from two separate centrifugation experiments (by dissection of the final cake) on one sludge have been included, and nearly coincide with the interpolation obtained using only settling and filtration data. Fig. 3 shows the hindered settling function for a selection of alum sludges, produced under different coagulation conditions. These are the same samples as for Fig. 2. The data indicate the existence of a dramatic change in slope in the region of intermediate , as was generally observed for R() data in the present study. Results presented here, along with other modelling work, suggest that the curvature is very sharp. As the precise location of this ‘elbow’ is uncertain, no interpolation is shown. Despite this uncertainty, it is important not to rashly identify a ‘discontinuity’ in the intermediate region: this impression is especially exaggerated by the use of log–log plots (necessary here to show numerical detail) such as

Fig. 3. Hindered settling function for several alum sludges, differing in coagulation dose and pH (log–log).

Fig. 3, which by their nature are less amenable to ‘intuitive’ interpolation. This is illustrated in the comparison of Fig. 3 with Fig. 4, which presents the data on log–linear co-ordinates. R() exhibits a similar trend to py (), as shown in Fig. 3, although the detail is slightly more complicated. Looking first at the filtration data, samples with doses ≥5 mg(Al)/L behave similarly, again suggesting that the precipitated coagulant is dominating, with R reducing as dose decreases below 5 mg(Al)/L. Hence the resistance to dewatering is reduced as the alum dose is decreased below a certain threshold, meaning the filtration will proceed more rapidly. The settling data show the same trend, although it is less pronounced. These results are consistent with previous experimental dewatering findings [11], as well as the finding that aggregates are less compact (lower fractal dimension) at high coagulant doses [25].

Fig. 4. Hindered settling function as a function of solid volume fraction (log–linear), for two alum sludges.

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Table 3 Hydrolysis ratios for laboratory alum sludges at varying coagulation pH and dose. Dose (mg(Al)/L) pH (−) Hydrolysis ratio (−) a

80 8.6 3.15

84 6.1 2.76

80 4.8 2.47

5.0 6.0 2.25 (2.03)a

1.5 6.0 0.74

The second value is for raw water spiked with additional NOM.

3.2.2. pH effects The effect of varying the coagulation pH has been studied in greatest detail at the limit of very high coagulant doses, where the precipitated coagulant will dominate dewatering behaviour. The trend in both py () and R() (Figs. 2 and 3) shows a significant deterioration in dewaterability as the coagulation pH is increased above pH ∼ 6. This is true across the entire span of solid volume fractions observed. The system pH has three key influences on the coagulation process: • it will alter the overall solubility of the metal; • it will alter the speed of the hydrolysis reaction; • it may also influence the precipitate phase favoured to form. From published plots of the solubility of aluminium [26,69,70], it is seen that solubility is minimum at about pH 6. An increase in solubility would thus be associated with an increase in pH above 6, suggesting a lower ‘driving force’ for precipitation, leading to a more compact floc. A sludge composed of such flocs would exhibit greater dewaterability. Clearly this mechanism is not controlling the behaviour here, which is reasonable, given that the differences in solubility are small in comparison with coagulant doses of 80 mg(Al)/L. The hydrolysis ratios of the four laboratory samples are presented in Table 3. These represent average values for the homogenised mixture; instantaneous values may be higher locally. The data indicate that the two high-dose samples generated under acidic conditions have hydrolysis ratios close to the probable threshold value, while the sample generated under alkaline conditions has a significantly higher hydrolysis ratio. This is consistent with the slow formation of compact, readily dewaterable aggregates at low pH, and the increasingly rapid formation of loose, open aggregates at higher pH that are more difficult to dewater. Note that due to the natural pH and buffering capacity of the raw water studied here, the nominal hydrolysis ratio actually decreases sharply at the lowest coagulant doses (for both Al and Fe). This suggests two synergic mechanisms arise as the coagulant dose is reduced that both work to reduce the reaction rate, leading to more compact structures (higher fractal dimension), and hence improved dewaterability. Of course, this pre-supposes that the rate of reaction is sufficient to generate aggregates of reasonable size in the available time—if the rate were so slow that minimal precipitation and aggregation occurred, then of course the kinetics of dewatering would suffer.

Fig. 5. Compressive yield stress for several ferric sludges, differing in coagulation dose and pH.

more pronounced. Still, the wide and low solubility curve for ferric iron [71] suggests that even at pH values of 5.6 and 9.0 the supersaturation effects may not be significant for doses >1.0 mg(Fe)/L. 3.3.1. Dose effects The compressive yield stress shows a clear deterioration as the dose is increased from 5 to 80 mg(Fe)/L. The plant sludges show a very high degree of variation, which is unlikely to be due to the coagulation pH, and which does not follow the proposed coagulant dose trend. It is likely that a significant part of the variation in py () for the plant sludges is due to changes in the raw water quality: for example the sample with the lowest py () curve had a ferric

3.3. Ferric sludges Data was also collected to identify the trend in dewatering behaviour of ferric sludges as a function of coagulant dose and pH (see Figs. 5 and 6). Two laboratory samples were produced at a coagulation pH of 5.6 and one at a pH of 7.3. The two plant curves plotted for comparison were obtained at much higher pH, approximately 9—no other ferric plant samples were available. It is not immediately clear how sensitive to pH the sludge dewaterability would be at the low plant doses, given that on the one hand precipitated coagulant would comprise a smaller proportion of the sludge but on the other hand any solubility effects would be

Fig. 6. Hindered settling function for several ferric sludges, differing in coagulation dose and pH.

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Table 4 Hydrolysis ratios for laboratory ferric sludges at varying coagulation pH and dose. Dose (mg(Fe)/L) pH (−) Hydrolysis ratio (−)

80 7.3 2.96

80 5.6 2.78

5 5.6 0.83

dose 46% higher than the other plant sludge, but the raw water colour was also 50% higher, and likely comprised NOM of a different character (spring versus autumn). From Fig. 6 the trend of decreasing resistance with decreasing dose is seen again for the high-solids data and most of the low-solids data. Only at the lowest solid volume fractions is there deviation from this trend. Variations in ferric sludge dewatering behaviour were not observed by Harbour et al. [8]. A good part of the variation depends upon the variation in S with ferric dose. The previous work included a somewhat narrower range of coagulant doses (2.4–30 mg(Fe)/L), and used a different value of S . It also was unable to isolate coagulant effects from flocculant and raw water quality effects. Although the plant sludges are not the focus here, it is observed that despite different settling behaviour and different py () in the filtration region, the R() curves at high volume fractions of solids overlay almost perfectly. Whatever structural features were differentiating the resistance at lower solid volume fractions appear to have been strained out of existence as higher stresses are applied. 3.3.2. pH effects Use of ferric salt coagulants is favoured in some WTP’s due to their reputation as being more robust operationally, and specifically being insensitive to the coagulation pH. This is also consistent with the much wider precipitation envelope bounded by the overall Fe solubility curves presented in the literature [71] compared to that of Al. The results presented in Figs. 5 and 6 show that the coagulation pH did not cause any significant change in dewatering behaviour. The slight deterioration in settleability of the low-pH sludge is not immediately consistent with a decrease in the reaction rate (due to a lower hydrolysis ratio), which would suggest the formation of more compact aggregates. Examination of Table 4 shows that for coagulant doses of 80 mg(Fe)/L the magnitude of the hydrolysis ratios for pH 5.6 and 7.3 are similar, and both above the ‘threshold’ value, so it is not surprising that their behaviour in dewatering is similar. On the other hand, at the much lower dose of 5 mg(Fe)/L the hydrolysis ratio is well below the threshold value for rapid coagulation, and the sludge produced from this process exhibited superior compressibility. It should be noted that a solid phase density of 4000 kg/m3 was used for all of the high-dose ferric sludges here, despite a suggestion that low pH may be associated with higher S . If S for the pH 5.6 ferric sludge had been underestimated in the computations, then it follows that  would be overestimated, so that that the true py () and R() curves would both be shifted to the left—i.e. poorer dewatering, enhancing the marginal tendency exhibited in the present graphs.

Fig. 7. Comparison of compressive yield stress for alum and ferric sludges.

coagulation conditions, and the more accurate values used for S . The key feature distinguishing the dewatering characteristics of alum and ferric sludges is the larger degree of variation observed for the alum sludges generated for the present work, which is consistent with both anecdotal industrial experience and the solubility properties of the two metals. Still, when more extreme doses or pH values are considered, as for the plant sludges, the variation is substantial for both coagulant types. 3.5. Variation of source water organic content The organic content of some raw waters was artificially increased to investigate the relationship between true colour, DOC, or ultraviolet absorbance of the raw water and the dewaterability

3.4. Influence of coagulant type Overlaying the data for the selected samples presented above allows a comparison of the dewatering behaviour of alum sludges with that of ferric sludges (Figs. 7 and 8). Of course, this does not cover the complete range of possible coagulation conditions. It is observed that the py () and R() curves for a range of representative alum and ferric sludge samples substantially overlay one another. This result is at odds with the conclusions of earlier work [8], and is attributed to the greater degree of control exercised here over the

Fig. 8. Comparison of hindered settling function for alum and ferric sludges.

D.I. Verrelli et al. / Colloids and Surfaces A: Physicochem. Eng. Aspects 348 (2009) 14–23

Fig. 9. Compressive yield stress for laboratory alum sludges generated from raw water spiked with NOM (dMIEX). The horizontal bars indicate deviation from nonspiked results.

of the generated sludge. Results are presented in Figs. 9 and 10. In order to improve clarity, only the fitted curves are shown for each parameter. To these are added horizontal bars, which indicate the ‘displacement’ from a corresponding control sludge. For py () all curves have the same range, and so the bars are drawn where the symbols are usually plotted. For R() the experimental data cover different ranges, and so the bars are drawn at the ends of each overlap. The control sludges are the obvious companions in Figs. 2 and 3, except for the low-dose material, which is compared with a non-spiked alum sludge of identical dose and pH. These new sludges show some of the trends identified earlier. The sludge at pH 8.7 shows the worst dewatering with the high-

Fig. 10. Hindered settling function for laboratory alum sludges generated from raw water spiked with NOM (dMIEX). The horizontal bars indicate deviation from nonspiked results.

21

est py () and about equal highest R(). The sludge at 5 mg(Al)/L exhibits the lowest py (), although its resistance to dewatering is not the lowest in the high- range. Comparing the spiked sludge behaviour with ‘control’ sludges should indicate the influence of raw water colour on sludge formation and dewatering. Given the previous finding that low coagulant doses resulted in a higher proportion of NOM (and turbidity) in the sludge, which was beneficial to dewatering, it may be expected that spiking the raw water with additional organic material would have a similar effect to a decrease in coagulant dose. Given that the additional NOM is of order 10 mg(C)/L, this would be expected to have a greater influence on behaviour at the lower alum dose of 5 mg(Al)/L. No consistent trend attributable to the spiking can be found that is valid across all sludges and solid volume fractions. The most consistent trend is present in the high- data: here the spiked sludges possess superior dewatering qualities in every case excepting R() for the high-dose sludges at pH 8.7 and ∼6 (the shift in the latter case being negligible). At lower solid volume fractions more relevant to unit operations such as centrifugation, thickening, or clarification, the effects are mixed. For py () there is not much difference in behaviour between pairs of sludges in this range, except for the 80 mg(Al)/L pH 4.8 sludges. It is not clear why these conditions in particular should give rise to a significant deviation in behaviour, in particular in the gel point; presumably it is due to the pH, rather than dose. NOM species are known to adopt different conformations as a function of ionic strength (or pH) [72], and are generally more globular at high ionic strength or pH [65]. The solubilities of the various species will also be differently affected by pH. For R() in the lower range of solid volume fractions the results show that the spiked sludges always had resistances greater than or equal to those of the non-spiked sludges. There are two physical reasons that typically underlie an increase in R(): the aggregates formed may be less compact, or the aggregates may be smaller. Each of these scenarios leads to narrower flow paths for a given . The influence on resistance to dewatering seems to break down under higher- conditions, which suggests that the structural mechanism behind the shift is relatively ‘weak’, and can be more or less neutralised by applying sufficient stress. In the same high solid volume fraction range there is an improvement in py (). The underlying structural reasons behind this type of shift are usually that the aggregates formed are more dense or the aggregates are smaller—or both. If the mechanisms are combined, a consistent hypothesis is achieved. Assuming that the aggregates formed are both smaller and denser (i.e. more compact) in the case of the sludges generated from more highly coloured (spiked) raw water, the behaviour at intermediate to high solid volume fractions is explicable. Such structures may arise if the reaction rate were decreased, as explained above. They may also arise due to the presence of an increased number of nucleation points or the formation of an external repulsive layer on the aggregates, perhaps NOM, [21,73] that reduces the further aggregation of existing clusters. Ordinarily it is expected that compact aggregates will pack together more efficiently, resulting in an increased gel point. To explain the observed decrease in g it is necessary to propose a second level of structure, such as the adherence of a thick ‘fluffy’ layer of material, perhaps NOM, to the surface of the otherwise hard and small particulates. This could result in the formation of a network at lower , but would be readily compressed so that it would have no influence on dewatering at higher volume fractions of solids. Finally, it is notable that the largest shifts upon spiking of the raw water are observed for the low-dose and the low-pH materials. It is clear that the NOM will make up a greater proportion of the sludge

22

D.I. Verrelli et al. / Colloids and Surfaces A: Physicochem. Eng. Aspects 348 (2009) 14–23

for the low-dose case. However in both cases it may be noted that the predicted reaction rate is low (see also Table 3). The industrial implication of these findings is that typical full-scale WTP’s appear to already be operating under optimal conditions. However, high NOM levels often demand higher coagulant doses, or ‘enhanced coagulation’, and sometimes changes in the coagulation pH: either of these can cause a reduction in sludge dewaterability, as shown.

References [1] [2] [3] [4] [5] [6] [7]

4. Conclusions The conditions under which coagulation is carried out have been found to influence the dewatering behaviour of the resulting precipitate across a range of solids concentrations from settling through to filtration. This dewatering behaviour has been fully characterised for each material in terms of the kinetic and equilibrium parameters R() and py (). For both ferric and alum sludges an improvement in dewatering was observed with a decrease in coagulant dose below a critical value of about 5 mg/L of the metal. This was consistent with slow, reaction-limited cluster aggregation, forming denser aggregates. As the pH was increased, the alum sludges (at high dose) exhibited poorer dewatering, which was consistent with rapid, diffusionlimited cluster aggregation due to the high hydrolysis ratio. Both alum and ferric sludges exhibited variation in their dewatering behaviours, and these variations occurred in approximately the same domain of py () and of R(), indicating that one cannot be automatically preferred over the other. A key factor in this finding has been in the solid phase densities used. These have a highly significant effect on the calculated parameters, and have been carefully determined here. Consistent results were obtained with both pilot plant and laboratory generated sludges. The results from full-scale WTP’s were also consistent with the identified trends, although less control could be exercised over the coagulation conditions, meaning that these did not span as wide a range of behaviours. This demonstrates that results obtained by characterising sludges generated in the laboratory can be used to predict full-scale dewatering behaviour. Solid phase density was found not to vary with alum dose, being constant at about 2500 kg/m3 . For ferric sludges the density may vary from <2700 km/m3 to >4000 kg/m3 , depending upon the dose. There may also be some minor pH-dependent effects. Hence analysis requires the use of accurate density data for a given sludge sample; large errors can be introduced by indiscriminate use of nominal values of solid phase density. The effect of raw water true colour or NOM content was investigated. The data for high raw water colour is consistent with the generation of smaller, denser aggregates with ‘fluffy’ surfaces that form a network at lower volume fractions of solids, and settle slower, but which could potentially be filtered to higher cake solid volume fractions. Raw water true colour or NOM may have different effects on dewaterability depending upon the coagulation conditions—even aside from the significant variation of NOM fractions and species in natural raw waters.

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Acknowledgements The authors wish to acknowledge United Utilities (UK) and Yorkshire Water for project sponsorship, and the Australian Research Council and The University of Melbourne for provision of a postgraduate scholarship. Melbourne Water, the CRC for Water Quality and Treatment, United Utilities Australia, and United Water are acknowledged for assistance in obtaining samples. The assistance of Dr. Shane P. Usher and Dr. Ross G. de Kretser is also acknowledged.

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