Abstract

Coagulation kinetics and floc properties are of great fundamental and practical importance in the field of water treatment. To investigate the performance of Fe(VI) and Fe(III) salt on particle coagulation, Malvern Mastersizer 2000 was employed to continuously and simultaneously monitor the kaolin floc size and structure change, and population balance modeling was used to investigate the coagulation mechanism. The results show dosage increase had positive effect on collision efficiency and floc strength and negative effect on restructure rate. Low shear rate resulted in higher collision efficiency and stronger floc. Low water temperature had a pronounced detrimental effect on coagulation kinetics. Temperature increase showed the most significant positive effect on collision efficiency, floc strength and restructure rate. The optimum pH zone for the coagulation was found to be between 6 and 8. Further pH increase lowered the collision efficiency and floc strength and increased the restructure rate. FeCl3 resulted in a larger ratio of the mass to volume of kaolin flocs (compactness) than those induced by ferrate.

HIGHLIGHTS

  • Population balance modeling achieved excellent approximation of coagulation process.

  • FeCl3 produced larger, stronger and more compact flocs than those by ferrate(VI).

  • Dosage increase had positive effect on collision efficiency and floc strength.

  • High temperature resulted in high collision efficiency, floc strength and restructure.

  • Optimum pH for both coagulation systems was between 6 and 8.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Introduction

As a result of self-decay and/or reactions with reductants (Zhou et al. 2019), ferrate(VI) (i.e. ) eventually becomes stable ferric ions (Fe(III)) via more active intermediate high valence iron species (i.e. Fe(V) and Fe(IV)) through electron flow. It is thus considered as a multifunctional water treatment agent due to its ability to oxidize, disinfect and coagulate simultaneously the undesirable substance in the water (Sharma et al. 2015). While most of the publication available focused on the oxidation functionality of Fe(VI) (Graham et al. 2010), only few studies have been conducted to evaluate its coagulation performance of Fe(VI) in water because it is hard to separate the coagulation effects from its multifunctionalities (Yu et al. 2016). Though the aforementioned difficulties, few efforts were still made to investigate the mechanisms of Fe(VI) coagulation along with its performance in the removal of colloidal particles from water. However, the results are inconsistent. Fe(VI) was reported as a better coagulant than Fe(III) when it was used to treat upland colored water (Jiang et al. 2001). Better algae and turbidity removal was observed when Fe(VI) was used as a pre-oxidation agent before alum coagulation (Ma & Liu 2002a, 2002b). It is reported Ferrate(VI) achieved comparable, or better, floc formation to ferric chloride, but less dissolved organic carbon (DOC) removal in humic acid solution in the absence of particulate matter (Graham et al. 2010). Fe(VI) pre-oxidation also improved the effluent turbidity of a downstream dual-media filter in continuous flow treatment experiments (Goodwill et al. 2016). In contrast, some research showed that Fe(III) demonstrated better coagulation effect than Fe(VI) using photometric dispersion analyzer 2000 (PDA 2000, Rank Brother, Inc., Cambridge, UK) (Yu et al. 2016). The flocs resulted from Fe(VI) and Fe(III) effects were different in size distributions and morphologies. Moreover, Fe(VI) resultant particles were mainly composed of crystalline Fe2O3, which was not detected in the Fe(III) resultant particles, as well as more nanoparticles that generated a stable colloidal suspension (Goodwill et al. 2015). It was found that the majority of Fe(VI)-induced flocs was suspended in the secondary effluent indicating its poor settleability (Zheng & Deng 2016). Fe(VI) dose played a key role in the size distribution and coagulation (Cui et al. 2018). High Fe(VI) dose could accelerate the aggregation of fine particles and facilitate downstream solid-liquid separation.

In order to only evaluate the coagulation ability of ferrate and clarify the coagulation performance of ferrate and Fe(III) salt, kaolin was used as the modelled solid particle in water suspension since it is inert to the oxidation effect of ferrate, so the observations of particle growth are believed to be caused by the coagulation effects of ferrate hydrolysis species and the FeCl3 (Tien et al. 2008).

It is commonly admitted that floc properties (such as floc size, structure and strength) of Fe(VI) and FeCl3 induced flocs have significant effect on the solid-liquid separation (Sang & Xiao 2008, 2009; Sang & Englezos 2012). Large and compact floc would facilitate the sedimentation and solid-liquid separation (Hubbe 2005). Previously, either jar test or photometric dispersion analyzer were employed to evaluate such floc properties. However, both methods have limitations. Conventional jar test methods are limited in terms of sensitivity and practical convenience, though they do provide a useful visual and semi-quantitative simulation of Fe(VI) and FeCl3 induced coagulation (Lv et al. 2018). Photometric dispersion analyzer (Rank Brother, Inc., Cambridge, UK), based on optical technique, can provide sensitive and rapid response of Fe(VI) and FeCl3 induced coagulation. However, information about degree of coagulation and the strength of flocs is ‘relative’. The degree of coagulation/flocculation is represented by a ‘relative’ turbidity. The floc strength is expressed as strength factor which is determined by the ratio of ‘relative’ turbidity obtained before and after the sudden increase in shear rate (Tien et al. 2008). Both aforementioned methods did not provide information on the real floc size, structure and strength. Traditionally, floc structure is assessed by microscopy imaging or transmission electron microscope (TEM) (Lv et al. 2018). The result is not representative for all the floc presented in the suspension as this method is limited to the measurement of only a few hundred flocs. It is time consuming and requires the performance of highly demanding procedures due to the fragility of the flocs.

It is thus necessary to take advantage of the latest developed technology which can monitor the floc properties during Fe(VI) and FeCl3 induced coagulation (Benn et al. 2018; Chassagne & Safar 2020). Mass fractal dimension is such a concept which describes the floc structure and the dynamic light scattering technique makes its measurement possible (Callesen et al. 2018). Therefore, the first objective of this research was to continuously monitor the floc size and floc structure change through dynamic light scattering using Mastersizer 2000 (Malvern Instruments Inc., Malvern, UK).

Strong flocs are desirable for solid-liquid separation during water treatment as good strength minimizes the floc breakage and increases the removal efficiency. The higher the floc density, the stronger the floc, the easier the removal of particles (Gregory 2004). Floc strength is therefore used to assess the floc quality. A traditional floc strength characterization is carried out by observing the floc index response to a sudden shear rate increase within a Gator reactor (Smith & Kitchener 1978). In order to measure the floc strength, micromechanical technique was developed to rupture a single floc in the tensile mode (Yeung & Pelton 1996). Because of limited measurement of flocs and highly demanding procedure due to floc fragility, micromechanical technique cannot provide the representative result for floc strength.

In order to manipulate the floc properties during Fe(VI) and FeCl3 induced coagulation, there is a need to obtain the quantitative information of floc strength and its dependence on process parameters. It is known floc breakup would happen when the hydrodynamic force applied to the flocs is equal or greater than the floc strength (Rau et al. 2018). Through incorporating the breakage rate and the critical energy dissipation rate required for the floc breakage into the population balance model, floc strength can be extracted. This is the second objective of this research.

In order to assist the judicial selection of chemicals and optimization of operating parameters, it is desired to have the relationship between collision efficiency, restructure rate and process conditions. Establishment of such relationship is the third objective of this research.

Materials and Methods

Materials

Kaolin, obtained from Chenming Group, China, was used as the model suspended solid in the water. The inherent volumetric mean particle size of the kaolin is 2.79 μm as measured by Mastersizer 2000 (Malvern Instruments Inc., Malvern, UK). It has a wide size distribution with the lowest limit size of 0.24 μm and the largest limit size of 7.26 μm. Kaolin suspension (50 mg/L) was prepared by adding kaolin to deionised distilled water and mixing thoroughly. FeCl3 and K2FeO4, both analytical regents, obtained from Chenming Group, China. Ferric chloride stock solution at a concentration of 0.01 mol/L was prepared by dissolving 2.71 g FeCl3.6H2O in 1 L deionised and distilled water immediately before the tests. Boric acid/NaOH (pH 9.0) buffer solution, prepared by mixing 50 mL 0.1 M boric acid/0.1 M KCl solution, 20 mL 0.1 M NaOH solution and 30 mL distilled and deionised water, was used to make ferrate solution. To maintain the reactivity of ferrate, stock K2FeO4 solutions were prepared immediately before use. Bicarbonate buffer solution (0.2 M) was made by dissolving NaHCO3 (Chenming Group, China) in distilled and deionised water. HCl and NaOH, analytical grade, were used to control pH values of the coagulation system.

Coagulation experiment

The kaolin coagulation process was continuously monitored by Mastersizer 2000 (Malvern Instruments Inc., Malvern, UK) where a peristaltic pump was used to circulate the kaolin suspension (50 mg/L) to the instrument for particle size and structure characterization. Figure 1 shows the schematic of the kaolin coagulation setup. Water bath was used to control the experiment temperatures. pH of the coagulation system was controlled by the addition of HCl and NaOH. The temperature, shear rate, pH, dosages of Fe(VI) and FeCl3 were adjusted accordingly to investigate their effects on the coagulation process. Kaolin is assumed to be inert to the oxidation effect of the ferrate and thus the observed floc properties (e.g. floc size, structure and strength) change could be regarded as the coagulation effects of Fe(III) from Fe(VI) self-decomposition. Experiments were conducted in the normal pH range of surface water and groundwater of 6 to 10.

Figure 1

Schematic of kaolin coagulation setup.

Figure 1

Schematic of kaolin coagulation setup.

Mass fractal dimension

Mass fractal dimension of flocs was calculated by Equation (1) from the scattered light density information obtained from Malvern Mastersizer 2000 (Teixeira 1988):
formula
(1)
where n is the fluid refractive index, λ is the laser light wavelength in vacuum, and θ is the scattering angle. The mass fractal dimension was obtained from the negative slope of the linear region of the log-log plot of scattered light intensity I(q) versus scattering vector Q (Bushell et al. 2002).

Population balance model

By discretizing the population balance model originally proposed by Smoluchowski (von Smoluchowski 1917) with respect to size and converting it to a set of nonlinear ordinary differential equations (ODE), the particle number concentration change rate in section i used for corresponding particle size calculation is given by (Hounslow et al. 1988):
formula
(2)
where Ni is the number concentration of particles in section i, αi,j is the collision efficiency, and βi,j is the collision frequency for the particles in sections i and j. The number concentration of primary particles is used as the initial condition for the population balance equation. Fragmentation rate Sj was used to describe the floc breakage rate. The distribution of floc fragments in interval i from the break-up of flocs in interval j was described by the breakage distribution function Гij. The first two terms on the right hand side of Equation (2) represent the aggregation birth of floc of size, i. The third and fourth terms represent the death of floc of size, i, due to aggregation. The fifth term is the death of floc of size, i. The sixth term is the breakage birth of floc of size, i, by breakage of larger flocs in interval j.

Collision efficiency

Collision efficiency is modeled either as a fitting parameter or as a function of the interaction forces between particles which take into account bridging attraction or steric repulsion, van der Waals attraction and electrical double layer repulsion between particles (Runkana et al. 2006). However, calculation of hydrodynamic interactions and short-range forces is required to determine the collision efficiency. Colloidal interactions and hydrodynamic retardation effects are usually ignored in the rectilinear model and then the collision efficiency is overestimated. Experimental data does not support the collision efficiency calculated by curvilinear model using DLVO theory (Wiesner 1992). Thus, a shell-core model was developed which enables a more accurate prediction of collision efficiency for the growth of floc (Kusters 1991). Due to its accuracy, it is adopted as a fitting parameter for population balance equation in this research and is described by Equation (3) (Kusters 1991).
formula
(3)
where, i and j represent the discretized sections of the floc size distribution. αmax, the maximum collision efficiency, equals to unity.

Collision frequency

The collision frequency, determined by particle radius, is defined as the number of particles passing through the capture cross-section per unit time. For particles smaller than one micron (Liu et al. 2012), the collision frequency is mainly determined by the Brownian motion if settling and inertial phenomenon are ignored (Hsu et al. 2001).

Equation (4) gives the collision frequency of particles smaller than one micron due to Brownian motion (Saffman & Turner 1956):
formula
(4)
where kB is the Boltzmann constant, T is the absolute temperature, and μ is the dynamic viscosity. Rci and Rcj are the collision radius, which can be calculated from the primary particle radius r0, the total number N of primary particles of the flocs in section i and the mass fractal dimensions df of the flocs (Saffman & Turner 1956):
formula
(5)
On the other hand, fluid motion and orthokinetic collision dominate the collision of particles larger than one micron. Both particle size and shear rate determine such collision frequency (Elimelech et al. 1995). The collision frequency of orthokinetic collisions between particles in sections i and j is calculated by Equation (6) (Elimelech et al. 1995).
formula
(6)
where ɛ (m2/s3) is the average energy dissipation rate, υ is the kinematic viscosity (m2/s). It shows the collision frequency is proportional to the cube of the particle radius, which has the major effect on the floc growth rate. The larger the floc size, the bigger the collision frequency.
Summing the collision caused by the perikinetic and orthokinetic motion, overall collision frequency is shown in Equation (7).
formula
(7)

Relationship between energy dissipation rate and breakage rate

The shear rate, flocs porosity, floc size and floc strength affect the breakability of the floc. Floc breakup happens when hydrodynamic force applied to the flocs is equal or greater than the aggregate strength (Kusters 1991). Equation (8) shows the relationship between the breakage rate Si and the critical energy dissipation rate required for the floc breakage (Kusters 1991):
formula
(8)
where ɛci is the critical energy dissipation rate per unit mass of fluid which cause the floc breakup, J·s−1·kg−1 (Shirazi et al. 2003).

Breakage distribution function

Binary breakage, ternary breakage and normally distributed breakage are the three most possible breakage distribution during the floc breakage process. Binary breakage functions resulted in sufficiently accurate and simple implementation compared to ternary breakage and normally distributed breakage functions (Spicer & Pratsinis 1996). Thus, the binary breakage distribution function shown in Equation (9) was chosen for modeling.
formula
(9)
By using the information of number concentration Ni of the particles in all the intervals and the characteristic floc diameter (m) in interval i (Kusters 1991), the volume mean diameter d4,3 (m) was calculated as in Equation (10):
formula
(10)
where, characteristic floc diameter (m) in interval i is calculated by the d0 and df obtained from Malvern Mastersizer 2000 measurement
formula
(11)

Floc structure change rate characterization

The floc structure changed during the coagulation process and its change rate can be represented by the dynamic change of mass fractal dimension and the rate can be calculated by Equation (12) (Bonanomi et al. 2004):
formula
(12)
γ (s−1) is restructure rate as fitting parameter and df,max is the maximum mass fractal dimension value.

Parameter estimation

Objective function with collision efficiency α, critical energy dissipation rate ɛci, and restructure rate γ as variables to be minimized is shown in Equation (13). Fminsearch solver in Matlab which uses Nelder-Mead simplex algorithm was used to find the minimum of the least squares:
formula
(13)

Ratio of the mass to volume of settled kaolin flocs (compactness)

The ratio of the mass to volume of settled kaolin flocs (compactness) was calculated as the ratio of the mass to the volume of settled flocs after 30 min of settling experiments.

Results and Discussion

Kaolin coagulation: experiment and simulation

As shown in Figure 2, population balance modeling achieved excellent approximation of kaolin floc size and floc structure change induced by the K2FeO4 and FeCl3 systems. The time required to reach steady state was about 8 min for K2FeO4 system and 5 min for FeCl3 system. The observed substantial floc formation induced by the two chemicals at iron concentration of 150 μM and neutral pH is corresponding to the sweep flocculation mechanism where kaolin is enmeshed within amorphous ferric hydroxide precipitates.

Figure 2

Experimental and simulated kaolin coagulation induced by K2FeO4 and FeCl3. (a) coagulation kinetics; (b) mass fractal dimension. Experimental conditions: dosage of Fe(VI) and FeCl3 = 150 μM, pH = 7; shear rate = 50 s−1; temperature = 25 °C. Notes: the data point is the experimental results whereas the connecting line shows the simulation result.

Figure 2

Experimental and simulated kaolin coagulation induced by K2FeO4 and FeCl3. (a) coagulation kinetics; (b) mass fractal dimension. Experimental conditions: dosage of Fe(VI) and FeCl3 = 150 μM, pH = 7; shear rate = 50 s−1; temperature = 25 °C. Notes: the data point is the experimental results whereas the connecting line shows the simulation result.

The results confirmed the simultaneous occurrence of aggregation, breakage and restructure during the coagulation process. During the initial stage of the coagulation, aggregation is the dominated factor which governs the floc growth. Meanwhile, floc became more susceptible to the hydrodynamic shear which broke, compacted (restructure effect) and limited the further growth of floc. It is evident that ferric chloride demonstrated better performance over potassium ferrate in inducing kaolin coagulation. This is probably because (1) the slow ferrate decomposition for coagulation and (2) the decomposition of Fe(VI) yields nanoparticles having a core-shell nanoarchitecture with a γ-Fe2O3 core and a γ-FeOOH shell (Prucek et al. 2013), which has a lower coagulation ability (Yu et al. 2016). Ferric chloride produced more compact flocs than those produced by potassium ferrate, as indicated by the higher mass fractal dimension. The mass fractal dimension also increased as coagulation proceeded, suggesting more compact flocs were formed.

Effect of dosage of potassium ferrate(VI) and FeCl3 on collision efficiency, energy dissipation rate and restructure rate

Figure 3 shows the positive effect of potassium ferrate(VI) and FeCl3 dosage increase on collision efficiency and floc strength (indicated by energy dissipation rate) and negative effect on restructure rate. Increasing the coagulant dosage gives progressively larger and stronger flocs but, little further improvement was observed beyond the optimum dosage.

Figure 3

Potassium ferrate(VI) and FeCl3 dosage effect on (a) collision efficiency, (b) energy dissipation rate and (c) restructure rate. Experimental conditions: pH = 7; shear rate = 50 s−1; temperature = 25 °C.

Figure 3

Potassium ferrate(VI) and FeCl3 dosage effect on (a) collision efficiency, (b) energy dissipation rate and (c) restructure rate. Experimental conditions: pH = 7; shear rate = 50 s−1; temperature = 25 °C.

Compared to potassium ferrate(VI) system, FeCl3 system demonstrated higher collision efficiency, higher floc strength and lower restructure rate. The inferior performance of ferrate system in comparison to ferric chloride is due to the slow decomposition of ferrate in water and low activity of the resulted iron nanoparticle for coagulation (Yu et al. 2016). The increase of potassium ferrate(VI) dosage would result in more decomposed iron nanoparticle for kaolin coagulation and thus higher collision efficiency. At the same time, high dosage provided more open structure from hydroxide precipitates for capturing colloidal particles and thus increased the collision efficiency and produced stronger flocs (Tran Tien 2012). It is also possible that the ‘bridging’’ of particles by precipitated hydroxide may give stronger aggregates.

Increasing Fe(VI) and FeCl3 dosage resulted in larger and stronger floc as indicated by the higher energy dissipation rate and lower restructure rate at high dosage. Compared to Fe(VI) system, higher energy dissipation rate (Figure 3(b)) and lower restructure rate (Figure 3(c)) of FeCl3 coagulation system is due to the larger and more compact flocs produce by the system, as shown in Figure 2.

Effect of shear rate on collision efficiency, energy dissipation rate and restructure rate

Figure 4 shows FeCl3 system resulted in higher collision efficiency, stronger floc and lower restructure rate than Fe(VI) system over the whole range of shear rate investigated. As shown in Figure 4(a) and 4(b), shear rate had a profoundly negative effect on collision efficiency and floc strength for both systems. Collision efficiency was lowered at high shear rate probably because the open structure and the bridge formed by hydroxide precipitates for colloidal particle capture were broken and therefore bonding point between the particles was reduced (Tran Tien 2012). Because of the reduction of bonding point, weaker floc was formed.

Figure 4

Effect of shear rate on (a) collision efficiency, (b) energy dissipation rate and (c) restructure rate. Experimental conditions: dosage of ferrate(VI) and FeCl3 = 150 μM; pH = 7; temperature = 25 °C.

Figure 4

Effect of shear rate on (a) collision efficiency, (b) energy dissipation rate and (c) restructure rate. Experimental conditions: dosage of ferrate(VI) and FeCl3 = 150 μM; pH = 7; temperature = 25 °C.

Restructure rate increase with the increase of shear rate was observed for both coagulants as shown in Figure 4(c). As the increase of shear rate, more force was applied to the flocs and thus more significant restructure. Restructure processes would help particles to rearrange their position within the flocs and form additional bonds to densify the flocs.

Effect of temperature on collision efficiency, energy dissipation rate and restructure rate

Figure 5 shows temperature increase positively affected the collision efficiency, floc strength and restructure rate. This phenomenon may be explained by the temperature effects on the particle transport processes and the coagulant chemistry. Through its effect on viscosity, and therefore on the mixing energy dissipated in water, water temperature eventually affects the particle collision rates during coagulation. Low temperature would negatively affect the fluid-particle interaction, particle-particle interaction, rate and extent of hydrolysis of metal salt coagulants, and adsorption and precipitation rates (Duan & Gregory 2003). For particles with a size greater than approximately 1 μm, orthokinetic collision dominates the coagulation process. However, orthokinetic collision caused by fluid shear diminished as the viscosity increased with decreasing temperature (Vik & Eikebrokk 1989). The poor rapid-mixing conditions at low water temperature also led to inhomogeneous coagulant distribution and resulted in poor coagulation (Vik & Eikebrokk 1989). Temperature decrease negatively affected the hydrolysis of ferrate(VI) and FeCl3 and the precipitation and solubility of Fe(OH)3 (Duan & Gregory 2003). High collision efficiency observed as temperature increase can be reasonably attributed to the effect of temperature on the perikinetic and orthokinetic aggregation process. High temperature has a positive effect on perikinetic collision frequency and results in more rapid Brownian motion as shown by Equation (4). Meanwhile, temperature increase also reduced the viscosity of water and facilitated fluid-particle interaction and particle-particle interaction and thus the coagulation process. Meanwhile, low viscosity of the system at high temperature would allow the shear force much easier to be transferred to flocs and restructure them. That is the possible reason for the high restructure rate observed at high temperature.

Figure 5

Effect of temperature on (a) collision efficiency, (b) energy dissipation rate and (c) restructure rate. Experimental conditions: dosage of ferrate(VI) and FeCl3 = 150 μM; pH = 7; shear rate = 50 s−1.

Figure 5

Effect of temperature on (a) collision efficiency, (b) energy dissipation rate and (c) restructure rate. Experimental conditions: dosage of ferrate(VI) and FeCl3 = 150 μM; pH = 7; shear rate = 50 s−1.

Effect of pH on collision efficiency, energy dissipation rate and restructure rate

Figure 6 shows the optimum pH for both coagulation system is between 6 and 8 where higher collision efficiency and stronger flocs were observed. It is well established the decomposition rate of ferrate depends on pH (Li et al. 2005). In order to achieve better Fe(VI) coagulation performance, it is desired to have higher Fe(VI) decomposition rate to generate Fe(III) for coagulation and lower solubility of amorphous iron(III) hydroxide to capture the colloidal particle. Fe(VI) has highest decomposition rate (Li et al. 2005) and lowest solubility of amorphous iron(III) hydroxide over pH range of 6 to 8 (Amirtharajah & Melia 1990; Tran Tien 2012). Fe(III) has limited solubility at pH range of 6 to 8 and the amorphous hydroxide would enmesh colloidal particles and cause hetero-coagulation (Duan & Gregory 2003). The combination of high Fe(VI) decomposition rate and low Fe(OH)3 solubility at pH range of 6 to 8 made the collision efficiency high and floc strong.

More significant decrease of collision efficiency and floc strength was observed for ferrate system than FeCl3 system when pH was increased beyond 8. This is probably because, at this pH range, Fe(VI) became extremely stable and had very low (close to zero) decomposition rate (Li et al. 2005). It is reported minimum decomposition rate was observed between pH 9.2 and 9.4 that means almost no Fe(III) was produced for coagulation (Li et al. 2005). Meanwhile, pH increase also decreased the positive charge of the amorphous iron(III) hydroxide and increased its solubility which made it less effective to induce coagulation (Tran Tien 2012). The more significant restructure observed at high pH is most likely due to the low strength flocs produced which made them more susceptible to restructure.

Ratio of the mass to volume of settled kaolin flocs (compactness)

Figure 7 shows the ratio of the mass to volume of settled kaolin flocs (compactness) after addition of ferrate or FeCl3. As shown in the figure, FeCl3 resulted in a larger ratio of the mass to volume of kaolin flocs (compactness) than those induced by ferrate. This result further confirmed more compact flocs formed by FeCl3 as indicated by larger mass fractal dimension shown in Figure 2. More compact floc would facilitate the particle separation from water suspension through sedimentation process (Wilen et al. 2003). Meanwhile, the formation of more compact floc could expel water from flocs which can also facilitate drainage during compressing (Hubbe 2005). The difference in the compactness is probably because the core-shell nanoarchitecture with a γ-Fe2O3 core and a γ-FeOOH shell from Fe(VI) decomposition has a lower coagulation ability than FeCl3 (Yu et al. 2016).

Figure 6

Effect of pH on (a) collision efficiency, (b) energy dissipation rate and (c) restructure rate. Experimental conditions: dosage of ferrate(VI) and FeCl3 = 150 μM; shear rate = 50 s−1; temperature = 25 °C.

Figure 6

Effect of pH on (a) collision efficiency, (b) energy dissipation rate and (c) restructure rate. Experimental conditions: dosage of ferrate(VI) and FeCl3 = 150 μM; shear rate = 50 s−1; temperature = 25 °C.

Figure 7

Ratio of the mass to volume of settled kaolin flocs (compactness). Experimental conditions: dosage of ferrate(VI) and FeCl3 = 150 μM; pH = 7; shear rate = 50 s−1; temperature = 25 °C.

Figure 7

Ratio of the mass to volume of settled kaolin flocs (compactness). Experimental conditions: dosage of ferrate(VI) and FeCl3 = 150 μM; pH = 7; shear rate = 50 s−1; temperature = 25 °C.

Conclusions

Population balance modeling achieved excellent approximation of kaolin floc size evolution induced by Fe(VI) and FeCl3. Ferric chloride produced larger, stronger and more compact flocs than those produced by potassium ferrate. Potassium ferrate(VI) and FeCl3 dosage increase had positive effect on collision efficiency and floc strength and negative effect on restructure rate. FeCl3 system resulted in higher collision efficiency, stronger flocs and lower restructure rate than Fe(VI) system over the whole range of shear rate investigated. Temperature increase positively affected the collision efficiency, floc strength and restructure rate. Optimum pH for both coagulation system is between 6 and 8 where higher collision efficiency and stronger flocs were observed. FeCl3 resulted in a larger ratio of the mass to volume of kaolin flocs (compactness) than those induced by ferrate which would facilitate the particle separation from water suspension through sedimentation process.

ACKNOWLEDGEMENTS

This work was supported by China Postdoctoral Science Foundation (Grant No. 2016M592271), 2016 Shandong Province Postdoctoral Innovation (Grant No. 201603007), Fundamental Research Funds for the Central Universities (Grant No. 18CX02106A), Shandong Provincial Natural Science Foundation (ZR2019MB022).

CONFLICT OF INTEREST

The authors declare no conflict of interest.

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

REFERENCES

Amirtharajah
A.
Melia
C. R.
1990
Coagulation Processes: Destabilization, Mixing, and Flocculation in Water Quality and Treatment, FW Pontius
.
McGraw-Hill
,
New York, NY, USA
.
Benn
F.
Fawell
P.
Halewood
J.
Austin
P.
Costine
A.
Jones
W.
Francis
N.
Druett
D.
Lester
D.
2018
Sedimentation and consolidation of different density aggregates formed by polymer-bridging flocculation
.
Chemical Engineering Science
184
,
111
125
.
Bonanomi
E.
Sefcik
J.
Morari
M.
Morbidelli
M.
2004
Analysis and control of a turbulent coagulator
.
Industrial & Engineering Chemistry Research
43
(
19
),
6112
6124
.
Bushell
G. C.
Yan
Y. D.
Woodfield
D.
Raper
J.
Amal
R.
2002
On techniques for the measurement of the mass fractal dimension of aggregates
.
Advances in Colloid and Interface Science
95
(
1
),
1
50
.
Cui
J.
Zheng
L.
Deng
Y.
2018
Emergency water treatment with ferrate (VI) in response to natural disasters
.
Environmental Science: Water Research & Technology
4
(
3
),
359
368
.
Duan
J.
Gregory
J.
2003
Coagulation by hydrolysing metal salts
.
Advances in Colloid and Interface Science
100
,
475
502
.
Elimelech
M. J.
Gregory
J.
Jia
X.
Williams
R. A.
1995
Particle Deposition and Aggregation: Measurement, Modeling, and Simulation
.
Butterworth-Heinemann Ltd
,
Oxford
,
UK
.
Goodwill
J. E.
Jiang
Y.
Reckhow
D. A.
Gikonyo
J.
Tobiason
J. E.
2015
Characterization of particles from ferrate preoxidation
.
Environmental Science & Technology
49
(
8
),
4955
4962
.
Goodwill
J. E.
Jiang
Y.
Reckhow
D. A.
Tobiason
J. E.
2016
Laboratory assessment of ferrate for drinking water treatment
.
Journal-American Water Works Association
108
(
3
),
E164
EE74
.
Graham
N.
Khoi
T.
Jiang
J.-Q.
2010
Oxidation and coagulation of humic substances by potassium ferrate
.
Water Science and Technology
62
(
4
),
929
936
.
Gregory
J.
2004
Monitoring floc formation and breakage
.
Water Science and Technology
50
(
12
),
163
170
.
Hounslow
M. J.
Ryall
R. L.
Marshall
V. R.
1988
A discretized population balance for nucleation, growth, and aggregation
.
AICHE Journal
34
(
11
),
1821
1832
.
Hsu
J. W. C.
Speers
R. A.
Paulson
A. T.
2001
Modeling of orthokinetic flocculation of Saccharomyces cerevisiae
.
Biophysical Chemistry
94
(
1–2
),
47
58
.
Hubbe
M. A.
2005
Mechanistic aspects of microparticle systems
.
TAPPI J.
4
(
11
),
23
28
.
Jiang
J.
Lloyd
B.
Grigore
L.
2001
Disinfection and coagulation performance of potassium ferrate for potable water treatment
.
Environmental Engineering Science
18
(
5
),
323
328
.
Kusters
K. A.
1991
The Influence of Turbulence on Aggregation of Small Particles in Agitated Vessels
.
PhD Dissertation/Thesis
,
Doctoral dissertation, Eindhoven University of Technology
,
The Netherlands
.
Li
C.
Li
X.
Graham
N.
2005
A study of the preparation and reactivity of potassium ferrate
.
Chemosphere
61
(
4
),
537
543
.
Liu
L. S.
Yang
G. L.
Yu
M. H.
2012
Simulation for sludge flocculation I: Brownian dynamic simulation for perikinetic flocculation of charged particle
.
Mathematical Problems in Engineering
2012
,
1
17
.
Lv
D.
Zheng
L.
Zhang
H.
Deng
Y.
2018
Coagulation of colloidal particles with ferrate(VI)
.
Environmental Science: Water Research & Technology
4
(
5
),
701
710
.
Prucek
R.
Tuček
J. I.
Kolařík
J.
Filip
J.
Marušák
Z. K.
Sharma
V. K.
Zbořil
R.
2013
Ferrate (VI)-induced arsenite and arsenate removal by in situ structural incorporation into magnetic iron (III) oxide nanoparticles
.
Environmental Science & Technology
47
(
7
),
3283
3292
.
Runkana
V.
Somasundaran
P.
Kapur
P. C.
2006
A population balance model for flocculation of colloidal suspensions by polymer bridging
.
Chemical Engineering Science
61
(
1
),
182
191
.
Saffman
P. G.
Turner
J. S.
1956
On the collision of drops in turbulent clouds
.
Journal of Fluid Mechanics
1
(
1
),
16
30
.
Sang
Y.
Englezos
P.
2012
Flocculation of precipitated calcium carbonate (PCC) by cationic tapioca starch with different charge densities. I: experimental
.
Colloids and Surfaces A: Physicochemical and Engineering Aspects
414
,
512
519
.
Sang
Y.
Xiao
H.
2008
Clay flocculation improved by cationic poly (vinyl alcohol)/anionic polymer dual-component system
.
Journal of Colloid and Interface Science
326
(
2
),
420
425
.
Sang
Y.
Xiao
H.
2009
Preparation and application of cationic cellulose fibers modified by in situ grafting of cationic PVA
.
Colloids and Surfaces A: Physicochemical and Engineering Aspects
335
(
1–3
),
121
127
.
Sharma
V. K.
Zboril
R.
Varma
R. S.
2015
Ferrates: greener oxidants with multimodal action in water treatment technologies
.
Accounts of Chemical Research
48
(
2
),
182
191
.
Shirazi
M.
van de Ven
T. G. M.
Garnier
G.
2003
Adsorption of modified starches on pulp fibers
.
Langmuir
19
(
26
),
10835
10842
.
Smith
D. K. W.
Kitchener
J. A.
1978
The strength of aggregates formed in flocculation
.
Chemical Engineering Science
33
(
12
),
1631
1636
.
Spicer
P. T.
Pratsinis
S. E.
1996
Coagulation and fragmentation: universal steady-state particle-size distribution
.
AICHE Journal
42
(
6
),
1612
1620
.
Teixeira
J.
1988
Small-angle scattering by fractal systems
.
Journal of Applied Crystallography
21
,
781
785
.
Tien
K. T.
Graham
N.
Jiang
J.-Q.
2008
Evaluating the Coagulation Performance of Ferrate: A Preliminary Study
.
ACS Publications
,
San Francisco, CA, USA
.
Tran Tien
K.
2012
Evaluation of Potassium Ferrate as a Coagulant in Water and Wastewater Treatment. PhD Thesis, Imperial College London, UK
.
Vik
E. A.
Eikebrokk
B.
1989
Coagulation Process for Removal of Humic Substances From Drinking Water
.
ACS Publications
,
Washington, DC, USA
.
von Smoluchowski
M. V.
1917
Mathematical theory of the kinetics of the coagulation of colloidal solutions
.
Zeitschriftfuer Physikalische Chemie
92
,
129
168
.
Wiesner
M. R.
1992
Kinetics of aggregate formation in rapid mix
.
Water Research
26
(
3
),
379
387
.
Wilen
B. M.
Jin
B.
Lant
P.
2003
Impacts of structural characteristics on activated sludge floc stability
.
Water Research
37
(
15
),
3632
3645
.
Yeung
A. K. C.
Pelton
R.
1996
Micromechanics: a new approach to studying the strength and breakup of flocs
.
Journal of Colloid and Interface Science
184
(
2
),
579
585
.
Zhou
J.
Zhao
Z.
Liu
J.
Peng
W.
Peng
X.
Han
Y.
Xiao
P.
2019
Removal of Microcystis aeruginosa and control of algal organic matters by potassium ferrate (VI) pre-oxidation enhanced Fe (II) coagulation
.
Korean Journal of Chemical Engineering
36
(
10
),
1587
1594
.
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