e:> Pergamon

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Wal. Sci. Tech. Vol. 36. No.4. pp. 77-84. 1997. Cl 1997 IAWQ. Published by Elsevier Science Ltd

Printed in Great Britain. 0273-1223197517'00 +0·00

SIMULATING THE EFFECT OF BLANKET CHARACTERISTICS ON THE FLOC BLANKET CLARIFICATION PROCESS

Richard Head*, Jem Hart * * and Nigel Graham***

• Water Treatment Group. Water Research Centre. Frankland Road. Blagrove. Swindon. SN58YF. UK •• Linhoff-March Limited. Targeting HOl/se. Gadbrook Park, Northwich. Cheshire. CW97UZ. UK ••• Professor of Environmental Engineering. Department of Civil Engineering. Imperial Col/ege of Science and Technology. Imperial College Road. London. SW72BU. UK

ABSTRACT

Previous attempts at simulating the floc blanket clarification process have failed to predict adequately the perf01lllance of 'real' tanks on water Ireatment works over a range of lreatment conditions. In this paper, a mathematical model of the floc blanket clarification process is presented. This model has been used to simulate the perf01lllance of a flat-bottomed clarifier operating on a full-scale waler Ireatment works. The model predictions have heen compared with data obtained from the treatment works. and the model has been shown to perf01lll well. The model has then been used to simulate the effects of changes in the operating conditions of the clarifier on the concentration of the blanket in the clarifier and the quality of the treated water. The importance and potential uses of a model of the process at a lrealrnent works are discussed. © 1997 IA WQ. Published by Elsevier Sdence Ltd

KEYWORDS

Hoc blanket clarification; hindered settling; mathematical modelling; water treatment

INTRODUCTION

Hoc blanket clarification is one of the principal water treatment processes. but is perhaps theoretically the least understood. In the United Kingdom. it is well-established as the major solids removal stage on many water treatment works. Previous modelling work has tended to be based mostly on empirical studies of the performance of pilot and fulI-scale units. Some fundamental studies have been undertaken. but these have failed to be corroborated by experimental data at full-scale works. For a model of such a process to be of use in the water industry. it must be capable of simulating with confidence. the constantly changing conditions on a real works. The aim of this work was to develop a model of the process and a method for calibrating the model. The model has been run to simulate a full-scale clarifier and the results compared with plant data. Once confidence in the ability of the model to simulate accurately full-scale process performance has been established. the model can be run to study the effects of changes in the operating conditions and raw water quality on the process performance.

77

78 R. HEAD et al.

Tesarik (1967) studied the performance of a suspension in a straight-sided column to simulate a floc blanket clarifier. He found that both the solids concentration of the effluent water and the void fraction in the blanket increased with upflow velocity and that the concentration of the blanket, measured as the 2h settled volume was constant through the experimental tube, apart from at the inlet, where it was reduced due to high turbulence. Below a certain value of up flow velocity, settling took place and floc accumulated at the bottom of the column.

Ives (1968) developed one of the first theoretical studies of floc blanket clarification. Based on the principles of orthokinetic flocculation within the blanket, he predicted the removal of particles in a clarifier to be

where

and

nt, nO are the number of particles entering the tank and after time t, respectively, G is the velocity gradient, C is the concentration of the suspension.

(1)

However, in this approach, Ives gave no indication of how to measure the floc concentration, C. Using typical values of G, C and t, equation (1) gives a removal efficiency (nlnr) of the order of 10,10, which is not achieved in practice. His theory suggests that at the bottom of a conical clarifier, a clear zone is formed. This is not supported by any published data, and it is these authors' experience that, in practice, the blanket concentration is fairly constant throughout the depth of a hopper-bottomed clarifier.

An extensive review of the work carried out up to 1979 was published by Gregory (1979). He commented that though some qualitative work had been presented, the only theoretical work was limited in application and explanations for poor clarifier performance were mainly unproved. Gregory recognised the importance of floc volume concentration and suggested that particles were removed from the treatment stream due to mechanical entrapment by the fully-grown flocs forming the blanket. He stated that the treated water quality depended on the blanket floc concentration, which in tum depended on the upflow velocity. He measured blanket concentration as the percentage of a 100ml sample occupied by settled tloc in a measuring cylinder after 30 minutes of quiescent settling. However, his work was mainly experimental and failed to link these parameters in a quantitative way, apart from defining an optimum blanket concentration of 14-18%. He reviewed various theories of hindered settling and found that his data was best described by adapting the relationship of Bamea and Mizrahi (1973): .

where

and

v = V,,(l - qC'r ' V is the settling velocity of the suspension, Vo is terminal settling velocity, q is a factor relating to shape and concentration measurement, C· is the measured blanket concentration after 30 mins settling, n is an exponential constant.

(2)

Gould (1974a) also suggested a settling velocity relationship of this form, defining C· as the concentration measured by a convenient and reproducible method, and q and n as parameters chosen so that the resulting formula closely approximates the observed behaviour of the suspension.

Two dIfferent theories of clarifier operation have been found to simulate the behaviour of floc blanket clarifiers. The solids tlux theory was developed and applied to secondary c1arit1ers in wastewater treatment (Vitasovic, 1989; Takacs et al., 1991). This theory has been applied in water treatment (Gregory et al., 1996) with promising results. A similar, but more complicated, analysis was presented by Clarke et al. (1978) and Wilson et al. (1979). However, their approach was heavy on computer time and, while valuable, appeared to bear little resemblance to the performance encountered on real works.

Floc blanket clarification process 79

A further method of study was proposed by Gould (I974b) and it is this approach which has been developed in this paper.

MODEL DEVELOPMENT

This model has been based upon Gould's theory of the operation of floc blanket clarifiers. After a change in the upt10w rate, or the settling velocity of the blanket, the surface of the blanket will rise or tall at a rate equal to the difference in velocities between the upflow and the hindered settling rate. This change in position of the blanket surface can be expressed as:

where

and

H is the height of the blanket above the bottom of the tank. U is the instantaneous upflow velocity in the tank. Vs is the instantaneous hindered settling velocity of the blanket.

(3)

To simulate hindered settling. a moditled version of the Bamea-Mizrahi equation has been used here:

(4)

where Cmin defines the cut-off point on the hindered settling curve. below which the settling velocity of the suspension remains constant. V max is the settling velocity at and below Cmin• C is the concentration of the suspension. defined as the 30 minute settled volume (Gregory. 1979). s is a shape factor

and n is an exponent factor.

The settling velocity equation needs to take into account the effects of temperature. Due to increased viscosity at low temperatures. a floc blanket has a much lower settling velocity in winter conditions. Hence. for calibration. each concentration measurement must be made at the same temperature.

It is well established (e.g. Gregory. 1979) that the removal rate of primary panicles entering the clarifier is dependent on the blanket concentration. Here. the removal was simulated as a continuous stirred tank reactor (CSTR). The mass balance for a single CSTR is given by:

where

and

-=-(x -x)- +=..:. X dX Q [keClI ,. A] dl V I L I'

X is the concentration of settleable solids leaving the clarifier. Xj is the concentration of settleable solids entering the clarifier. Q is the inlet now rate. V is the volume of the tank. A is the surface area of the tank. L is the height of the tank. Vs is the settling velocity of primary panicles entering the tank. kfl is a tlocculation factor. reflecting the collision efficiency of the floes.

(5)

80 R. HEAD tt al.

The solids loading on the clariner is calculated from the concentration of suspended solids in the raw water and the dose of coagulant (Warden and Craft, 1980):

where

and

(6)

Xraw is the raw water suspended solids concentration (mgll), .. 2*raw water turbidity (NTU), D AI is the dose of an aluminium based coagulant (mgll Al3+), DFe is the dose of a ferric coagulant (mgll Fe3+), Crem is the colour removed due to the addition of coagulant (OHazen). Dpolv is the dose of polyelectrolyte (mgll). if used, D PAC is the dose of powdered activated carbon (mgll), if used.

The removal of excess floc via a sludge cone is assumed to take place at the level of the top of the cone and at a constant rate. To take into consideration the possibility of poor coagUlation, a non-settleable fraction of solids can be passed into the model.

These equations have been coded into FORTRAN. Equation (3) is solved using a finite difference approach. and the CSTR equation (5) has been solved using the Runge-Kutta method. After the new level of the blanket is estimated for each time step, the change in blanket concentration due to addition/removal of solids and expansion/contraction of the blanket is calculated to predict a new blanket concentration for the next step.

CALIBRA nON AND TESTING OF THE MODEL

For studying the model. data was obtained from a water treatment works treating a surface water abstracted directly from a river in the south of England. The characteristics of the raw water over a year are shown below (Table I).

Temperature (oC) pH Turbidity (fTU) Colour (~az.en) Total Iron (mg Fe '+/1 )

Table 1. Raw water characteristics

Maximum 21 tt4 35

27.t! 0.X2

Minimum 3.5 7.7

0.41 3

0.01

Average 11.7 8.1 3.0 6.4

0.09

The treatment works has 7 flat-bottomed noc blanket clarifiers. One of these was chosen for stUdy, due to the large amount of data that had been collected from it during a one month period. The clarifier has a surface area of 216m2 and is 6m deep. Excess noc is removed through a cone at a height of 4.3m from the noor of the tank. The plant uses ferric sulphate as the coagulant. without any polyelectrolyte, and due to the low colour in the raw water, the colour removal term has little effect, so that the solids loading Equation (6) becomes

(7)

Data from the first half of the month was chosen for the purposes of calibrating the model. as during this time. the raw water temperature was constant when measured at the same time each day. The coagulant dose and raw water turbidity were also fairly steady during this period. To calibrate the model, the constants in the hindered settling equation (4) must be estimated. This equation can be linearised to give

(8)

Floc blanket clarification process 81

Plotting In(V) against In[1 - s(C - Cmin)] for the plant data, and optimising C min and s to give the best straight line through the points gives values for the calibration constants. For the site studied, the values found were s = I, n = 2.425, V,nax = 5.65 mh-! and Cmin = 10%. The flocculation factor, kfl' in equation (5) can be estimated by choosing a time when the influent concentration, effluent concentration and throughput are steady and known. Rearranging (5) gave a value of kfl = 12.5 hoi. A non-settleable solids fraction of 5% was used, based on typical jar test results.

Using these values and the plant data for the month, the model was then run to simulate the performance of the clarifier on the works. During the month, the average daily temperature ranged from 9.5 to 14.5°C. and the clarifier throughput from 24 to 26 MUd. A diurnal temperature profile was imposed upon the average daily temperatures that had been recorded. such that the highest water temperature was at 1800h and the lowest at 0800h. O.5°C above and below the mean respectively. Figure I shows the comparison between the measured and predicted blanket concentrations for the month simulated. and Figure 2 a similar comparison between the measured and predicted clarified water turbidities.

30

25

5

- Model predictions

• Planldata

O~--~r----+-----r----~----~--~-----+-3110/95 7110195 11/10/95 15/10195 19/10/95 23110195 27110195 31110195

Dal.

Figure 1. Comparison of model predictions of blanket concentration with plant data.

5

4.5

4 - Model predictions .... Planldala

O+--------+------+---------+------~ 1/10/95 5/10195 9110195

Dal. 13110195 17110195

Figure 2. Comparison of model predictions of clarified water turbidity with plant data.

82 R. HEAD el al.

The tigures show that the model gives good predictions of the plant performance, particularly for blanket concentration. The concentration rises and falls due to changes in the throughput, the solids loading on the claritier and the temperature of the water. The effect of temperature can be seen most clearly as the decrease in blanket concentration from the middle of the month onwards. The rise in concentration at the end of the month (30/10/95 onwards) was caused by a sudden increase in the solids concentration in the river, and a subsequent increase in coagulant dose to cope with this. Since a low desludging rate has been used in the model, the effect of this increase in loading is that the top of the blanket initially rises, before settling back down to the level of the top of the sludge cone over a couple of days. Thereafter, the blanket concentration steadily increases. If an increased desludging rate had been used, the model would predict the increase in concentration earlier, and show closer agreement with the plant results. However, the operating data relating to the control of the desludging was not available, so this assumption was not made in the model predictions. It also worth noting here that the measurement technique for blanket concentration only allows it to be measured to the nearest 1%.

The clarified water data shows that the model is good at predicting the trends in the water qUality. Unfortunately, the on-line clarified water turbidity was only available for the first half of the month. The tirst of the two large (> 5NTU) peaks in the plant turbidity was a recording error as the on-line turbidimeter was cleaned. The second of these peaks was recorded just after the dosing of powdered activated carbon (PAC) was started. The effects of carbon dosing have not been considered in the modelling at this stage, except as an increase in the solids loading on the claritier. The model predictions are much smoother than the recorded plant data, and this is partly due to the nature of the on-line measurements. The clarified water turbidities are low (around I NTU), with short-term variation probably caused by the carry-over of larger floc particles from the blanket or the clumping of particles leaving the clarifier.

USES OF THE MODEL

The model can be used in a wide range of possible applications, in both operational and design scenarios. Examples of using the model as an operational tool include: assessing the effect of sudden changes in the raw water quality; studying the effect of changing the now; estimating the maximum throughput while keeping a good quality treated water; assessing the effects of temperature on the performance characteristics; studying the optimum rate at which desludging should be carried out to achieve good control of the blanket. When there is a bank of clarifiers on a works, the model can be use to study the effect of taking one clarifier out of service for cleaning on the performance of the other clarifiers. In a design situation, the model can be used to study the performance of new plant, prior to installation or to estimate the increase in clarifier size required to treat an increase in throughput.

As an example of using the model, the effect of temperature on the performance characteristics of the clarifier that has previously been studied has been simulated (Figures 3 and 4). It is clear that care must be taken in operating such a claritier when the temperature is low in winter, as the blanket can easily be washed out at rise rates which in summer produced a good quality treated water (e.g. at goC, the maximum rise rate before washout was 5.3 mh-), whilst at 15°C, the rise rate could be increased to 6.5 mho) before washout).

CONCLUSIONS

A model of the noc blanket clarification process has been developed. The model has been calibrated and tested on a full-scale works. Using this calibration, the model has been used to predict the performance of the clarifier on the works in both winter and summer conditions and has helped to pin-point the throughput at whIch blanket washout would start to occur. Various uses of the model have been discussed and an example described to highlight the importance of such a model to plant operators. The model is currently being tested on other full-scale and pilot-scale clarifiers. Future work will involve building the effect of coagulation conditions and particle size distribution into the model.

2.5

0.5 -+

Floc blanket clarification process

3.5 4 45 5 5.5

Rise Rate (m/hr)

Figure 3. Effect of throughput on blanket concentration ..

-T=15'C•

: ... _l~~'C_~

o t ---------------+ .---------+---

2.5 3.5 4 4.5 5.5 6

Rls. Rate (mlh,)

Figure 4. Effect of throughput on treated water quality.

ACKNOWLEDGEMENTS

83

6.5

6.5 7

Richard Head was in receipt of a Postgraduate Training Award under the Postgrauuate Training Partnership (PTP) scheme. The authors would like to thank Southern Water Services for providing access to plant data.

84 R. HEAD et al.

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Clarke, A. N .• Wilson, D. J. and Clarke, J. H. (1978). Theory of clarifier operation. III. Sludge blanket and upflow reactor-clarifiers. Sep. Sci. Tech .• 13(]0), 895-915.

Gould, B. W. (1974a). Hindered settling and sludge blanket clarifiers. Effl· Wat. Treat. J., 14(3), 131-139. Gould, B. W. (]974b). Upflow clarifiers - flow flexibility related to concentrator size. Effl. Wat. Treat. J., 14(11),621-631. Gregory, R. (1979). Floc blanket clarification. WRc Technical Report, TRll1, Water Research Centre, Medmenham, UK. Gregory, R., Head, R. J. M. and Graham, N. J. D. (1996). Blanket solids concentration in floc blanket clarifiers. Proc Gothenburg

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1271. Tesarik, I. (] 967). Flow in sludge blanket clarifiers. Proc. A.S.C.E. J. San. Eng. Div .. 93 (SA6), 105-120. Vitasovic, Z. (1989). Continuous Settler Operation: A dynamic model. In: Dynamic Modelling and Expert Systems in Wastewater

Engineering, G. G. Patry and D. Chapman (eds), Lewis Publishers Inc., Chelsea. MI, USA. pp. 59-81. Warden. J. H. and Craft, D. G. (]980). Waterworks Sludge - Production and Disposal in the UK. WRc Technical Report, TR150,

Water Research Centre, Medmenham, UK. Wilson, D. J .. Clarke. A. N. and French, R. H. (1979). Theory of clarifier opeartion. IV. Orthokinetic flocculation in concentrated

slurries. Sep. Sci. Tech., 14(]), 1-12.