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Viability of microbial mass in compartmentalised single activated sludge process
Water Res, Vol, 19. No. 7. pp. 923'-932, 1985 Printed in Great Britain. All rights reserved
0043-1354 85 $3.00 +0.00 Copyright C~ 1985 Pergamon Press Ltd
VIABILITY OF MICROBIAL MASS IN COMPARTMENTALISED SINGLE ACTIVATED SLUDGE PROCESS IQBAL ALl, H. KHARARJIAN and MUNIR AHMED University of Petroleum and Minerals, Dhahran. Saudi Arabia ( Receh'ed December 1984)
Abstract--Investigations were carried out for steady-state conditions on laboratory-scale model of an anaerobic-aerobic-anaerobic-aerobic configuration of the activated sludge process, for hydraulic detention time of 9.13 h and sludge age of 9, 16.4 and 28.3 days. Data was obtained on volatile suspended solids (MLVSS), adenosine triphosphate (ATP). oxygen uptake rate (OUR) and relative dehydrogenase activity (RDA) on the laboratory model and the extended aeration biological treatment unit at the ARAMCO treatment plant in Dhahran. A mathematical model was obtained based on the laboratory data, using a calibration method to estimate MLVSS for given values of OUR, RDA and ATP. Regression coefficients for MLVSS and OUR, MLVSS and RDA and MLVSS and ATP were determined. It was found that the mathematical model obtained does not adequately describe the inter-relationship between various parameters. The inter-relationship is a function of operational conditions occurring in the plant and too complex to be described by a mathematical model. MLVSS offers the best estimate and cannot be related by an equation with OUR, ATP and RDA. Key words--single activated sludge, microbial mass, MLVSS, OUR, ATP, RDA, cell count, steady-state
INTRODUCTION According to the present day practice volatile suspended solids (MLVSS) are the main indicator for the viable biomass concentration in an activated sludge process. Since the dead mass contained in the MLVSS does not contribute to biological treatment, therefore, many a time this may not provide a correct indication of treatment efficiency. Adenosine triphosphate (ATP), oxygen uptake rate ( O U R ) and relative dehydrogenase activity ( R D A ) have been investigated as control parameters in an attempt to use them as indicators of efficiency and control parameters of a biological treatment. Their determination calls for a sophisticated laboratory process which may not be available in every treatment plant. Numerous models of the activated sludge process have been developed, including a parameter expressing the active biological solids for which MLVSS has been used. However, since only a small percentage of the organisms in activated sludge are viable (Weddle and Jenkins, 1971), the validity of such models becomes questionable. Investigations have been done to measure R D A , ATP, O U R and cell count as viability parameters. Tebbut and Parakevopoulos (I 981) state that these parameters do not provide a satisfactory measure of the sludge activity. ATP was found proportional to viable cell count under steady-state conditions in activated sludge by Weddle and Jenkins (1971). They concluded that ATP can be an appropriate biomass parameter for the growth rates commonly encountered in activated sludge plant operation. 927 WR. l q T - - H
Levin et al. (1975) concluded that A T P analysis could be employed to control mixed liquor biomass through regulation of return sludge. Roe and Bhagat (1982) show that ~o viability (ATP/SS ratio) decreases with cell residence time. They conclude that although A T P was a good indicator o f the biokinetic and settling characteristics of activated sludge under laboratory steady-state conditions, there is need of research for shock loadings. O U R has been a c o m m o n method for measuring activity rates of sludge. Weddle and Jenkins (1971) point out that O U R expressed on MLVSS basis reflect the viable content of the sludge and is independent of net growth rate when expressed on a viable cell basis. Edwards and Sherrard (1982)concluded that specific O U R cannot be used as a control parameter for the activated sludge process, when treating a highly variable influent quantity and quality of flow. Sherrard (1980) citing the study of Duggan and Cleasby (1976) has opposed the use of O U R as a valid control parameter on the basis of highly variable and fluctuating O U R values with little or no change in effluent quality. In a recent study Huang and Cheng (1984) concludes that O U R reflects the extent of microbial activity and specific O U R of mixed liquor can be used to predict the final effluent soluble C O D concentrations during transient loading condition. R D A has been used by Lenhard et aL (1964) and Bucksteeg (1966) to measure oxidising capacity of sludge. Tebbut and Paraskevopoulos (1981) conclude that R D A is not a particularly satisfactory method to measure the sludge activity because many factors
928
IQBAL ALl et al.
affect the analytical results. They suggest the use o f M L V S S for routine assessment o f the activity o f the activated sludge process since the use o f specialised parameters to describe the viability o f microorganisms in wastewater treatment process is o f questionable validity. Roy et al. (1983) carried out studies and collected data for a year on a full-scale domestic wastewater treatment plant at Quebec, to investigate any relationship between ATP or A T P pool and other biomass-related variables, operating condition or BOD reduction. Their conclusion in this regard is " A T P did not correlate well with biomassrelated variables but low values did seem to indicate p o o r aeration performance". In view o f the above background, there is need for a comprehensive study involving different hydraulic detention times and sludge ages to arrive at a positive conclusion. This paper presents another attempt to f i n d - - i f a n y - - a correlation between MLVSS on the one hand and O U R , ATP and R D A on the other based on steady-state condition o f a labora;:ory-scale model operated at hydraulic detention time o f 9.13 h and sludge ages o f 9, 16.4 and 28.3 days. The calibration analysis was performed for three tanks and tested for the data obtained from N o r t h A r a m c o treatment plant for almost the same range o f hydraulic detention times and sludge ages. MATERIALS AND METHODS A schematic diagram of the laboratory-scale model and the arrangement of anaerobic and aerobic compartment is shown in Fig. 1. This arrangement was adopted based on the results of the investigations (Khararjian and All, 1982) which indicates highest nitrogen removal efficiency for such configuration. The unit was started by feeding the activated sludge seed from the North Aramco treatment plant in Dhahran, and then onwards 28.41 day-~ of degritted sewage was supplied to the tank. Harvard Peristaltic pump supplied the system at an average rate of 19.74mlmin-L The
/
wastev, ater with the return sludge flowed through the eight compartments and finally entered the sedimentation basin. Each compartment was 9cm i.d., with an average water depth of 21.06 cm and a total measured volume of 10.804 I., for the eight compartments, giving a hydraulic detention time of 9.13 h. The effluent was collected in 25 I. containers while the settled sludge was pumped back into the first compartment by the Harvard Peristaltic pump at a recirculation ratio of 70°~. Each day activated sludge was wasted from eight compartments. The amount wasted depended on sludge ages of 9, 16.4 and 28.3 days. The steady-state was assumed when MLSS and MLVSS were relatively constant in the system. Figure 2 gives a typical graph showing the period during which steady state conditions were assumed.
SAMPLI~!G AND ANALYSIS Grab samples were collected for each day from tanks, 1, 2 and 8. and were analysed for MLSS, MLVSS, OUR (mg 1- I day- t ), ATP (/~g 1- i ) and RDA (# tool 1- t ). For mixed liquor volatile suspended solids (MLVSS) well mixed samples were filtered through Whatman GF/C2. I cm dia glass fibre filters in 25 ml Gooch crucibles. The samples dried at 103~C for I h were burned for 20min in Muffle furnace at 550C. The procedure followed is in accordance with Standard Method~ (APHA, 1975). This sample volume was part of the daily sludge wastage. For determining oxygen uptake rates, a standard 300 ml BOD bottle was filled with mixed liquor from the reactor of interest and dissolved oxygen was read using YSI model 51B dissolved oxygen meter with dissolved oxygen probe. After measurement the mixed liquor was returned into the respective tanks. Modified Lenhard and Nourse method by Ford et al. (1966) using ITC (triphenyltetrazolium chloride) as hydrogen acceptor was used for the determination of dehydrogenase activity. ATP was measured by Firefly Luminescence Assay method as described by Strehler and Totter (1974) using Analytical Luminescence Laboratory Model 401 Fluorometer. Grab samples from influent and effluent were also obtained from the North Aramco treatment plant and analysed.
/ko'o 1.o,o
Feed
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tank
A
J
I
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i
A
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i,
Fe
pump I Compartmentalized activated sludge units
t
Effluent collection
tank
/R
Recycle pump Anoxic basin
Inf 'uent ..----,-,r~~
E,,,uen, - - -
0
0
@
0 0 @ @ Section A-A
Fig. 1. Schematic diagrams of the laboratory unit and the anaerobic-aerobic arrangement evaluated.
Viability of microbial mass in compartmentalised single activated sludge process
929
E
u~
30.000 i C
20,000
i 0,000
I 2
1
I
1
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4
6
8
10
12
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46
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I
10 20 Doys
I
l
I
I
I
I
22
24
26
28
30
32
I 34
F 36
Fig. 2. Typical variation of MLSS, MLVSS in the system showing steady-state.
MATHEMATICAL MODEL
In an experiment, if X is an independent variable and Y is a dependent variable in a linear model Y , = ~ + f l X i + ~ ,,
i=1,2 ..... n
(1)
where ei is assumed independent and identically normally distributed random variable with mean zero and variance ,~-', and if we estimate a value of J," for a given value of Y0 using X" = Y 0 - ~/fl
(2)
laboratory-scale model, correlating MLVSS, with O U R , RDA, A T P for each tank and three different sludge ages of 9, 16.4 and 28.3 days. Table 4 provides the correlation for X (MLVSS) and Y (OUR, R D A and ATP) values combining and averaging the three sludge ages, for three tanks. Table 5 shows corn-
;'oo = 6oo
)0
0
I
When ~( and fl are least square estimates, the method is called a calibration method (Graybill, 1976). MLVSS was calibrated with OUR, RDA and ATP for each of the three tanks and each sludge age. In the calibration experiment the MLVSS (Xi) is controlled at n levels and the corresponding readings of Y~ i.e. OUR, R D A and ATP observed. Using these n data points (Xi, Y~) least square estimates of ~t and fl are obtained on the basis of model (1). The fitted lines for ~t and fl are given in Table 3. The MLVSS (X;) values are estimated from particular values of Y (OUR, R D A or ATP) using equation (2). Table 3 provides the equations correlating MLVSS (X) and OUR, RDA and ATP for the laboratory data. It also gives correlation coefficient. Figure 3 gives some of the fitted lines.
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RESULTS
Steady-state data for 9.13 h for detention time and sludge age 9, 16.4 and 28.3 days are given in Table 1 for tanks 1, 2 and 8. The data consist of MLVSS, and corresponding values of OUR, R D A and ATP. Table 2 provides the same parameters from the Aramco treatment plant which has an average hydraulic detention time of 10-14 h and has an extended aeration system. Table 3 gives the equations based on
00
°
120
60
400
(
t
420
440
S 460
t 480
.,[ 500
t
t
I
520
540
560
Fig. 3. A typical set of fitted lines for tank 2, sludge age 9 days. Similar lines were obtained using computer graphics for tanks 1, 2 and 8, sludge ages of 9, 16.4 and 28.3 days.
930
IQBAL ALl et al.
Table 1. Steady-state data for laboratory model for 9.13 h detention time, sludge ages 9. 16.4 and 28.3 da~s
Sludge age 9 Days
Tank I 2 8
16.4 Days
I 2 8
28.3 Days
l 2 8
Value Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
MLVSS ( m g l -q)
OUR ( m g l - ~ d a y -~)
RDA ( v m o l l -I)
ATP ( # g l -~)
Relative OUR (rag r a g - ' day -~)
731.40 160.65 482.20 59.89 677.60 147.93 1219.00 341.00 1070.00 162.48 1559.00 336.10 2886.00 446.27 2768.00 246.30 3210.00 260.17
--530.10 97.96 122.50 18.60 --620.10 129.08 249.80 56.78 --952.70 323.27 485.40 64.61
7.60 1.50 7.24 1.55 10.51 2.90 81.83 66.2t 60.80 44.43 79.90 57.47 303.00 85.50 199.60 82.24 248.10 107.76
140.80 69.80 152.70 51.41 263.20 120.81 269.60 144.38 380.80 275.57 460.80 169.76 597.80 309.80 667.10 390.78 720.89 333.38
--1.11 0.24 0.19 0.04 --0.60 0.t7 0.16 0.04 --0.34 0.11 0.15 0.02
parison between the measured values of MLVSS from the North Aramco treatment plant for 5 days and predicted values based on mathematical model. This gives an idea how closely MLVSS can be predicted for the measured values of OUR, RDA and ATP by the mathematical model developed from the laboratory data.
Relative RDA (,amol mg -~ MLVSS)
Relative ATP l u g m g -* of MLVSS)
0.01 0.003 0.02 0.003 0.01 0.003 0.07 0.05 0.05 0.03 0.05 0.03 0.11 0.03 0.07 0.03 0.15 0.02
0.21 0.12 0.31 0.09 0.40 018 0.23 0.11 0.35 0.23 0.30 0.10 0.2t 0.11 0.25 0.17 0.22 0.10
DISCUSSION
The correlation coefficients as shown in Table 3 for RDA in case of tank 1 are 0.436, 0.424 and 0.237 for 9, 16.4 and 28.3 days, respectively, while for tank 2 they are 0.068, 0.738 and 0.081 and for tank 8 they are 0.486, 0.363 and 0.005. This clearly indicates that
Table 2. Aramco treatment plant data for five consecutive days MLVSS ( m g l -I)
OUR ( m g l - ' d a y -t)
RDA(#moll -t)
A T P ( . u g l -I)
Days
Initial
Final
Initial
Final
Initial
Final
Initial
Final
I 2 3 4 5
1815 2020 2060 2160 1820
t760 2100 2030 2050 1810
1120 864 1210 1188 734
380 528 346 304 245
69.0 71.0 42.0 65.2 74.6
124.4 79.8 37.3 84.0 88.7
1107 1177 1071 1132 1104
tl31 1196 870 1085 1130
Table 3. Mathematical models for individual sludge ages Tank No.
Sludge age
Tank t (Anaerobic)
9 days (n = 10) 16.4 days (n = 105 28.3 days (n = 105
.v, = y~ = Y2 = Y3 = y: = Y3 =
4.83 + 0.0038x 259.7 -- 0.1626x - 18.39 + 0.0822x 206.61 + 0.0517x 173.22 + 0.046x 593.44 + 0.0015x
0.436 -0.374 0.424 0.112 0.237 0.0022
Tank 2 (Aerobic5
9 days (n = 10)
),~ = y., = Y3 = y~ = y., = ),~ = Yt = y., = 1,~ =
459.73 + 0.146x 6.51 + 0.0017x -- 146.64 + 0.621x 697.34 -- 0.0722x -- 155.18 + 0.202x --214.10 + 0.556x --764.70 + 0.620x 274.8 -- 0.028x -- 2726.33 -- 0.7439x
0.089 0.068 0.723 -0.091 0.738 0.328 0.473 -0.081 - 0.469
16.4 days (n ~ t0) (n = 10) 28.3 days (n ~ I0) Tank 8 (Aerobic) (Last in the series)
Model
Correlation coefficient
9 days (n = 10)
Yt = 67.72 + 0.08Ix y., = 4.32 + 0.0089x Y3 = 130.85 + 0.195x
0.643 0.486 0.239
16.4 days (n ~ 10)
y~ = y, = Y3 = y~ = v., = )'3 =
0.404 0.363 0.275 0.457 0.005 0.202
28.3 days (n ~ 105
143.43 + 0.0682x - 16.92 + 0.062x 244.21 + 0.139x 120.96 + 0.| 13x 241.74 + 0.002x 83.86 + 0.263x
x = MLVSS; yt = O U R (rag 1-t d a y - ' ); y: = R D A ( ~ m o l I- ~); y~ = A T P ( # g I-~ 5; n = number o f observations.
Viability of microbial mass in compartmentalised single activated sludge process
931
Table 4. Mathematical model based on n = 30 for tanks I. 2 and 8. combining and averaging the three sludge ages of 9. 16.4 and 28.3 days Tank Model Correlation coefficient Tank 1 (Anaerobic) N = 30
y: = 32.9 + 0.0613x Y3~ 192.6 + 0.0890x
0.619 0.464
Tank 2 (Aerobic) n = 30
y~ = 422.5 + 0.1933x y., = -28.0 + 0.0824x yj = I 15.5 + 0.1977x
0.707 0.845 0.684
Tank 8 (Aerobic) n = 30
y~ = -799.2 + 0,5867x y, = -54.2 + 0.0920x )3 = 145.7 + 0.1924x
0.54 0.825 0.700
For explanation of the symbols x. )'~. y., and )'j. refer to Table3.
s l u d g e a g e d o e s n o t i n f l u e n c e t h e v a l u e s o f 'y a n d fl in a n y set p r e d i c t a b l e m a n n e r . T h i s is true for A T P for all t h e t a n k s a n d O U R for t a n k s 2 a n d 8. T h i s i n d i c a t e s t h a t c o r r e l a t i o n o f M L V S S with e n z y m e activity R D A a n d A T P is a n u n p r e d i c t a b l e f u n c t i o n o f s l u d g e a g e a n d also o f a n a e r o b i c a n d a e r o b i c a r r a n g e m e n t s . T h i s also a p p l i e s for t h e c o r r e l a t i o n b e t w e e n M L V S S a n d O U R as c a n be seen for t a n k s 2 a n d 8. H o w e v e r , t h e r e is a m a r k e d i m p r o v e m e n t in t h e c o r r e l a t i o n coefficient for O U R f r o m t a n k 2 a n d t a n k 8. T a n k 8 is t h e last in the series. F o r t a n k 2 the v a l u e s for t h r e e s l u d g e a g e s are 0.089, - 0 . 0 9 1 a n d 0.473. T h i s is e x p e c t e d as t h e r e o c c u r s c o n t i n u o u s r e d u c t i o n in B O D in t h e s y s t e m . T h e d a t a for e a c h t a n k irrespective o f t h e s l u d g e a g e s w a s c o m b i n e d a n d lines were fitted g e t t i n g a n o t h e r set o f v a l u e s for ~ a n d /L T h i s set o f e q u a t i o n s i g n o r e s t h e s l u d g e a g e effect a n d is g i v e n in T a b l e 4. It w a s t h o u g h t t h a t s u c h a n e q u a t i o n c o v e r i n g t h e d a t a w h i c h s p r e a d o v e r 9 - 2 8 . 3 clays s l u d g e a g e m a y p r o v i d e a m o d e l to a d e q u a t e l y describe M L V S S w i t h e n z y m e activity or O U R , T h e c o r r e l a t i o n coefficient v a l u e s in g e n e r a l a r e very m u c h
h i g h e r t h a n t h o s e o b t a i n e d in i n d i v i d u a l e q u a t i o n for e a c h s l u d g e age. The measured values obtained from Aramco treatment plant for MLVSS, OUR, RDA and ATP are s h o w n in T a b l e 2. T h e A r a m c o t r e a t m e n t p l a n t is a n a c t i v a t e d s l u d g e t y p e w i t h a n e x t e n d e d a e r a t i o n syst e m a n d a s l u d g e a g e o f 20 d a y s a n d e s t i m a t e d h y d r a u l i c d e t e n t i o n t i m e o f 1 0 - 1 4 h. T w o sets o f d a t a for initial a n d final c o n d i t i o n s f r o m t r e a t m e n t p l a n t c o r r e s p o n d to t a n k s 2 a n d 8 o f t h e l a b o r a t o r y - s c a l e m o d e l respectively. T h e m e a s u r e d v a l u e s o f O U R , R D A a n d A T P f r o m t h e p r o t o t y p e were fed i n t o t h e e q u a t i o n s to find t h e v a l u e s o f M L V S S . T a b l e 5 shows a comparison between the predicted values of MLVSS based on the measured values of OUR, R D A a n d A T P , a n d t h e M L V S S v a l u e s m e a s u r e d at the plant. F r o m T a b l e 5, in t h e c a s e o f t a n k 2, p r e d i c t e d v a l u e s o f M L V S S f r o m O U R m e a s u r e m e n t s are, f r o m a b o u t 13 to I 0 0 ~ h i g h e r t h a n t h e m e a s u r e d v a l u e s as o b t a i n e d f r o m t h e p r o t o t y p e . O n l y t w o o u t o f five p r e d i c t e d v a l u e s u s i n g O U R a r e w i t h i n 1 3 ~ o f t h e m e a s u r e d values. In t h e c a s e o f t a n k 8, t h e r e is a
Table 5. Showing predicted values of MLVSS as determined by the equations (Table 3) and measured values of MLVSS from Aramco Treatment Plant (tanks 2 and 8 in the laboratory model correspond to Initial and Final data of Aramco Treatment Plant respectively) Tank No. 2 ATP =yj (,ugl -a ) RDA = y , (,umol I-I ) O U R = y j (mgl-Jday -t) y; = 115.5 -4-0.1977x Yz ~ - 2 8 + 0.0824x y~ = 422.5 + 0.1933x x = MLVSS mg/ measured values (Aramco Plant) x = MLVSS Yl measured x ~ MLVSS y, measured x = MLVSS ),~ measured Initial predicted Aramco predicted Aramco predicted Aramco 1815 2020 2060 2160 1820 x = MLVSS Final 1760 2100 2030 2050 1810
3608 2284 4074 3960 1613
1120 864 1210 1188 734,4
OUR Yt = -779.2 + 0.586 1976 2228 1918 1846 1746
380 528 346 304 245
:
1177 69 1198 71 850 42 792 65.2 1245 74.6 Tank No. 8
5015 5369 4834 5144 4999
RDA y, = -54.2 + 0.092x
ATP ).~ = 145.7 + 0.1924x
1351 1456 I014 1502 1552
5121 5458 4522 4881 5116
124.4 79.8 37.3 84.0 88.7
1107 I 177 1071 1132 1104
1131 l 196 870 1085 1130
932
[QBAL ALl et al.
better correlation, which may be due to lower B O D in the system. This indicates that only for tow values of BOD the model adequately describes O U R - M L V S S relationship, and cannot be generalised to include the initial stages when BOD is high. Measured R D A values when plugged in the equations provided a lesser range of variations with the predicted values of MLVSS as compared to O U R and ATP. The variation ranged between as high as 63°0 to as close as 14,,O/o. Better correlation for tank 8 may again be attributed to low BOD is in the system in the final stages of treatment. It is obvious that R D A - M L V S S correlation is poor and cannot be relied upon irrespective of the operational and environmental conditions. The predicted values of M L V S S differ widely from the measured values, up to 182% as seen in Table 5. ATP provides the worst estimates for MLVSS for both the tanks 2 and 8, and hence cannot be used as a parameter to indicate the biomass in an activated sludge process. The correlation coefficient values for the combined data equations (Table 4) are much better than the correlation coefficients for the individual sludge age equation. However, this does not reflect any improvement as far as modelling is concerned. It is due to the mathematical reason of having derived the equation based on a larger collection of data (30 observations, 10 for each of the three sludge ages). This shows that the effect of sludge cannot be ignored. The above discussion indicates that inter-relation between O U R R D A and A T P with M L V S S depends on environmental and operational conditions of a plant and cannot be adequately described by a set of equations. Therefore MLVSS remains the best estimate for the biomass in the system. CONCLUSIONS (1) Correlation of M L V S S with O U R and the enzyme activity of R D A and ATP is unpredictable and there is no consistency in the behaviour. (2) The correlation of MLVSS with O U R and R D A improves with the reduction of BOD in the system. (3) The sludge age has significant effect on the enzyme and O U R activity. (4) This activity could be used as a tool to estimate MLVSS only when the system has reached stabilised biomass, i.e. low B O D in the mixture. (5) When such environments exist, O U R gives the best correlation for estimation of MLVSS. (6) Based on the results, and because of the variation in wastewater characteristics, M L V S S remains
the most reliable estimate of viable biomass in biological process. Acknowledgements--This work is part of funded research project AR-3-16 of the Saudi Arabian National Centre of Science and Technology (SANCST) and was carried out in the Environmental Engineering Laboratories of the Civil Engineering Department, University of Petroleum and Minerals, Dhahran. Saudi Arabia. The authors extend their thanks to Mr Abdul Appa and Essam EI-Deeb of the Department of Civil Engineering for their time. efforts and help.
REFERENCES
APHA (1975) Standard Methods for the Examination of Water and tVastewater, 14th Edition. American Public Health Association, Washington, DC. Buksteeg W. (1966) Die Beurteilung von Abwasser und Schlamm mittels "f'TC. Proceedings of the 3rd International Conference of the [.4 WPR, pp. 212-220, Munich. Duggan J. B. and Cleasby J. L. (1976) Effect of variable loading on oxygen uptake. J. Wat. Pollut. Control Fed. 43, 540--550. Edwards G. L. and Sherrard J. H. (1982) Measurement and validity of oxygen uptake as an activated sludge process control parameter. J. Wat. Pollut. Control. Fed. 54, 1546-1552. Ford D. K., Yang J. T. and Eckenfelder W. W. (1966) Dehydrogenase enzyme as a parameter of activated sludge activities. Proc. 21st Int. Waste Conf., Purdue Unit. pp. 538-545. Graybill F. A. (1976) Theory and Application of the Linear Model, pp. 275-279. Duxbury Press, Belmont. Huang J. Y. C. and Cheng M. D. (1984) Measurement and new applications of oxygen uptake rates in activated sludge process. J. War. Pollut. Control. Fed. 56, 254--265. Khararjian H. and Ali 1. (1983) Nitrogen removal in activated sludge under varying aerobic-anaerobic profiles. Enrir. Technol. Lett. 4, 107-114. Lenhard G., Nourse L. and Schwartz H. M. (1964) The measurement of dehydrogenase activity of activated sludge. Proceedings of the 2nd International Conference of the IAWPR, pp. 115-119, Tokyo. Levin G. V., Schort J. R. and Hess W. C. (1975) Methodology for application of adenosine triphosphate determination in wastewater treatment. Era'it. Sci. Technol. 9, 961-965. Roe P. C. Jr and Bhagat S. K. (1982) Adenosine triphosphate as a control parameter for activated sludge processes. J, Wat. Pollut. Control Fed. 54, 244-253. Roy D., LeDuy A. and Roy P. H. (1983) One year survey of ATP and dynamic behavior of an activated sludge treatment plant. J. Wat. Pollut. Control Fed. 55, 1348-1354. Sherrard J. H. (1980) Communication: oxygen uptake rate as an activated sludge control parameter. J. Wat. Pollut. Control Fed. 52, 2033-2036. Strehler B. L. and Totter J. R. (1974) Determinatin of ATP and related compounds; firefly luminescence and other methods. Meth. Biochem. Anal. I, 341-349. Tebutt T. H. Y. and Paraskevopoulos A. G. (1981) Viability parameters for activated sludge. Envir. Sci. Technol. 2, 293-302. Weddle C. L., and Jenkins D. (1971) The viability and activity of activated sludge. Water Res. 5, 621-640.
0043-1354 85 $3.00 +0.00 Copyright C~ 1985 Pergamon Press Ltd
VIABILITY OF MICROBIAL MASS IN COMPARTMENTALISED SINGLE ACTIVATED SLUDGE PROCESS IQBAL ALl, H. KHARARJIAN and MUNIR AHMED University of Petroleum and Minerals, Dhahran. Saudi Arabia ( Receh'ed December 1984)
Abstract--Investigations were carried out for steady-state conditions on laboratory-scale model of an anaerobic-aerobic-anaerobic-aerobic configuration of the activated sludge process, for hydraulic detention time of 9.13 h and sludge age of 9, 16.4 and 28.3 days. Data was obtained on volatile suspended solids (MLVSS), adenosine triphosphate (ATP). oxygen uptake rate (OUR) and relative dehydrogenase activity (RDA) on the laboratory model and the extended aeration biological treatment unit at the ARAMCO treatment plant in Dhahran. A mathematical model was obtained based on the laboratory data, using a calibration method to estimate MLVSS for given values of OUR, RDA and ATP. Regression coefficients for MLVSS and OUR, MLVSS and RDA and MLVSS and ATP were determined. It was found that the mathematical model obtained does not adequately describe the inter-relationship between various parameters. The inter-relationship is a function of operational conditions occurring in the plant and too complex to be described by a mathematical model. MLVSS offers the best estimate and cannot be related by an equation with OUR, ATP and RDA. Key words--single activated sludge, microbial mass, MLVSS, OUR, ATP, RDA, cell count, steady-state
INTRODUCTION According to the present day practice volatile suspended solids (MLVSS) are the main indicator for the viable biomass concentration in an activated sludge process. Since the dead mass contained in the MLVSS does not contribute to biological treatment, therefore, many a time this may not provide a correct indication of treatment efficiency. Adenosine triphosphate (ATP), oxygen uptake rate ( O U R ) and relative dehydrogenase activity ( R D A ) have been investigated as control parameters in an attempt to use them as indicators of efficiency and control parameters of a biological treatment. Their determination calls for a sophisticated laboratory process which may not be available in every treatment plant. Numerous models of the activated sludge process have been developed, including a parameter expressing the active biological solids for which MLVSS has been used. However, since only a small percentage of the organisms in activated sludge are viable (Weddle and Jenkins, 1971), the validity of such models becomes questionable. Investigations have been done to measure R D A , ATP, O U R and cell count as viability parameters. Tebbut and Parakevopoulos (I 981) state that these parameters do not provide a satisfactory measure of the sludge activity. ATP was found proportional to viable cell count under steady-state conditions in activated sludge by Weddle and Jenkins (1971). They concluded that ATP can be an appropriate biomass parameter for the growth rates commonly encountered in activated sludge plant operation. 927 WR. l q T - - H
Levin et al. (1975) concluded that A T P analysis could be employed to control mixed liquor biomass through regulation of return sludge. Roe and Bhagat (1982) show that ~o viability (ATP/SS ratio) decreases with cell residence time. They conclude that although A T P was a good indicator o f the biokinetic and settling characteristics of activated sludge under laboratory steady-state conditions, there is need of research for shock loadings. O U R has been a c o m m o n method for measuring activity rates of sludge. Weddle and Jenkins (1971) point out that O U R expressed on MLVSS basis reflect the viable content of the sludge and is independent of net growth rate when expressed on a viable cell basis. Edwards and Sherrard (1982)concluded that specific O U R cannot be used as a control parameter for the activated sludge process, when treating a highly variable influent quantity and quality of flow. Sherrard (1980) citing the study of Duggan and Cleasby (1976) has opposed the use of O U R as a valid control parameter on the basis of highly variable and fluctuating O U R values with little or no change in effluent quality. In a recent study Huang and Cheng (1984) concludes that O U R reflects the extent of microbial activity and specific O U R of mixed liquor can be used to predict the final effluent soluble C O D concentrations during transient loading condition. R D A has been used by Lenhard et aL (1964) and Bucksteeg (1966) to measure oxidising capacity of sludge. Tebbut and Paraskevopoulos (1981) conclude that R D A is not a particularly satisfactory method to measure the sludge activity because many factors
928
IQBAL ALl et al.
affect the analytical results. They suggest the use o f M L V S S for routine assessment o f the activity o f the activated sludge process since the use o f specialised parameters to describe the viability o f microorganisms in wastewater treatment process is o f questionable validity. Roy et al. (1983) carried out studies and collected data for a year on a full-scale domestic wastewater treatment plant at Quebec, to investigate any relationship between ATP or A T P pool and other biomass-related variables, operating condition or BOD reduction. Their conclusion in this regard is " A T P did not correlate well with biomassrelated variables but low values did seem to indicate p o o r aeration performance". In view o f the above background, there is need for a comprehensive study involving different hydraulic detention times and sludge ages to arrive at a positive conclusion. This paper presents another attempt to f i n d - - i f a n y - - a correlation between MLVSS on the one hand and O U R , ATP and R D A on the other based on steady-state condition o f a labora;:ory-scale model operated at hydraulic detention time o f 9.13 h and sludge ages o f 9, 16.4 and 28.3 days. The calibration analysis was performed for three tanks and tested for the data obtained from N o r t h A r a m c o treatment plant for almost the same range o f hydraulic detention times and sludge ages. MATERIALS AND METHODS A schematic diagram of the laboratory-scale model and the arrangement of anaerobic and aerobic compartment is shown in Fig. 1. This arrangement was adopted based on the results of the investigations (Khararjian and All, 1982) which indicates highest nitrogen removal efficiency for such configuration. The unit was started by feeding the activated sludge seed from the North Aramco treatment plant in Dhahran, and then onwards 28.41 day-~ of degritted sewage was supplied to the tank. Harvard Peristaltic pump supplied the system at an average rate of 19.74mlmin-L The
/
wastev, ater with the return sludge flowed through the eight compartments and finally entered the sedimentation basin. Each compartment was 9cm i.d., with an average water depth of 21.06 cm and a total measured volume of 10.804 I., for the eight compartments, giving a hydraulic detention time of 9.13 h. The effluent was collected in 25 I. containers while the settled sludge was pumped back into the first compartment by the Harvard Peristaltic pump at a recirculation ratio of 70°~. Each day activated sludge was wasted from eight compartments. The amount wasted depended on sludge ages of 9, 16.4 and 28.3 days. The steady-state was assumed when MLSS and MLVSS were relatively constant in the system. Figure 2 gives a typical graph showing the period during which steady state conditions were assumed.
SAMPLI~!G AND ANALYSIS Grab samples were collected for each day from tanks, 1, 2 and 8. and were analysed for MLSS, MLVSS, OUR (mg 1- I day- t ), ATP (/~g 1- i ) and RDA (# tool 1- t ). For mixed liquor volatile suspended solids (MLVSS) well mixed samples were filtered through Whatman GF/C2. I cm dia glass fibre filters in 25 ml Gooch crucibles. The samples dried at 103~C for I h were burned for 20min in Muffle furnace at 550C. The procedure followed is in accordance with Standard Method~ (APHA, 1975). This sample volume was part of the daily sludge wastage. For determining oxygen uptake rates, a standard 300 ml BOD bottle was filled with mixed liquor from the reactor of interest and dissolved oxygen was read using YSI model 51B dissolved oxygen meter with dissolved oxygen probe. After measurement the mixed liquor was returned into the respective tanks. Modified Lenhard and Nourse method by Ford et al. (1966) using ITC (triphenyltetrazolium chloride) as hydrogen acceptor was used for the determination of dehydrogenase activity. ATP was measured by Firefly Luminescence Assay method as described by Strehler and Totter (1974) using Analytical Luminescence Laboratory Model 401 Fluorometer. Grab samples from influent and effluent were also obtained from the North Aramco treatment plant and analysed.
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Viability of microbial mass in compartmentalised single activated sludge process
929
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20,000
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22
24
26
28
30
32
I 34
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Fig. 2. Typical variation of MLSS, MLVSS in the system showing steady-state.
MATHEMATICAL MODEL
In an experiment, if X is an independent variable and Y is a dependent variable in a linear model Y , = ~ + f l X i + ~ ,,
i=1,2 ..... n
(1)
where ei is assumed independent and identically normally distributed random variable with mean zero and variance ,~-', and if we estimate a value of J," for a given value of Y0 using X" = Y 0 - ~/fl
(2)
laboratory-scale model, correlating MLVSS, with O U R , RDA, A T P for each tank and three different sludge ages of 9, 16.4 and 28.3 days. Table 4 provides the correlation for X (MLVSS) and Y (OUR, R D A and ATP) values combining and averaging the three sludge ages, for three tanks. Table 5 shows corn-
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0
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When ~( and fl are least square estimates, the method is called a calibration method (Graybill, 1976). MLVSS was calibrated with OUR, RDA and ATP for each of the three tanks and each sludge age. In the calibration experiment the MLVSS (Xi) is controlled at n levels and the corresponding readings of Y~ i.e. OUR, R D A and ATP observed. Using these n data points (Xi, Y~) least square estimates of ~t and fl are obtained on the basis of model (1). The fitted lines for ~t and fl are given in Table 3. The MLVSS (X;) values are estimated from particular values of Y (OUR, R D A or ATP) using equation (2). Table 3 provides the equations correlating MLVSS (X) and OUR, RDA and ATP for the laboratory data. It also gives correlation coefficient. Figure 3 gives some of the fitted lines.
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RESULTS
Steady-state data for 9.13 h for detention time and sludge age 9, 16.4 and 28.3 days are given in Table 1 for tanks 1, 2 and 8. The data consist of MLVSS, and corresponding values of OUR, R D A and ATP. Table 2 provides the same parameters from the Aramco treatment plant which has an average hydraulic detention time of 10-14 h and has an extended aeration system. Table 3 gives the equations based on
00
°
120
60
400
(
t
420
440
S 460
t 480
.,[ 500
t
t
I
520
540
560
Fig. 3. A typical set of fitted lines for tank 2, sludge age 9 days. Similar lines were obtained using computer graphics for tanks 1, 2 and 8, sludge ages of 9, 16.4 and 28.3 days.
930
IQBAL ALl et al.
Table 1. Steady-state data for laboratory model for 9.13 h detention time, sludge ages 9. 16.4 and 28.3 da~s
Sludge age 9 Days
Tank I 2 8
16.4 Days
I 2 8
28.3 Days
l 2 8
Value Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
MLVSS ( m g l -q)
OUR ( m g l - ~ d a y -~)
RDA ( v m o l l -I)
ATP ( # g l -~)
Relative OUR (rag r a g - ' day -~)
731.40 160.65 482.20 59.89 677.60 147.93 1219.00 341.00 1070.00 162.48 1559.00 336.10 2886.00 446.27 2768.00 246.30 3210.00 260.17
--530.10 97.96 122.50 18.60 --620.10 129.08 249.80 56.78 --952.70 323.27 485.40 64.61
7.60 1.50 7.24 1.55 10.51 2.90 81.83 66.2t 60.80 44.43 79.90 57.47 303.00 85.50 199.60 82.24 248.10 107.76
140.80 69.80 152.70 51.41 263.20 120.81 269.60 144.38 380.80 275.57 460.80 169.76 597.80 309.80 667.10 390.78 720.89 333.38
--1.11 0.24 0.19 0.04 --0.60 0.t7 0.16 0.04 --0.34 0.11 0.15 0.02
parison between the measured values of MLVSS from the North Aramco treatment plant for 5 days and predicted values based on mathematical model. This gives an idea how closely MLVSS can be predicted for the measured values of OUR, RDA and ATP by the mathematical model developed from the laboratory data.
Relative RDA (,amol mg -~ MLVSS)
Relative ATP l u g m g -* of MLVSS)
0.01 0.003 0.02 0.003 0.01 0.003 0.07 0.05 0.05 0.03 0.05 0.03 0.11 0.03 0.07 0.03 0.15 0.02
0.21 0.12 0.31 0.09 0.40 018 0.23 0.11 0.35 0.23 0.30 0.10 0.2t 0.11 0.25 0.17 0.22 0.10
DISCUSSION
The correlation coefficients as shown in Table 3 for RDA in case of tank 1 are 0.436, 0.424 and 0.237 for 9, 16.4 and 28.3 days, respectively, while for tank 2 they are 0.068, 0.738 and 0.081 and for tank 8 they are 0.486, 0.363 and 0.005. This clearly indicates that
Table 2. Aramco treatment plant data for five consecutive days MLVSS ( m g l -I)
OUR ( m g l - ' d a y -t)
RDA(#moll -t)
A T P ( . u g l -I)
Days
Initial
Final
Initial
Final
Initial
Final
Initial
Final
I 2 3 4 5
1815 2020 2060 2160 1820
t760 2100 2030 2050 1810
1120 864 1210 1188 734
380 528 346 304 245
69.0 71.0 42.0 65.2 74.6
124.4 79.8 37.3 84.0 88.7
1107 1177 1071 1132 1104
tl31 1196 870 1085 1130
Table 3. Mathematical models for individual sludge ages Tank No.
Sludge age
Tank t (Anaerobic)
9 days (n = 10) 16.4 days (n = 105 28.3 days (n = 105
.v, = y~ = Y2 = Y3 = y: = Y3 =
4.83 + 0.0038x 259.7 -- 0.1626x - 18.39 + 0.0822x 206.61 + 0.0517x 173.22 + 0.046x 593.44 + 0.0015x
0.436 -0.374 0.424 0.112 0.237 0.0022
Tank 2 (Aerobic5
9 days (n = 10)
),~ = y., = Y3 = y~ = y., = ),~ = Yt = y., = 1,~ =
459.73 + 0.146x 6.51 + 0.0017x -- 146.64 + 0.621x 697.34 -- 0.0722x -- 155.18 + 0.202x --214.10 + 0.556x --764.70 + 0.620x 274.8 -- 0.028x -- 2726.33 -- 0.7439x
0.089 0.068 0.723 -0.091 0.738 0.328 0.473 -0.081 - 0.469
16.4 days (n ~ t0) (n = 10) 28.3 days (n ~ I0) Tank 8 (Aerobic) (Last in the series)
Model
Correlation coefficient
9 days (n = 10)
Yt = 67.72 + 0.08Ix y., = 4.32 + 0.0089x Y3 = 130.85 + 0.195x
0.643 0.486 0.239
16.4 days (n ~ 10)
y~ = y, = Y3 = y~ = v., = )'3 =
0.404 0.363 0.275 0.457 0.005 0.202
28.3 days (n ~ 105
143.43 + 0.0682x - 16.92 + 0.062x 244.21 + 0.139x 120.96 + 0.| 13x 241.74 + 0.002x 83.86 + 0.263x
x = MLVSS; yt = O U R (rag 1-t d a y - ' ); y: = R D A ( ~ m o l I- ~); y~ = A T P ( # g I-~ 5; n = number o f observations.
Viability of microbial mass in compartmentalised single activated sludge process
931
Table 4. Mathematical model based on n = 30 for tanks I. 2 and 8. combining and averaging the three sludge ages of 9. 16.4 and 28.3 days Tank Model Correlation coefficient Tank 1 (Anaerobic) N = 30
y: = 32.9 + 0.0613x Y3~ 192.6 + 0.0890x
0.619 0.464
Tank 2 (Aerobic) n = 30
y~ = 422.5 + 0.1933x y., = -28.0 + 0.0824x yj = I 15.5 + 0.1977x
0.707 0.845 0.684
Tank 8 (Aerobic) n = 30
y~ = -799.2 + 0,5867x y, = -54.2 + 0.0920x )3 = 145.7 + 0.1924x
0.54 0.825 0.700
For explanation of the symbols x. )'~. y., and )'j. refer to Table3.
s l u d g e a g e d o e s n o t i n f l u e n c e t h e v a l u e s o f 'y a n d fl in a n y set p r e d i c t a b l e m a n n e r . T h i s is true for A T P for all t h e t a n k s a n d O U R for t a n k s 2 a n d 8. T h i s i n d i c a t e s t h a t c o r r e l a t i o n o f M L V S S with e n z y m e activity R D A a n d A T P is a n u n p r e d i c t a b l e f u n c t i o n o f s l u d g e a g e a n d also o f a n a e r o b i c a n d a e r o b i c a r r a n g e m e n t s . T h i s also a p p l i e s for t h e c o r r e l a t i o n b e t w e e n M L V S S a n d O U R as c a n be seen for t a n k s 2 a n d 8. H o w e v e r , t h e r e is a m a r k e d i m p r o v e m e n t in t h e c o r r e l a t i o n coefficient for O U R f r o m t a n k 2 a n d t a n k 8. T a n k 8 is t h e last in the series. F o r t a n k 2 the v a l u e s for t h r e e s l u d g e a g e s are 0.089, - 0 . 0 9 1 a n d 0.473. T h i s is e x p e c t e d as t h e r e o c c u r s c o n t i n u o u s r e d u c t i o n in B O D in t h e s y s t e m . T h e d a t a for e a c h t a n k irrespective o f t h e s l u d g e a g e s w a s c o m b i n e d a n d lines were fitted g e t t i n g a n o t h e r set o f v a l u e s for ~ a n d /L T h i s set o f e q u a t i o n s i g n o r e s t h e s l u d g e a g e effect a n d is g i v e n in T a b l e 4. It w a s t h o u g h t t h a t s u c h a n e q u a t i o n c o v e r i n g t h e d a t a w h i c h s p r e a d o v e r 9 - 2 8 . 3 clays s l u d g e a g e m a y p r o v i d e a m o d e l to a d e q u a t e l y describe M L V S S w i t h e n z y m e activity or O U R , T h e c o r r e l a t i o n coefficient v a l u e s in g e n e r a l a r e very m u c h
h i g h e r t h a n t h o s e o b t a i n e d in i n d i v i d u a l e q u a t i o n for e a c h s l u d g e age. The measured values obtained from Aramco treatment plant for MLVSS, OUR, RDA and ATP are s h o w n in T a b l e 2. T h e A r a m c o t r e a t m e n t p l a n t is a n a c t i v a t e d s l u d g e t y p e w i t h a n e x t e n d e d a e r a t i o n syst e m a n d a s l u d g e a g e o f 20 d a y s a n d e s t i m a t e d h y d r a u l i c d e t e n t i o n t i m e o f 1 0 - 1 4 h. T w o sets o f d a t a for initial a n d final c o n d i t i o n s f r o m t r e a t m e n t p l a n t c o r r e s p o n d to t a n k s 2 a n d 8 o f t h e l a b o r a t o r y - s c a l e m o d e l respectively. T h e m e a s u r e d v a l u e s o f O U R , R D A a n d A T P f r o m t h e p r o t o t y p e were fed i n t o t h e e q u a t i o n s to find t h e v a l u e s o f M L V S S . T a b l e 5 shows a comparison between the predicted values of MLVSS based on the measured values of OUR, R D A a n d A T P , a n d t h e M L V S S v a l u e s m e a s u r e d at the plant. F r o m T a b l e 5, in t h e c a s e o f t a n k 2, p r e d i c t e d v a l u e s o f M L V S S f r o m O U R m e a s u r e m e n t s are, f r o m a b o u t 13 to I 0 0 ~ h i g h e r t h a n t h e m e a s u r e d v a l u e s as o b t a i n e d f r o m t h e p r o t o t y p e . O n l y t w o o u t o f five p r e d i c t e d v a l u e s u s i n g O U R a r e w i t h i n 1 3 ~ o f t h e m e a s u r e d values. In t h e c a s e o f t a n k 8, t h e r e is a
Table 5. Showing predicted values of MLVSS as determined by the equations (Table 3) and measured values of MLVSS from Aramco Treatment Plant (tanks 2 and 8 in the laboratory model correspond to Initial and Final data of Aramco Treatment Plant respectively) Tank No. 2 ATP =yj (,ugl -a ) RDA = y , (,umol I-I ) O U R = y j (mgl-Jday -t) y; = 115.5 -4-0.1977x Yz ~ - 2 8 + 0.0824x y~ = 422.5 + 0.1933x x = MLVSS mg/ measured values (Aramco Plant) x = MLVSS Yl measured x ~ MLVSS y, measured x = MLVSS ),~ measured Initial predicted Aramco predicted Aramco predicted Aramco 1815 2020 2060 2160 1820 x = MLVSS Final 1760 2100 2030 2050 1810
3608 2284 4074 3960 1613
1120 864 1210 1188 734,4
OUR Yt = -779.2 + 0.586 1976 2228 1918 1846 1746
380 528 346 304 245
:
1177 69 1198 71 850 42 792 65.2 1245 74.6 Tank No. 8
5015 5369 4834 5144 4999
RDA y, = -54.2 + 0.092x
ATP ).~ = 145.7 + 0.1924x
1351 1456 I014 1502 1552
5121 5458 4522 4881 5116
124.4 79.8 37.3 84.0 88.7
1107 I 177 1071 1132 1104
1131 l 196 870 1085 1130
932
[QBAL ALl et al.
better correlation, which may be due to lower B O D in the system. This indicates that only for tow values of BOD the model adequately describes O U R - M L V S S relationship, and cannot be generalised to include the initial stages when BOD is high. Measured R D A values when plugged in the equations provided a lesser range of variations with the predicted values of MLVSS as compared to O U R and ATP. The variation ranged between as high as 63°0 to as close as 14,,O/o. Better correlation for tank 8 may again be attributed to low BOD is in the system in the final stages of treatment. It is obvious that R D A - M L V S S correlation is poor and cannot be relied upon irrespective of the operational and environmental conditions. The predicted values of M L V S S differ widely from the measured values, up to 182% as seen in Table 5. ATP provides the worst estimates for MLVSS for both the tanks 2 and 8, and hence cannot be used as a parameter to indicate the biomass in an activated sludge process. The correlation coefficient values for the combined data equations (Table 4) are much better than the correlation coefficients for the individual sludge age equation. However, this does not reflect any improvement as far as modelling is concerned. It is due to the mathematical reason of having derived the equation based on a larger collection of data (30 observations, 10 for each of the three sludge ages). This shows that the effect of sludge cannot be ignored. The above discussion indicates that inter-relation between O U R R D A and A T P with M L V S S depends on environmental and operational conditions of a plant and cannot be adequately described by a set of equations. Therefore MLVSS remains the best estimate for the biomass in the system. CONCLUSIONS (1) Correlation of M L V S S with O U R and the enzyme activity of R D A and ATP is unpredictable and there is no consistency in the behaviour. (2) The correlation of MLVSS with O U R and R D A improves with the reduction of BOD in the system. (3) The sludge age has significant effect on the enzyme and O U R activity. (4) This activity could be used as a tool to estimate MLVSS only when the system has reached stabilised biomass, i.e. low B O D in the mixture. (5) When such environments exist, O U R gives the best correlation for estimation of MLVSS. (6) Based on the results, and because of the variation in wastewater characteristics, M L V S S remains
the most reliable estimate of viable biomass in biological process. Acknowledgements--This work is part of funded research project AR-3-16 of the Saudi Arabian National Centre of Science and Technology (SANCST) and was carried out in the Environmental Engineering Laboratories of the Civil Engineering Department, University of Petroleum and Minerals, Dhahran. Saudi Arabia. The authors extend their thanks to Mr Abdul Appa and Essam EI-Deeb of the Department of Civil Engineering for their time. efforts and help.
REFERENCES
APHA (1975) Standard Methods for the Examination of Water and tVastewater, 14th Edition. American Public Health Association, Washington, DC. Buksteeg W. (1966) Die Beurteilung von Abwasser und Schlamm mittels "f'TC. Proceedings of the 3rd International Conference of the [.4 WPR, pp. 212-220, Munich. Duggan J. B. and Cleasby J. L. (1976) Effect of variable loading on oxygen uptake. J. Wat. Pollut. Control Fed. 43, 540--550. Edwards G. L. and Sherrard J. H. (1982) Measurement and validity of oxygen uptake as an activated sludge process control parameter. J. Wat. Pollut. Control. Fed. 54, 1546-1552. Ford D. K., Yang J. T. and Eckenfelder W. W. (1966) Dehydrogenase enzyme as a parameter of activated sludge activities. Proc. 21st Int. Waste Conf., Purdue Unit. pp. 538-545. Graybill F. A. (1976) Theory and Application of the Linear Model, pp. 275-279. Duxbury Press, Belmont. Huang J. Y. C. and Cheng M. D. (1984) Measurement and new applications of oxygen uptake rates in activated sludge process. J. War. Pollut. Control. Fed. 56, 254--265. Khararjian H. and Ali 1. (1983) Nitrogen removal in activated sludge under varying aerobic-anaerobic profiles. Enrir. Technol. Lett. 4, 107-114. Lenhard G., Nourse L. and Schwartz H. M. (1964) The measurement of dehydrogenase activity of activated sludge. Proceedings of the 2nd International Conference of the IAWPR, pp. 115-119, Tokyo. Levin G. V., Schort J. R. and Hess W. C. (1975) Methodology for application of adenosine triphosphate determination in wastewater treatment. Era'it. Sci. Technol. 9, 961-965. Roe P. C. Jr and Bhagat S. K. (1982) Adenosine triphosphate as a control parameter for activated sludge processes. J, Wat. Pollut. Control Fed. 54, 244-253. Roy D., LeDuy A. and Roy P. H. (1983) One year survey of ATP and dynamic behavior of an activated sludge treatment plant. J. Wat. Pollut. Control Fed. 55, 1348-1354. Sherrard J. H. (1980) Communication: oxygen uptake rate as an activated sludge control parameter. J. Wat. Pollut. Control Fed. 52, 2033-2036. Strehler B. L. and Totter J. R. (1974) Determinatin of ATP and related compounds; firefly luminescence and other methods. Meth. Biochem. Anal. I, 341-349. Tebutt T. H. Y. and Paraskevopoulos A. G. (1981) Viability parameters for activated sludge. Envir. Sci. Technol. 2, 293-302. Weddle C. L., and Jenkins D. (1971) The viability and activity of activated sludge. Water Res. 5, 621-640.