An empirical model for primary sedimentation of sewage

Environment Inkmahnal, Vol. 24. No. 8, pp. 925-934.1998 copyti8bl Of9911 Ehvia .5ciaw Ltd hinted in the USA. AU rights maved 0160-4120/98 S19.00+.00

Pergamon

P11S0160-4120(98)00076-2

AN EMPIRICAL MODEL FOR PRIMARY SEDIMENTATION OF SEWAGE D.G. Christoulas,

P.H. Yannakopoulos,

and A.D. Andreadakis

Department of Civil Engineering, National Technical University of Athens, lroon Polytechniou 5. Athens 15780, Greece

EI 9803-121 M (Received 2 March 1998; accepted 16 July 1998)

models of primary sedimentation and column settling tests have failed until now.to predict the behaviour of sedimentation tanks under actual operating conditions due to the difficulties in simulating the effect of the density currents and the complex phenomenon of flocculation. Therefore, empirical models can be helpful in the design of sedimentation tanks. Using performance data from three different pilot-scale sedimentation tanks, empirical mathematical models were developed in ‘this paper relating suspended solids (SS) removal Theoretical mathematical

effkiency to surface overflow rate, influent SS concentration, and sewage temperature. The model coeffkients were derived from the combined analysis of three well correlated sets of data, thus giving a good indication for their possible general applicability. The analysis of experimental data also gave a relationship between SS and chemical oxygen demand (COD) removal efficiencies. 01998 Elscvicr Science Ltd

INTRODUCTION The main design variable of a PST is its surface area A. The depth of the tank is usually chosen on the basis of technical considerations in the rather narrow range 3.0-4.5 m (Metcalf and Eddy 1991). For the rational design of PSTs, deterministic or stochastic mathematical models are needed, connecting A and more generally the tank geometry, to E,, the sewage flow rate Q, as well as other sewage characteristics which affect sedimentation. Although plain sedimentation is the oldest and one of the most widely used unit processes in sewage treatment, no satisfactory mathematical models have been developed as yet, mainly due to the complexity of the phenomena involved. Sewage contains flocculent particles, which do not have constant settling characteristics. Flocculation and settling are influenced by many factors such as the detailed velocity field, influent SS concentration, particle size and density, and

Primary sedimentation tanks (PST) are used in sewage treatment for the removal of suspended solids (SS) by gravitational settling. Since the SS are largely organic in nature, their removal results in significant decrease of the sewage organic load, usually expressed in terms of biochemical oxygen demand (BOD) or chemical oxygen demand (COD). The SS removal efficiency E, of PSTs is defined as follows:

(1)

where Si and S, are the influent and effluent SS concentrations, respectively. Similar definitions are applied for the BOD and COD removal efficiencies E, and E,, respectively. 925

D.G. Christoulas et al.

926

LIST OF SYMBOLS A D a, b, c E, E,

E

W

:: q S, si t

TS V vss 0

Surface area of sedimentation tank Tank diameter Constants COD removal efficiency Suspended solids removal efficiency Volatile suspended solids removal efficiency Tank depth Flow rate m3 d-l Surface overflow rate = Q/A Effluent suspended solids concentration mg& Influent suspended solids concentration mg/L Temperature ’C Total solids Tank volume Volatile suspended solids Hydraulic retention time

the density and viscosity of the fluid. The chemical characteristics of the particles and the fluid also affect flocculation. Random environmental factors (heat flux and wind action) and inlet conditions often cause drastic changes to the density and velocity field, which in turn can cause major variations in SS removal. Short circuiting and circulating flow are typical examples of such changes. The theoretical mathematical models, though helpful to the understanding of the sedimentation process, are still far from being reliable and effective design tools. The models of Valioulis and List (1984) and Lyn et al. (1992) are two characteristic cases. The first was derived from an advanced description of flocculation under idealized flow conditions. The second is more successful in the description of the flow regime, but poorer in the description of flocculation. Models have also been proposed which combine simplified or advanced flow and dispersion equations with an assumed constant average settling velocity of the particles (Shiba et al. 1979; Alarie et al. 1980; Schamber and Larock 1983; Lessard and Beck 1988). However, the assumption of an average velocity and the possibility to reliably predict it by column-settling tests are questionable. These tests often fail to predict or explain the behaviour of tanks under operating conditions and high scale-up factors 1.25-l .75 are required to compensate for the deviations from the real regime (WPCF 1985). In the absence of a more valid practical approach, the empirical models, sometimes called “regression

models”, can be helpful to the design of sedimentation tanks, either directly or after calibration with pilotplant performance data. Surface overtlow rate q=Q/A and influent SS concentration, S, have long been recognized as important factors in SS removal. Sometimes, retention time, 8, is used instead of q

(2) where V is the liquid volume and H is its average depth in the tank. The higher SS concentrations result in higher collision rates between particles and consequently in better flocculation. However, the influence Of Si and q, when studied on the basis of hourly or daily performance data, is often overshadowed by the random effect of the previously mentioned factors. Only when the study is carried out with averaged data over adequately long periods, several weeks at least, the random variations are smoothed out and the dependence of the SS removal efficiency, E,, on q and Si becomes clear. Temperature, t, is an important factor in sewage sedimentation. It affects the settling velocities as well as the velocity gradients in the liquid which in turn affect flocculation. It seems that the ultimate effect of temperature on tank efficiency is less important at the higher SS concentrations (WPCF 1985). The empirical models have generally been restricted to relate the SS removal efficiency, E,, to q (or 0) and Si. Steel (1960) proposed a diagram relating E, to 8 and Si. A similar graph was offered by Fair and Geyer (1954), but in neither case were there any details regarding the source of the data and the range of conditions covered by the data. Smith (1969) proposed the following relationship based on data from a large number of full-scale PSTs in the U.S.A. E, = 0.82 exp (-0.0088q)

(3)

The data (q, E,) were characterized by great scatter, which might be explained by the fact that Si and t had not been taken into account. Data from a number of full-scale plants were examined by Escritt (1972), who derived the following relationship:

An empirical sedimentation

se =

model

927

‘i

_St? = o.4 q -0.09 . S -“.42 . @/~)-0.26

c, eqlog

si

$

logsi

(5)

2

and C, = 1.1, C, = 10 with 0 measured in h. The relationship published by CIRIA (1973) was developed from the analysis of data obtained from a number of large sewage works in the London area and which covered the rather narrow q range 6-33 m d’. E, = [0.00043 Si + 0.511 [ 1 - exp (0.70)]

(6)

Based on data from a pilot-plant of the University of Birmingham, U.K., Tebbutt and Christoulas (1975) proposed the following empirical model:

E, = aexp(-S

b

(8)

‘i

where Tj =

i

(4)

- cq)

where D is the tank diameter. Equation 8 suggests that an increase in the tank depth, H, would result in lower removal efficiencies, a conclusion of questionable validity. The study of performance data from several PSTs led Anderson and Mun (198 1) to the conclusion that the SS removal Si-S, was proportional to the concentration of the so-called “settleable” solids. The empirical models have not included temperature, t, as one of their parameters, which may, in part, explain the different results given by different models. An indication about the influence of temperature on PST performance is given by the following relationship (WPCF 1985; Metcalf and Eddy 1991):

= 1.82 exp (-0.03t) 8 20

(9)

(7)

I

The pilot-plant, 2.1 m in diameter with a maximum water depth of 2.8 m, received domestic sewage for continuous periods of 15-20 weeks at overflow rates, q, of 25, 50, 100, and 150 m d-l. Employing double regression analysis to the pilotplant data for Si~200 mgL_‘, Eq. 7 gives a=l.l2, b=358 mg L-l, and c=O.O020 m-l d with correlation coefficient x=0.94. Tebbutt and Christoulas (1975) found that Eq. 7 provided a satisfactory description of the E,-q relationships given by the aforementioned models of Smith, Steel, Escritt, and CIRIA, and also of the E,-Si relationships given by Steel and Escritt. However, significant differences in the derived values of a, b, and c were observed, which were attributed to differences in sewage composition and temperature. Later on, Tebbutt (1979) observed that Eq. 7 provided a good description (r=O.95) of the full-scale data of White and Allos (1976), and that it compared well with data obtained in Edinburgh, U.K. Annesini et al. (1979) proposed the following regression model based on published performance data from various pilot and full-scale primary sedimentation tanks:

where 0,, and 8, are the retention times needed for sewage temperature t=2O”C and t<20”C, respectively. In 1985, a pilot-plant study of the treatability of Athens sewage was carried out (Christoulas et al. 1985). The pilot-plant was situated at the Acrokeramos site close to the point where 600 000 m3 d-’of sewage were being discharged to the sea. The plant included secondary treatment units following two primary sedimentation tanks 2.0 m in diameter, operating in parallel, one with a constant q=48 m d-’ and the other with varying q in the range 24- 120 m d-‘. Table 1 gives the experimental results for q=48 m d-’for various Si ranges during the winter period (February and March 1985). The model 7 was adopted, which is linear in terms of InE,, l/Si, and q. Regression analysis of the data in Table 1 gave the following relationship with r=O.98:

Es = Bexp

(-352) ‘i

(10)

where B=aexp(-cq)=

1.141

ill)

The average sewage temperature during February and March 1985 was 15.9”C, not much different from the

D.G. Christoulas et al.

928

Table l.AveragedatafromtheAcrokeramospilot-plant(q= Si range

<300 301-400 401-500 501-600 601-700 701-800 >801

Daily observations

Si

Es

6 11 16 6 4 4 II

258 351 445 559 658 766 1212

0.27 0.46 0.50 0.66 0.68 0.70 0.80

48mb’).

average temperature of Birmingham sewage (about 15 “C). The b values were also not very different, b=352 for Acrokeramos and b=358 for Birmingham. This implies that temperature and SS concentration are possibly the only sewage parameters which can cause significant variations in the average performance of sedimentation tanks treating municipal wastewater. Regression of E, on q gave c=O.O030. By applying c=O.O030 and q=48 in Eq. 11, the value of the coefficient a (model 7) was derived, equal to a=l.320. During summer, the Acrokeramos pilot-plant encountered operational problems which seriously limited the amount of reliable performance data. However, the influence of sewage temperature on SS removal was prominent. It appears that the general model 7 (Tebbutt and Christoulas 1975) satisfactorily describes the dependence of E, on q and Si. Additional research work on its validity would be useful, but the main problem is the variability of the coefficients a, b, and c with the source of sewage, which greatly reduces the usefulness of the model. Relationships between a, b, and c and influential and predictable sewage characteristics would be needed for the model to become a useful design tool. With the exception of S, it seems that the most significant sewage characteristic is temperature. The opportunity for the needed additional research work was given within the framework of the Athens Water Corporation (EYDAP) and NATO Science for Stability project involving the Metamorphosis PilotPlant (MPP). The research was carried out by these authors. The MPP included two fully equipped primary sedimentation tanks followed by secondary treatment units. The tanks operated in parallel for one year (December 1993-November 1994) with municipal waste waters. Temperature was systematically measured along with the other typical sewage characteristics.

In this paper, the MPP performance data are presented and analyzed separately and jointly with the data of the Birmingham and Acrokeramos pilot-plants and also with other comparable published data. The main objective of the paper was to further examine the validity of model 7 and to study the influence of the temperature and other possible factors on the coefficients a, b, and c of the model. The relationship between SS and COD removal efficiencies was also studied.

EXPERIMENTAL DETAtLS The MPP is situated close to the Metamorphosis

Sewage Treatment Plant (MSTP) where about 10 000 m3 de’of sewage, originating from the northern suburbs of Athens, are subjected to primary and secondary treatment along with 10 000 m3 d-’of septic sewage. The MPP primary sedimentation units consist of two centre-fed circular tanks, 4.1 m in diameter, with minimum side and maximum water depths of 3.2 and 4.0 m, respectively. The tanks are equipped with cylindrical inlet baffles 1.0 m in diameter and rotating sludge scrapers. They are fed with fresh municipal sewage which is diverted from the main sewer leading to the MSTP. Prior to primary sedimentation, the sewage is subjected to screening and grit removal. One of the tanks (No. 1) was subjected to variable hydraulic loadings in the range 23-80 m d-‘. The other tank (No. 2) was to operate under a constant hydraulic loading of q=20 m d-l, but during the summer of 1994, the sewage diversion device failed to supply the pilot-plant with adequate flow, resulting in a loading value as low as 7.9 m d-‘. However, the deviation from q=20 m d-’was too small to seriously affect the SS removal efficiency for these low q values in view of the fact that E, is approximately an exponential function of q. Throughout the experimentation period (December 1993-November 1994), 24-h composite influent and effluent samples were analysed daily for the determination of SS, VSS, TS, 5-d BOD and COD. All analyses were conducted according to Standard Methods (APHA 1989). The results of the pilot-plant studies, averaged’over periods of time and within q, t, and Si ranges, are presented in Tables 2-5. E,, and E, denote, respectively, VSS and COD removal efficiencies.

An empirical sedimentation

model

929

Table 2. Average MPP performance Sedimentation Period

Si

t

a,

S.,

data over periods of time.

tank 1 E.,

Sedimentation a,

tank 2

S -7

E.,

C

18-31 Dec. 1993

332

20

40.20

208

0.37

16.70

182

0.45

0.0830

1-21 Jan. 1994

231

20

60.00

201

0.13

20.95

160

0.31

0.0076

22-3 1 Jan. 1994

280

20

42.60

157

0.44

19.00

145

0.48

0.0146

1-l 1 Feb. 1994

253

18

52.90

159

0.37

18.91

160

0.37

0.0000

12-28 Feb. 1994 1-16 Mar. 1994 17-31Mar. 1994 l-10 Apr. 1994 1I-20 Apr. 1994 21-30 Apr. 1994 1-31 May 1994 l-30 June 1994 1-12 July 1994 13-18July 1994 19-26July 1994

271

18

68.50

183

0.32

22.00

158

0.42

0.0058

308

19

72.50

222

0.28

23.63

178

0.42

0.0083

373

19

56.23

197

0.47

19.67

158

0.58

0.0057

325

19

50.00

174

0.46

16.60

155

0.52

0.0037

319

20

80.25

194

0.39

24.70

168

0.47

0.0034

353

22

68.40

211

0.40

23.00

176

0.50

0.0049

284

23

79.50

191

0.33

26.32

153

0.46

0.0062

294

25

78.97

188

0.36

26.33

160

0.46

0.0047

291

26

81.00

184

0.37

26.92

164

0.44

0.0032

256

26

39.00

150

0.41

13.00

176

0.31

-0.0107

217

26

26.25

144

0.34

8.75

142

0.35

0.0017

27-3 1 July 1994

274

26

40.25

157

0.43

13.25

129

0.53

0.0014

1-31 Aug. 1994

259

25

23.29

143

0.45

7.90

125

0.52

0.0094

l-30 Sept. 1994

263

26

23.60

157

0.40

7.87

136

0.48

0.0116

1-31 Oct. 1994

309

25

42.76

170

0.45

14.26

147

0.52

0.005 1

l-30 Nov. 1994

300

21

57.00

172

0.43

19.00

163

0.46

0.0017

Table 3. Average MPP performance q range m/d

Daily observations

data within q ranges (Tanks

tot zd

1 and 2).

Si mg/L

Se mg&

Es

06- 10

63

24.60

7.86

270

134

0.51

11-18

68

20.15

14.76

324

160

0.50

19-22

63

21.07

20.99

299

154

0.49 0.43

23-25

76

22.86

23.76

262

150

26-29

80

21.62

27.11

297

159

0.46

30-50

57

21.04

39.90

322

175

0.46

51-74

58

21.32

62.74

295

183

0.38

75 - 105

60

21.91

82.02

295

194

0.34

RESULTS AND DISCUSSION

Removal of SS Mathematical model: Equation

7 was adopted as the basic model, with the a priori recognition that at least the coefficient b is temperature-dependent. This coefficient is a measure of the influence of Si on sedimentation through flocculation and higher b values reflect an increased influence of Si on E,. However, at

higher sewage temperatures, the particle-settling velocities increase, and consequently the effect of flocculation and Si on SS removal becomes less significant. Therefore, the coeffkient b should decrease as temperature increases in such a way as to compensate also for the direct positive effect of temp erature on sedimentation. The possible dependence of the other two coeffkients, a and c, on t should also be investigated.

D.G. Christoulas et al.

930

Table 4. Average MPP perfotmance data within t and Si ranges (Tank 2). 20
t r; 20°C Average t=19.60°C

Si range

Daily obs.

< 23°C

t>23YJ

Average t=22.00°C

Si

&

Daily obs.

range

Average t=2540°C

Si

Es

Si range

Daily obs.

Si

Es

130-219

16

189.69

0.27

102-240

13

193.9

0.30

158 - 238

20

206.95

0.42

220-264

16

247.00

0.35

241 -276

13

260.4

0.42

239-259

19

252.21

0.48 0.48

265 -302

16

281.81

0.41

277-298

14

290.2

0.49

260-276

20

266.65

303 - 340

17

3 19.29

0.45

299-338

14

319.7

0.50

277 - 294

19

284.95

0.45

341-374

17

356.65

0.53

339-532

14

417.6

0.56

295 - 308

18

300.06

0.48

375-580

17

445.12

0.60

309-376

20

344.85

0.50

377-740

18

485.39

0.64

Table 5. Average MPP performance Si range

data within Si ranges (Tanks 1 and 2).

Si

VSS

CODi

Es

E,,

EC

122-208

186

155

577

0.36

0.23

0.16

209 - 230

222

196

649

0.35

0.29

0.13

231 - 249

242

208

654

0.33

0.27

0.18

250 - 259

255

208

670

0.39

0.33

0.20

260 - 264

262

220

676

0.44

0.42

0.20

265 - 272

268

229

708

0.42

0.36

0.19

273 - 283

278

240

642

0.40

0.40

0.19

284 - 295

287

237

718

0.39

0.39

0.21

296 - 301

297

254

726

0.47

0.47

0.27

302-311

304

257

818

0.41

0.37

0.17

312 - 327

319

271

756

0.45

0.43

0.22

328 - 347

337

284

819

0.45

0.45

0.25

348 - 371

357

279

742

0.47

0.45

0.21

372 - 424

392

324

877

0.52

0.48

0.27

425 - 1024

524

376

973

0.64

0.58

0.41

E@ct of surface overfzow rate: Under ideal flow conditions, the performance data of the two, operating in parallel, sedimentation tanks No. 1 and No. 2, should clearly show the effect of the surface overflow rate, q, on the SS removal efficiency, E,. However, short circuiting and other flow instabilities may overshadow the effect of q. It appears that this was the case during the periods 1- 11 February and 13- 18 July when, in spite of the much lower q values of tank No. 2, the SS removal efficiencies observed were equal and even smaller than the ones of tank No. 1 (Table 2). This probably explains why the c values which appear in Table 2 vary so much from period to period. They were calculated by applying Eq. 12, derived from Eq. 7:

In

2 =c(q,

-q*)

(12)

sl

where the indices 1 and 2 refer, respectively, to tanks No. 1 and No. 2. By using average performance data over various q ranges (Table 3), it can be expected that the average t and Si values will not vary significantly and that the effect of the flow anomalies (short circuiting and circulating flows) will be equalized. With t and Si values being nearly constant, Eq. 7 can be written as follows:

An empirical sedimentation model

931

9

W

z -

c

0.8

-

0.6

0

50 q

100

(m/g

Fig. 1. Effect of surface overflow rate on SS removal efficiency.

E, = Aexp (-cq)

(13)

or 1 In = -InA Es

+ cq

(14)

Equation 14 satisfactorily fits the data of Table 3, with c=O.O052 m-l d (Fig. 1) and a correlation coefficient 1=0.95, significant at 99% probability level. Comparison between c and t values in Table 2 does not suggest any dependence of c on t. Tebbutt and Christoulas (1975) found that Eq. 14 provided a satisfactory description of the data given by Smith, Steel, Escritt, and CIRIA. The derived c values were 0.008, 0.0047, 0.0023, and 0.0123 m-l d, respectively. On the other hand, model 14 has been found to satisfactorily describe the data of the pilot-plants of Birmingham, Acrokeramos, and Metamorphosis with c values equal to 0.0020, 0.0030, and 0.0052, respectively. The differences in c values can be explained, in part, by the fact that the model takes into account only the surface area of the tank and not its detailed geometry, which must have an effect on the SS removal efficiency E,. The great scatter of Smith’s data and the low q values used in Escritt’s analysis raise questions about the reliability of the derived, rather high, c values 0.0088 and 0.00123, respectively. The range 0.0020-0.0050 suggested by the remaining five groups of data seems to be the normally expected one.

As Eq. 14 (or 13) suggests, calculations of E, with the average value c=O.O035 may lead to errors probably not exceeding 11% even for the high q=70 m d’. Further research on the influence of the tank geometry and inlet details on the c value could restrict the uncertainty of its choice in design. Effect of injluent SS concentration and temperature: The variation of the surface overflow rate in tank No. 2 was too small to seriously affect the SS removal. The differences between the low q values (q=8-20 m d-‘) cannot significantly affect the E, values, because of the exponential dependence of E, on (-9). Therefore, the performance data from this tank are suitable for studying the SS and temperature influence on sedimentation. The daily data were averaged in Table 4 over Si and t ranges. For each one of the three temperature intervals, the following equation can be written: E, = Bexp(-$)

(15)

I

or Ini

= In($) s

+ b(k) I

where B = a exp (-0.0052 q) (17)

932

D.G. Christoulas et al.

with q being equal to 19.7, 19.7 and 16.3 m d’ for the periods with t equal to 19.6”C, 22.O”C, and 25.4”C, respectively. Equation 16 provides a satisfactory description of the data presented in Table 4, as shown in Figs. 2,3, and 4 @average temperature in Celsius degrees). Linear regression analysis along with Eq. 17 produced the following results: for t-19.6 (average Si = 309): B=l .OSand b=268 with 1=0.99, a=l.20; for 1~22.0 (average Si = 298): Bcl.03 and b=234 with 1=0.99, a=l.14; and for t=25.4 (average Si = 304): B~0.79 and b=137 with r=O.89, a=O.86. All the correlation coefficients are significant at probability levels higher than 99%. The Birmingham pilot-plant had given a=1.12 and b=358 with t close to 15 “C and the Acrokeramos pilotplant had given a=1.32 and b=352, with t=15.9”C. The five points a-t and the five points b-t were plotted in Figs. 5 and 6. As expected, the coefficient b is strongly dependent on temperature. Regression analysis produced the following expression:

t =

0.002

t

t = 22.0

0.006

0.004

“c

0.005

0.006

l/S; Fig. 3. Influence of Si on E, for t=22.0 a C.

t=

25.4k

1 .la JJ_ 0.900.7 c

0.503 0.002

1

0.003

0.004

0.005

j/Si Fig. 4. Influence of Si on E, for t~25.4 ’ C.

organic matter

averaged E, and E, data over SS ranges (Table 5) show a good linear relationship (Fig. 8). Regression analysis produced the following relationship: E, = 0.783 E, - 0.12 (20) The

0.003

(18)

with 1=0.76 significant at 93% probability level. The monthly data derived from Table 2, the Acrokeramos data (Table 1) and the data from the Birmingham pilot-plant (Tebbutt and Christoulas 1975) suggest that the calculated (from Eqs. 7, 18, and 19 with c=O.O035) and the observed SS removal efficiencies in the three pilot-plants are in good agreement (Fig. 7). This is a good indication of the validity and general applicability of the empirical model consisting of Eqs. 7, 18, and 19 with c = 0.0035 m-’d.

0.005

Fig. 2. Influence of Si on E, for tz19.6 ’ C.

with r=O.99 significant at probability level higher than 99%. Figure 6 suggests that the coefficient a is a decreasing linear function oft. The following equation was derived: a= 1.71 - 0.03 t (19)

Removal of

0.004

?? C

i/Si

0.002 b=683.6-21.13

0.003

19.6

with r=O.93 significant at a probability level higher than 99%. Least square linear analysis was applied (Tebbutt 1979) to the data from the Birmingham pilot-plant. The following equation had been derived:

933

a

b

1.4 J 1.2 1 0.8

1; i

14

420

I -I

320, 220,

I

24

19

120 ,\

,

I

,

14

29

19

24

t

29

t

Fig. 5. Coefftcient a vs. temperature.

Fig. 6. Coeffkient

b vs. temperature.

1.00

0.80

0.20

0.00

0.00

0.20

0.60

0.40

Es

OBSERVED

Fig. 7. Calculated vs. observed ES values.

0.80

1.00

D.G. Christoulas et al.

934

0.30

0.40

0.50

0.60

0.70

ES

Fig. 8. Relation between SS and COD removal efficiency.

E, = 0.684 E, - 0.05

(21)

Equations 20 and 21 are not much different, though obtained from different data (Athens and Birmingham). Adopting the average values of the coefficients of Eqs. 20 and 21, a valid relationship between E, and E, can be derived in the form of Eq. 22. E, = 0.733 E, - 0.08 (22) CONCLUSIONS

Analysis of the performance data from the Metamorphosis pilot-plant combined with the data from the Acrokeramos (Athens) and Birmingham pilotplants has shown that a simple empirical performance relationship in the form: E, = a exp(-(b/S) - cq] can satisfactorily describe the average SS removal in terms of surface overflow rate and influent SS. The coefficients a and b were found to be temperaturedependent according to equations b=683.6-2 1.13 t and a=l.7 l-0.03 t, valid for temperatures in the range 15-26°C. It seems that ~0.0035 m-’ d, is an appropriate value and that equation E,=0.733 E,-0.08 provides a good estimate of the COD removal efficiency. REFERENCES Alarie, R.; McBean, E.; Farquhar, Gr. Simulation modeling of primary clarifiers. J. Environ. Eng. ASCE 106(2): 293-309; 1980. Anderson, J.A.; Mun, M. Primary sedimentation of sewage. Water Pollut. Control U.K. 80: 413-420; 1981. Annesini, C.; Beccari, M.; Mininni, G. Solids removal efficiency of primary settling tanks in municipal wastewater treatment plants. Water Air Soil Pollut. 12: 441-447; 1979.

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