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Kinetics of heavy metal bioleaching from sewage sludge—II. Mathematical model
Wat. Res.
Vol. 27, No. II, pp. 1653-1661, 1993
0043-1354/93 $6.00+0.00
Printed in Great Britain. All rights reserved
Copyright © 1993PergamonPress Ltd
KINETICS OF HEAVY METAL BIOLEACHING FROM SEWAGE SLUDGE--II. MATHEMATICAL MODEL R. D. T Y A G I * ~ , T. R. SREEKRISHNAN, P. G. C. CAMPBELL and J. F. BLAIS ~ Institut National de la Recherche Scientifique, 2700 rue Einstein, Complexe Scientifique, Sainte-Foy, Qu6bec, Canada GIV 4C7 (First received May 1992; accepted in revised form March 1993)
Abstract--A conceptual model of the overall process of metal bioleaching from sewage sludge has been developed on the basis of experimental observations. Sludge pH was identified as the parameter which controls bacterial growth and thus the overall process. Quantitative relationships among the various process parameters were incorporated in the conceptual model, giving a mathematical model for the process. Bacterial growth, sulfate concentration and pH profiles simulated using the model were found to match experimental observations. The degree of solubilization of each metal was found to depend on the sludge pH and the type of the sludge and is given as a set of solubilization curves. Key words--sewage sludge, acidophilic bacteria, specific growth rate, specific product formation rate, buffering capacity, Thiobacillus
NOMENCLATURE A = constant (dimensionless) relating a and b (a = Ab) Al = constant (h-~) [equation (2): pH dependence of growth of less acidophilic species] A2=constant (h -~) [equation (II): pH dependence of growth of acidophilic species] a = slope o f pH-[SO4] curve at pH 7.0 [(mg/l)/unit pH] B~ =constant (h -I) [equation (2): pH dependence of growth of less acidophilic species] B2=constant (h -~) [equation (11): pH dependence of growth of acidophilic species] b = buffering capacity index (BCI) [(mg/l)/unit pH) C= constant (dimensionless) relating b and c (c = Cb) CI =constant (dimensionless)(=AI/BI) C2 = constant (dimensionless) (= A2/B2) c = slope of pH-[SO4] curve at pH 1.0 [(mg/l)/unit pH] kl = constant [(rag/l)/(105 CFU/ml)] [equation (6): sulfate production by less acidophilic species] k 2 = constant [(mg/1)/(10s CFU/ml)] [equation (14): sulfate production by acidophilic species] N=cell concentration (CFU/ml) t = time (h) #1 = specific growth rate of less acidophilic species (h -~) #2 = specific growth rate of acidophilic species (h -t) #~' =specific growth rate of less acidophilic species at pH7.0 (h -~) #~' = specific growth rate of acidophilic species at pH 4.0 (h -l) v~= specific product formation rate of less acidophilic species [(mg/hl)/(105 CFU/ml)]
into receiving waters has resulted in many varieties of wastewater treatment facilities. During these treatmerit processes, a large volume of sludge is generated. The sludge volume is approx. 1% of the wastewater treated, with a solids content of 1-7% (Lester et al.,
v: = specific product formation rate of acidophilic species [(mg/hl)/(10 s CFU/ml)]
The overall process taking place in the reactor is visualized as the work of two distinct sulfur-oxidizing bacterial species, a less acidophilic species (pHop t 7.0) and an acidophilic species (pHop,4.0). The less aeidophilic species grows over the pH range of 7.0-4.0, whereas growth of the acidophilic species occurs in the pH range of 4.0-1.5. Sewage sludges normally have a pH of 6.0-7.0, under which conditions the less acidophilic species grows and the acidophilie species does not. The growth of less
INTRODUCTION Growing awareness of the harm done to the environment by the indiscriminate disposal of waste streams *Author to whom all correspondence should be addressed,
1983; Davis, 1986). Since many of the heavy metals present in the original wastewater become concentrated in the sludge, disposal of this metal-laden material may represent an environmental hazard (Tyagi and Couillard, 1989). Recently, a novel method has been developed for the removal of heavy metals from sewage sludge, based on the bioleaching of the sludge by a consortium of sulfur-oxidizing bacteria present in the sludge (Jain and Tyagi, 1992; Blais et al., 1992). The effect of system parameters such as the sludge solids concentration, medium pH, etc. on bacterial growth, acid production and metal solubilization process has already been investigated (Sreekrishnan et al., 1993). In this paper, we incorporate the information gathered on the system kinetics into a mathematical model for the bioleaching of metals in a batch reactor. CONCEPTUAL MODEL OF THE PROCESS
1653
1654
R.D. TYAGI et al.
acidophilic species in the presence of elemental sulfur is associated with the production of sulfuric acid, which brings down the medium pH. The lowering of medium pH has an adverse effect on the specific growth rate of the less acidophilic bacteria but they continue to grow at a reduced rate, resulting in further acid production, until the sludge pH reaches a value of 4.0. At this point, the less acidophilic species ceases to grow and the acidophilic species takes over the task of acid production. The growth of the acidophilic species is also adversely affected by the progressively lower pH conditions encountered as a result of continued acid formation. Finally, at a pH of about 1.5, the growth of the acidophilic species also ceases. The process of metal solubilization proceeds in parallel with the acid production process. The quantity of metal(s) leached into the liquid phase depends on the sludge pH, sludge type (or, speciation of metal in the sludge) and the initial metal concentration present in the sludge, We now proceed to develop the mathematical expressions that can suitably express each of the steps as envisaged above. Dependence o f the growth rate o f the less acidophilic bacteria on medium p H
computed as - 1.33 ± 0.05. Substituting this value in equation (4), we get /h =/z*
(5) 0.616 It is worth noting that at a pH of 4.0, from equation (5), #~ = 0.09/~ implying very low specific growth rates for the less acidophilic species at this pH. Thus the experimentally determined value of CI supports the model assumption that growth of the less acidophilic species ceases at a pH of 4.0.
o.60 a o.80 o.lo
: /
= -o.lo
Specific growth rate (p) is defined as 1 dN # - N dt
- 1.33 + ln(pH)
-o.3o (1)
-o.8o 0
where N is the number of bacterial cells (colony forming units/ml) and t is the time. It has been observed that the growth rate of the less acidophilic bacteria bears a linear relationship to the natural logarithm of the medium pH (Sreekrishnan et al., 1993):
2
,
,
4
6
8
pH
0.so b
#~ = A~ + B~ in(pH)
(2)
where /.lI ~-specific growth rate of the less acidophilic species at that pH A 1, BI = constants. If #* is the specific growth rate of the less acidophilic species at a pH of 7.0, #~ = Aj + Bi In(7.0)
(3)
0.80
. ~
~
o.lo
:. / r - f / "~ -o.lo ~ = " . -0.30
Equations (2) and (3) can be combined to yield C l Jl-
-0, 50 0.00
ln(pH)
/ij =/~* Cl + in(7.0)
(4)
where C1 =A1/BI.
Specific growth rates were calculated from experimental growth data (i.e. by differentiating the cell concentration vs time curve) at various pH values for the less acidophilic species and plotted as shown in Fig. l(a) and (b). From these and many other similar curves for various sludges, the value of C~ was
1.60
. . 4.80
3.20
. . 6.40
8.00
pH
Fig. 1. Plot of the specificgrowth rate of the less acidophilic bacteria at various sludge pH values [equation (5)]. (a) Various types of sludges: &, non-digested sludge from St-Georges de Beauce; O, non-digested sludge from Beauceville; , , aerobically digested Black Lake sludge; l , aerobically digested sludge from Ste-Claire; and I-q,anaerobically digested sludge from Valcartier. (b) Same sludge (aerobically digested Black Lake sludge) with various solids concentrations: &, 7.0 g/l; O, 22.8 g/l; O, 71.8 g/l; and I , 70.0g/I.
Mathematical model for metal bioleaching
>-
5 -
4
1655
o.so
/•
--
/
~
0.40
•
. ~ -'. 2_ ~
~.
o.
0.20
.~
~
O.lO
I/" 0.00
i
I
J
i
0.03
0.06 g l ' h-1
0.09
0.12
[
Fig. 2. Plot of the specific product (acid) formation rate against specific growth rate for the less acidophilic bacteria [equation (7): vI = 35 + 1.05/h].
Relation between growth and product (sulfate)formation for the less acidophilic bacteria Sulfate production by the less acidophilic species is modeled as a case of growth-associated product formation, i.e. the rate of production of sulfate is directly proportional to the bacterial growth rate. This also implies that the substrate is consumed for growth as well as sulfate production in the same proportion. Consequently, the specific growth rate (p,) vs specific product formation rate ( h ) graph is a straight line of slope k~ that passes through the origin. The specific product formation rate (v) is defined as v = 1 d (SO4) N dt vl = k, p
(6)
The specific product formation rates and specific
' 0.08
J 0.08
' 0.09
0.12
p.~, h -z Fig. 4. Plot of the specific product (acid) formation rate against specific growth rate for the acidophilic bacteria [equation (15): v2 = (4.0 + 0.3) P2]. growth rates were calculated for various sludges and plotted as shown in Fig. 2. The data points were somewhat scattered but a straight line could be fitted. From these, we find the value of kj to be 35.0 4- 1.05. Hence •1
=
35.0 p,
(7)
In equation (7),/Zl has units of h - ' and Vl has units of (mg/l h)/(105 CFU/ml).
Relation between medium sulfate concentration andpH in the less acidophilic species' range The buffering capacity index (BCI) for the sludge has been defined elsewhere (Sreekrishnan et al., 1993) and is represented by the following equation:
d(SO,) BCI = d (pH)p.=4.0 = b
(8)
The pH vs SO4 concentration profile is assumed to follow a path such that the slope of the curve varies linearly from a pH of 7.0 to a pH of 4.0.
0.20
o.10
If
o.12
d (S04) d (pH)
pH=7.0
=
a
(9)
then, at any value of pH such that 4.0 ~
7& = 0.08
_
d(SO,)= (b-a) .(7.0-pH)+a d (pH) (7.0-4.0)
•*
' o.0o
1.oo
(10)
Dependence of the growth rate of the acidophilic bacteria on medium p H
0.04
0.0o o.0o
0.00 0.00
2.4o
a.2o
4.00
pH Fig. 3. Specific growth rate variation with sludge pH for acidophilic bacteria [equation (13): ,uz=p*(-0.40+ ln(pH))/0.986]: &, non-digested sludge from St-Georges de Beauce; and O, aerobically digested Black Lake sludge.
For the acidophilic species the growth rate was also found to vary linearly as the natural logarithm of the sludge pH (Sreekrishnan et al., 1993).
#2 = A2 + B2 ln(pH)
(11)
where /~2 = specific growth rate of acidophilic species A 2, B2 = constants.
1656
R.D. TYAGI et /
al.
INPUT / , Nt, N2, pH, 504, ~ 1 " ~2'' b
t
/
INPUT
/
a, c
t
Calculate la Calculate v
t
(Eqn. 5) ] (Eqn. 6) ]
V
N <-- Nt
[
A SO 4 -v*N At
I [~
t <--t +At
]
V
504
=
ASO4 At
* At
V dSO4 (Eqn. 10) dp---H--
Calculate
V pH <-- pH + (-A SO4/
Y e s + t N2 N <--
V
dS04 )
No [Calculate Ix
(Eqn. 5)
[Calculate Ix
(Eqn. 14)
VN <-- N + (La * N * At)
I Calculatev
(Eqn. 15)
I Calculatev
Aso 4
--At = v * N
~
,SO4
<-.
(Eqn. 6)
SO 4 + ASO4
, V I
t<--t+At t A SO4
A SO 4 =
/
At
* At
Calculate dSO4
~Eq..
OUTPUT / t, N, pH, SO 4, Ix, v
16)
dSO 4
pH <-- pH + (-A SO4/d"~ - ) ] Calculate p,
(Eqn. 14) I
¢ L N~--N+.*N*At
I
[
[
i
SO 4 <-- SO 4 -t- ASO 4
[ Calculate v
(Eqn. 15) [
t, N, pH, SO 4, Ix,
Yes
No
Fig. 5. Flow chart for simulating bacterial growth, acid production and sludge pH
Mathematical model for metal bioleaching 1000
1657
800
0
o
•
800
640
~
c
:~
600
"~
400
~> ,
0
480
0
"> . r"
320
r"
o
o
200
160
0 =-~--~ - " ' - - -
0
24
48
" -" -" -" -
72
96
0-z
120
0
-::nP.x:z._,
24
T i m e (h)
=
acidophilie
,
48
, ,
72
.
96
120
Time (h)
~-
less
acidophilic
--4--
simulated
Fig. 6. Experimental and simulated bacterial growth curves. (a) N~ = l; N 2 = 18; initial pH = 6.92; initial sulfate concentration = 780 mg/l; #1 = 0.175 h-~; #2 = 0.11 h-~; BCI = 162; a = 0.025 BCI; and b = 20.0 BC1. (b) N~ = 0.433; N 2 = 15.0; initial p H = 6.95; initial sulfate concentration = 1500 mg/1;/~t = 0.175 h-~; /J2 = 0.11 h - t ; BCI = 565; a = 0.025 BCI; and b = 4.0 BCI.
I f / ~ ' r e p r e s e n t s t h e specific g r o w t h r a t e o f t h e a c i d o p h i l i c species a t a p H o f 4.0, C 2 + In(pH) P2 = P *
(12)
C2 + ln(4.0)
T h e p H vs #2 g r a p h s were p l o t t e d f o r t h e a c i d o p h i l i c species as s h o w n in Fig. 3. F r o m m a n y s u c h g r a p h s , t h e a v e r a g e v a l u e o f C2 w a s f o u n d to be - 0 . 4 -I- 0.05. Hence,
where C2 = A 2 / B :
P2 = P ~
7.00
7.00
5.60
5.60
4.20
4,20
2.80
2.80
1.40
1.40
0.00 0
'
'
'
'
24
48
72
96
~ "
0.00
120
0
T i m e (h) -"
experimental
-0.40 + ln(pH) 0.986
"""
T
,
,
,
,
1
24
48
72
96
120
T i m e (h)
--e--
simulated
Fig. 7. Experimental and simulated pH profiles. (a) N~ = l; N 2 = 18; initial pH = 6.92; initial sulfate concentration = 780 mg/1;/z t = 0. i 75 h-~;/t 2 = 0.11 h-~; BCI = 162; a = 0.025 BCI; and b = 20.0 BCI. (b) N~=0.433; N2=15.0; initial p H = 6 . 9 5 ; initial sulfate c o n c e n t r a t i o n = 1 5 0 0 m g / 1 ; # 1 = 0 . 1 7 5 h - ~ ; /t 2 = 0.11 h - t ; BCI = 565; a =0.025 BCI; and b = 4.0 BCI.
(13)
R.D. TTAGIet al.
1658 8200
6000
4180
~
E .
.
c o
o
= c o o C o
o
4800
E 3120
=
3600
c • o ~ o
2080
o
2400
1040 _ = ( ~
~
1200
0
0
I
I
24
48
I
I
72
06
0
120
0
I
I
I
I
24
48
72
06
Time (h) .L
120
Time (h) experimental
--*--
simulated
Fig. 8. Experimental and simulated sulfate concentrations. (a) N] = l; N 2 = 18; initial pH = 6.92; initial sulfate concentration = 780 rag/l; /~, = 0.175 h - I ; / h = 0.11 h-I; BCI = 162; a = 0.025 BCI; and b = 20.0 BCI. (b) N I = 0.433; N 2 = 15.0; initial pH = 6.95; initial sulfate concentration = 1500 mg/l; #~ = 0.175 h-~; /a2 = 0.11 h-~; BCI = 565; a = 0.025 BCI; and b = 4.0 BCI.
Again, at a p H of 1.5,
increases linearly to a value of c such that
/a2 = 0.0055 ~* This means that acidophilic species is consistent with acidophilic species
d (SO4)
the specific growth rate of the is very low at a pH of 1.5, which the model assumption that the stops growing at a pH of 1.5.
c
pH=L0
Hence, the pH-SO4 concentration curve can be represented by the equation
Relation between growth and product (sulfate) formation for the acidophilic bacteria
d (SO4) d (pH)
The production of acid by the acidophilic species is modeled as a case of growth-associated product formation, as in the case of less acidophilic bacteria. The v2 vs/t2 graph is a straight line through the origin with a slope of k2 such that V2 = k 2 / ~ 2
~
b + (c3.0b)" (4.0 - pH)
(16)
CADMIUM
100
(14) 80
Sulfate production rates and growth rates were computed for the acidophilic species growing in a number of sludges and the v2 vs/~2 graph was plotted. Figure 4 is an example o f one such plot. F r o m these graphs, the average value of k 2 was found to be 4.0 + 0.3: v2 = 4.0/~2
(15)
where /~2 has units of h -l and v2 has units of (mg/l h)/(105CFU/ml).
Relation between medium sulfate concentration and p H in the acidophilic species' range d (SO4) BCI - d (pH) pH=4.0 ~" b
(8)
The slope of the p H vs SO4 concentration curve is modeled such that the slope of b at a pH of 4.0
~.~
60
-~
40
"~ 20
o 2
4
6
a
pH
Fig. 9. Solubility charts for cadmium. (a) Non-digested sludges; (b) anaerobically digested sludges; and (c) aerobically digested sludges.
Mathematical model for metal biol._thing
1659
CHROMIUM
NICKEL
100
100
8S
80
ss
=~
so
40
-a
40
20
~
0 0
' 2
20
' 4
' 6
0 8
' 2
0
pH
4
' 6
8
pH
Fig. 10. Solubility charts f o r chromium. (a) Non-digested
Fig.
sludges; Co) anaerobically digested sludges; and (c) aerobically digested sludges,
sludges; Co) anaerobically digested sludges; and (c) aerobically digested sludges.
sludge
Model input parameters The following input parameters have to be specified if the model is to be used to predict bacterial growth, pH vs time profiles and acid production: (i) initial number of viable iess-acidophilic bacterial cells in the sludge (N~) (ii) initial number of viable acidophilic bacterial cells in the sludge (N2) (iii) initial sludge pH (iv) initial concentration of sulfate in the
12. Solubility charts f o r nickel. (a) Non-digest=d
(v) specific growth rate of the less acidophilic species at a pH of 7.0 (p~) (vi) specific growth rate of the acidophilic species at a pH of 4.0 (p~) (vii) buffering capacity index for the sludge (b) (viii) value of a (slope of pH--SO4 curve at pH 7.0) and b (slope of pH SO4 curve at pH 4.0). The values of a and b were found to be related to the buffering capacity index and the following relation
COPPER
LEAD
100
100
80
80
5°0
so -
"~
40
~
20
0 --
2S
' 2
4
' 6
S 8
pH
Fig. 11. Solubility charts for copper. (a) Non-digested sludges; CO) anaerobically digested sludges; and (c) acre* bically digested sludges, WR 27/I I--E
40
2
' 4
' 6
8
pH
Fig. 13. Solubility charts for lead. (a) Non-digested sludgvs; Co) anaerobically digested sludges; and (c) aerobically digested sludges.
1660
R.D. TYAOI et aI.
ZINC
using various sludges were used to compare the model output with actual process performance. It was found that the model gives a reasonably good prediction of bacterial growth, and of temporal changes in pH and sulfate concentration in the reactor. Figure 6(a) and (b), Fig. 7(a) and Co) and Fig. 8(a) and (b) give the comparison between experimental and simulated data for cell growth, pH profile and sulfate concentration profile for experiments carried out in shake flasks.
100
80 e0 o
M o d e l shortcomings
40 c
Compared with the shake flask experimental data, one shortcoming for the model as presented is that it does not take into consideration any lag time for bacterial growth. According to the model, the less 0 acidophilic bacteria start growing exponentially at 0 2 4 6 s time t = 0 whereas in a shake flask, bacterial growth exhibits an initial lag phase. The lag time (time pH elapsed before the bacteria actually start growing Fig. 14. Solubility charts for zinc. (a) Non-digested sludges; exponentially) could easily be included in the model (b) anaerobically digested sludges; and (c) aerobically if exact information regarding the lag were available. digested sludges. The duration of the lag phase will depend on a number of parameters such as the nature of the was derived after examining a number of pH-SO4 sludge, inoculum age, the presence of toxic comconcentration profiles: pounds, etc. The specific growth rate of the bacteria is modeled a = Ab as a function of pH only. This is contrary to the where A varied over the range 0.01-0.025. A value of normal practice of modeling growth as a function of 0.025 was found appropriate for sludges having low limiting substrate concentration (or inhibitor concenBCI values, such as 200 or less; the value of A tration). We consider the change in medium pH and decreased with increasing BCI value. Similarly, its effect on growth rate to be more important than c = Cb any possible substrate limitation on bacterial growth. In fact, this was verified by raising the pH to 7.0 by where alkali addition after it had been lowered down to 1.5 C = 4.0; b > 500 by the bioleaching process; every time this was done, the bacteria started growing again, producing acid C = 10.0-20.0; 200 ~
Table 1. Validityrange for metal solubilizationcharts Validity range (rag metal/kg dry sludge initially present) Type of sludge Non-digested Anaerobically digested Aerobically digested
CA 0-24 0-31
Cr 0-475 0-650
Cu Ni Pb Zn 0-2100 0-222 0-278 0-1460 0-3690 0-68 0--650 0-1930
0-11
0-1720 0-1830 0-177 0-336 0-1200
Mathematical model for metal bioleaching metal solubilization process are not yet available. For this reason, we are not in a position, at least for the present, to extend the model to the metal solubilization process. To partially offset this shortcoming, metal solubilization can be predicted using empirical solubility charts, Metal solubility charts
Of all the factors that are seen to affect the metal solubilization process, the most difficult ones to incorporate in the model are the concentrations, relative as well as absolute, of each metal present in the sludge, and the metal speciation in the sludge. To circumvent this problem, we express metal solubilities in the form of a chart with metal concentration in the liquid phase, expressed as a percentage of the metal concentration initially present in the sludge, presented as a function of sludge pH. The effect of metal speciation on metal solubility is accounted for by using three different solubility charts for each metal depending on whether the sludge is non-digested, aerobically digested or anaerobically digested. These are shown in Figs 9-14 for the metals Cd, Cr, Cu, Ni, Pb and Zn. These correlation charts were prepared using actual data from laboratory experiments using 20 different types of sludges. These graphs were obtained by drawing a smooth curve through the solubilization values obtained experimentally. Based on the sludges used for the study, the solubilization data are valid for initial metal concentration levels as given in Table 1. However, these are not the concentration limits for metal solubilization from sewage sludges but are the limits for metal concentrations used in the present work. The validity of these charts for initial metal concentrations lying outside these limits will have to be verified before using these charts. Again, these charts are meant to provide only an approximate value for the solubilization to be expected, in the absence of exact mathematical relationships to predict solubilization values. As already mentioned, even though pH is the most important parameter to influence solubilization levels, it is not the only factor. Also, classification of sludges into three groups, i.e. non-digested, anaerobically digested and aerobically digested sludges, cannot take into account all the variations seen among sewage sludges from various sources. It can be seen, from these charts, that for Cr and Cu, the solubilization achieved for a given sludge pH was almost the same irrespective of the sludge being
1661
non-digested, anaerobically digested or aerobically digested. For Cd and Pb, solubilization was achieved more readily in aerobically digested sludges followed by anaerobically digested sludges and non-digested sludges, in that order. For Ni, initial solubilization occurred earlier in aerobically digested sludges compared to the other two sludge types. In the case of Zn, solubilization was easiest for aerobically digested sludges, followed by non-digested sludges and anaerobically digested sludges, in that order. SUMMARYAND CONCLUSION Mathematical relations, based on experimental data, were written for various steps in the metal bioleaching process. These relations were incorporated into a model that could satisfactorily predict the bacterial growth and acid production processes. The metal solubilization process could not be incorporated directly into the model due to the influence of too many unquantified factors on this step. Instead, correlation charts were developed which could, within some limits, predict the metal solubilization using the sludge pH profile output generated by the model and the type of sludge used. Acknowledgements--We thank the Natural Sciences and Engineering Research Council of Canada (Grants A4984 and STR0100710), the Qu6bec Fonds pour la Formation de chercheurs et l'Aide ~i la Recherche (Grants FCAR 90-AS9713)and the Universit~ due Quebec (FODAR) for providing financial assistance to carry out this work.
REFEnENCF..S BlaisJ. F., Tyagi R. D. and Auclair J. C. (1992) Bioleaching of trace metals from sewage sludge by indigenous sulfur oxidizingbacteria. J. envir. Engng 118, 690-707. Davis R. D. (1986) Cadmium in sludges used as fertilisers. Experient. Suppl. 50, 55. Jain D. K. and Tyagi R. D. (1992) Leaching of heavy metals from anaerobic sewage sludge by sulfur oxidizing bacteria. Enzym. Microb. Technoi. 14, 376--383. Lester J. N., Sterrit R. M. and Kirk P. W. W. (1983) Significanceand behaviour of heavy metals in wastewater treatment process--II: Sludge treatment and disposal. Sci. Total Envir. 30, 45-83. Sreekrishnan T. R., Tyagi and R. D., Blais J. F. and Campbell P. G. C. (1993) Kinetics of heavy metal bioleaching from sewage sludge---I. Effect of process parameters.War. Res. 27, 1641-1651. Tyagi R. D. and CouiUard D. (1989) Bacterial leaching of metals from sludge. In Encyclopedia of Environmental Control Technology, I/oi-3: Wastewater Treatment Technology (Edited by Cheremisinoff P. E.), pp. 557-590. Gulf Publishing Co., Texas, U.S.A.
Vol. 27, No. II, pp. 1653-1661, 1993
0043-1354/93 $6.00+0.00
Printed in Great Britain. All rights reserved
Copyright © 1993PergamonPress Ltd
KINETICS OF HEAVY METAL BIOLEACHING FROM SEWAGE SLUDGE--II. MATHEMATICAL MODEL R. D. T Y A G I * ~ , T. R. SREEKRISHNAN, P. G. C. CAMPBELL and J. F. BLAIS ~ Institut National de la Recherche Scientifique, 2700 rue Einstein, Complexe Scientifique, Sainte-Foy, Qu6bec, Canada GIV 4C7 (First received May 1992; accepted in revised form March 1993)
Abstract--A conceptual model of the overall process of metal bioleaching from sewage sludge has been developed on the basis of experimental observations. Sludge pH was identified as the parameter which controls bacterial growth and thus the overall process. Quantitative relationships among the various process parameters were incorporated in the conceptual model, giving a mathematical model for the process. Bacterial growth, sulfate concentration and pH profiles simulated using the model were found to match experimental observations. The degree of solubilization of each metal was found to depend on the sludge pH and the type of the sludge and is given as a set of solubilization curves. Key words--sewage sludge, acidophilic bacteria, specific growth rate, specific product formation rate, buffering capacity, Thiobacillus
NOMENCLATURE A = constant (dimensionless) relating a and b (a = Ab) Al = constant (h-~) [equation (2): pH dependence of growth of less acidophilic species] A2=constant (h -~) [equation (II): pH dependence of growth of acidophilic species] a = slope o f pH-[SO4] curve at pH 7.0 [(mg/l)/unit pH] B~ =constant (h -I) [equation (2): pH dependence of growth of less acidophilic species] B2=constant (h -~) [equation (11): pH dependence of growth of acidophilic species] b = buffering capacity index (BCI) [(mg/l)/unit pH) C= constant (dimensionless) relating b and c (c = Cb) CI =constant (dimensionless)(=AI/BI) C2 = constant (dimensionless) (= A2/B2) c = slope of pH-[SO4] curve at pH 1.0 [(mg/l)/unit pH] kl = constant [(rag/l)/(105 CFU/ml)] [equation (6): sulfate production by less acidophilic species] k 2 = constant [(mg/1)/(10s CFU/ml)] [equation (14): sulfate production by acidophilic species] N=cell concentration (CFU/ml) t = time (h) #1 = specific growth rate of less acidophilic species (h -~) #2 = specific growth rate of acidophilic species (h -t) #~' =specific growth rate of less acidophilic species at pH7.0 (h -~) #~' = specific growth rate of acidophilic species at pH 4.0 (h -l) v~= specific product formation rate of less acidophilic species [(mg/hl)/(105 CFU/ml)]
into receiving waters has resulted in many varieties of wastewater treatment facilities. During these treatmerit processes, a large volume of sludge is generated. The sludge volume is approx. 1% of the wastewater treated, with a solids content of 1-7% (Lester et al.,
v: = specific product formation rate of acidophilic species [(mg/hl)/(10 s CFU/ml)]
The overall process taking place in the reactor is visualized as the work of two distinct sulfur-oxidizing bacterial species, a less acidophilic species (pHop t 7.0) and an acidophilic species (pHop,4.0). The less aeidophilic species grows over the pH range of 7.0-4.0, whereas growth of the acidophilic species occurs in the pH range of 4.0-1.5. Sewage sludges normally have a pH of 6.0-7.0, under which conditions the less acidophilic species grows and the acidophilie species does not. The growth of less
INTRODUCTION Growing awareness of the harm done to the environment by the indiscriminate disposal of waste streams *Author to whom all correspondence should be addressed,
1983; Davis, 1986). Since many of the heavy metals present in the original wastewater become concentrated in the sludge, disposal of this metal-laden material may represent an environmental hazard (Tyagi and Couillard, 1989). Recently, a novel method has been developed for the removal of heavy metals from sewage sludge, based on the bioleaching of the sludge by a consortium of sulfur-oxidizing bacteria present in the sludge (Jain and Tyagi, 1992; Blais et al., 1992). The effect of system parameters such as the sludge solids concentration, medium pH, etc. on bacterial growth, acid production and metal solubilization process has already been investigated (Sreekrishnan et al., 1993). In this paper, we incorporate the information gathered on the system kinetics into a mathematical model for the bioleaching of metals in a batch reactor. CONCEPTUAL MODEL OF THE PROCESS
1653
1654
R.D. TYAGI et al.
acidophilic species in the presence of elemental sulfur is associated with the production of sulfuric acid, which brings down the medium pH. The lowering of medium pH has an adverse effect on the specific growth rate of the less acidophilic bacteria but they continue to grow at a reduced rate, resulting in further acid production, until the sludge pH reaches a value of 4.0. At this point, the less acidophilic species ceases to grow and the acidophilic species takes over the task of acid production. The growth of the acidophilic species is also adversely affected by the progressively lower pH conditions encountered as a result of continued acid formation. Finally, at a pH of about 1.5, the growth of the acidophilic species also ceases. The process of metal solubilization proceeds in parallel with the acid production process. The quantity of metal(s) leached into the liquid phase depends on the sludge pH, sludge type (or, speciation of metal in the sludge) and the initial metal concentration present in the sludge, We now proceed to develop the mathematical expressions that can suitably express each of the steps as envisaged above. Dependence o f the growth rate o f the less acidophilic bacteria on medium p H
computed as - 1.33 ± 0.05. Substituting this value in equation (4), we get /h =/z*
(5) 0.616 It is worth noting that at a pH of 4.0, from equation (5), #~ = 0.09/~ implying very low specific growth rates for the less acidophilic species at this pH. Thus the experimentally determined value of CI supports the model assumption that growth of the less acidophilic species ceases at a pH of 4.0.
o.60 a o.80 o.lo
: /
= -o.lo
Specific growth rate (p) is defined as 1 dN # - N dt
- 1.33 + ln(pH)
-o.3o (1)
-o.8o 0
where N is the number of bacterial cells (colony forming units/ml) and t is the time. It has been observed that the growth rate of the less acidophilic bacteria bears a linear relationship to the natural logarithm of the medium pH (Sreekrishnan et al., 1993):
2
,
,
4
6
8
pH
0.so b
#~ = A~ + B~ in(pH)
(2)
where /.lI ~-specific growth rate of the less acidophilic species at that pH A 1, BI = constants. If #* is the specific growth rate of the less acidophilic species at a pH of 7.0, #~ = Aj + Bi In(7.0)
(3)
0.80
. ~
~
o.lo
:. / r - f / "~ -o.lo ~ = " . -0.30
Equations (2) and (3) can be combined to yield C l Jl-
-0, 50 0.00
ln(pH)
/ij =/~* Cl + in(7.0)
(4)
where C1 =A1/BI.
Specific growth rates were calculated from experimental growth data (i.e. by differentiating the cell concentration vs time curve) at various pH values for the less acidophilic species and plotted as shown in Fig. l(a) and (b). From these and many other similar curves for various sludges, the value of C~ was
1.60
. . 4.80
3.20
. . 6.40
8.00
pH
Fig. 1. Plot of the specificgrowth rate of the less acidophilic bacteria at various sludge pH values [equation (5)]. (a) Various types of sludges: &, non-digested sludge from St-Georges de Beauce; O, non-digested sludge from Beauceville; , , aerobically digested Black Lake sludge; l , aerobically digested sludge from Ste-Claire; and I-q,anaerobically digested sludge from Valcartier. (b) Same sludge (aerobically digested Black Lake sludge) with various solids concentrations: &, 7.0 g/l; O, 22.8 g/l; O, 71.8 g/l; and I , 70.0g/I.
Mathematical model for metal bioleaching
>-
5 -
4
1655
o.so
/•
--
/
~
0.40
•
. ~ -'. 2_ ~
~.
o.
0.20
.~
~
O.lO
I/" 0.00
i
I
J
i
0.03
0.06 g l ' h-1
0.09
0.12
[
Fig. 2. Plot of the specific product (acid) formation rate against specific growth rate for the less acidophilic bacteria [equation (7): vI = 35 + 1.05/h].
Relation between growth and product (sulfate)formation for the less acidophilic bacteria Sulfate production by the less acidophilic species is modeled as a case of growth-associated product formation, i.e. the rate of production of sulfate is directly proportional to the bacterial growth rate. This also implies that the substrate is consumed for growth as well as sulfate production in the same proportion. Consequently, the specific growth rate (p,) vs specific product formation rate ( h ) graph is a straight line of slope k~ that passes through the origin. The specific product formation rate (v) is defined as v = 1 d (SO4) N dt vl = k, p
(6)
The specific product formation rates and specific
' 0.08
J 0.08
' 0.09
0.12
p.~, h -z Fig. 4. Plot of the specific product (acid) formation rate against specific growth rate for the acidophilic bacteria [equation (15): v2 = (4.0 + 0.3) P2]. growth rates were calculated for various sludges and plotted as shown in Fig. 2. The data points were somewhat scattered but a straight line could be fitted. From these, we find the value of kj to be 35.0 4- 1.05. Hence •1
=
35.0 p,
(7)
In equation (7),/Zl has units of h - ' and Vl has units of (mg/l h)/(105 CFU/ml).
Relation between medium sulfate concentration andpH in the less acidophilic species' range The buffering capacity index (BCI) for the sludge has been defined elsewhere (Sreekrishnan et al., 1993) and is represented by the following equation:
d(SO,) BCI = d (pH)p.=4.0 = b
(8)
The pH vs SO4 concentration profile is assumed to follow a path such that the slope of the curve varies linearly from a pH of 7.0 to a pH of 4.0.
0.20
o.10
If
o.12
d (S04) d (pH)
pH=7.0
=
a
(9)
then, at any value of pH such that 4.0 ~
7& = 0.08
_
d(SO,)= (b-a) .(7.0-pH)+a d (pH) (7.0-4.0)
•*
' o.0o
1.oo
(10)
Dependence of the growth rate of the acidophilic bacteria on medium p H
0.04
0.0o o.0o
0.00 0.00
2.4o
a.2o
4.00
pH Fig. 3. Specific growth rate variation with sludge pH for acidophilic bacteria [equation (13): ,uz=p*(-0.40+ ln(pH))/0.986]: &, non-digested sludge from St-Georges de Beauce; and O, aerobically digested Black Lake sludge.
For the acidophilic species the growth rate was also found to vary linearly as the natural logarithm of the sludge pH (Sreekrishnan et al., 1993).
#2 = A2 + B2 ln(pH)
(11)
where /~2 = specific growth rate of acidophilic species A 2, B2 = constants.
1656
R.D. TYAGI et /
al.
INPUT / , Nt, N2, pH, 504, ~ 1 " ~2'' b
t
/
INPUT
/
a, c
t
Calculate la Calculate v
t
(Eqn. 5) ] (Eqn. 6) ]
V
N <-- Nt
[
A SO 4 -v*N At
I [~
t <--t +At
]
V
504
=
ASO4 At
* At
V dSO4 (Eqn. 10) dp---H--
Calculate
V pH <-- pH + (-A SO4/
Y e s + t N2 N <--
V
dS04 )
No [Calculate Ix
(Eqn. 5)
[Calculate Ix
(Eqn. 14)
VN <-- N + (La * N * At)
I Calculatev
(Eqn. 15)
I Calculatev
Aso 4
--At = v * N
~
,SO4
<-.
(Eqn. 6)
SO 4 + ASO4
, V I
t<--t+At t A SO4
A SO 4 =
/
At
* At
Calculate dSO4
~Eq..
OUTPUT / t, N, pH, SO 4, Ix, v
16)
dSO 4
pH <-- pH + (-A SO4/d"~ - ) ] Calculate p,
(Eqn. 14) I
¢ L N~--N+.*N*At
I
[
[
i
SO 4 <-- SO 4 -t- ASO 4
[ Calculate v
(Eqn. 15) [
t, N, pH, SO 4, Ix,
Yes
No
Fig. 5. Flow chart for simulating bacterial growth, acid production and sludge pH
Mathematical model for metal bioleaching 1000
1657
800
0
o
•
800
640
~
c
:~
600
"~
400
~> ,
0
480
0
"> . r"
320
r"
o
o
200
160
0 =-~--~ - " ' - - -
0
24
48
" -" -" -" -
72
96
0-z
120
0
-::nP.x:z._,
24
T i m e (h)
=
acidophilie
,
48
, ,
72
.
96
120
Time (h)
~-
less
acidophilic
--4--
simulated
Fig. 6. Experimental and simulated bacterial growth curves. (a) N~ = l; N 2 = 18; initial pH = 6.92; initial sulfate concentration = 780 mg/l; #1 = 0.175 h-~; #2 = 0.11 h-~; BCI = 162; a = 0.025 BCI; and b = 20.0 BC1. (b) N~ = 0.433; N 2 = 15.0; initial p H = 6.95; initial sulfate concentration = 1500 mg/1;/~t = 0.175 h-~; /J2 = 0.11 h - t ; BCI = 565; a = 0.025 BCI; and b = 4.0 BCI.
I f / ~ ' r e p r e s e n t s t h e specific g r o w t h r a t e o f t h e a c i d o p h i l i c species a t a p H o f 4.0, C 2 + In(pH) P2 = P *
(12)
C2 + ln(4.0)
T h e p H vs #2 g r a p h s were p l o t t e d f o r t h e a c i d o p h i l i c species as s h o w n in Fig. 3. F r o m m a n y s u c h g r a p h s , t h e a v e r a g e v a l u e o f C2 w a s f o u n d to be - 0 . 4 -I- 0.05. Hence,
where C2 = A 2 / B :
P2 = P ~
7.00
7.00
5.60
5.60
4.20
4,20
2.80
2.80
1.40
1.40
0.00 0
'
'
'
'
24
48
72
96
~ "
0.00
120
0
T i m e (h) -"
experimental
-0.40 + ln(pH) 0.986
"""
T
,
,
,
,
1
24
48
72
96
120
T i m e (h)
--e--
simulated
Fig. 7. Experimental and simulated pH profiles. (a) N~ = l; N 2 = 18; initial pH = 6.92; initial sulfate concentration = 780 mg/1;/z t = 0. i 75 h-~;/t 2 = 0.11 h-~; BCI = 162; a = 0.025 BCI; and b = 20.0 BCI. (b) N~=0.433; N2=15.0; initial p H = 6 . 9 5 ; initial sulfate c o n c e n t r a t i o n = 1 5 0 0 m g / 1 ; # 1 = 0 . 1 7 5 h - ~ ; /t 2 = 0.11 h - t ; BCI = 565; a =0.025 BCI; and b = 4.0 BCI.
(13)
R.D. TTAGIet al.
1658 8200
6000
4180
~
E .
.
c o
o
= c o o C o
o
4800
E 3120
=
3600
c • o ~ o
2080
o
2400
1040 _ = ( ~
~
1200
0
0
I
I
24
48
I
I
72
06
0
120
0
I
I
I
I
24
48
72
06
Time (h) .L
120
Time (h) experimental
--*--
simulated
Fig. 8. Experimental and simulated sulfate concentrations. (a) N] = l; N 2 = 18; initial pH = 6.92; initial sulfate concentration = 780 rag/l; /~, = 0.175 h - I ; / h = 0.11 h-I; BCI = 162; a = 0.025 BCI; and b = 20.0 BCI. (b) N I = 0.433; N 2 = 15.0; initial pH = 6.95; initial sulfate concentration = 1500 mg/l; #~ = 0.175 h-~; /a2 = 0.11 h-~; BCI = 565; a = 0.025 BCI; and b = 4.0 BCI.
Again, at a p H of 1.5,
increases linearly to a value of c such that
/a2 = 0.0055 ~* This means that acidophilic species is consistent with acidophilic species
d (SO4)
the specific growth rate of the is very low at a pH of 1.5, which the model assumption that the stops growing at a pH of 1.5.
c
pH=L0
Hence, the pH-SO4 concentration curve can be represented by the equation
Relation between growth and product (sulfate) formation for the acidophilic bacteria
d (SO4) d (pH)
The production of acid by the acidophilic species is modeled as a case of growth-associated product formation, as in the case of less acidophilic bacteria. The v2 vs/t2 graph is a straight line through the origin with a slope of k2 such that V2 = k 2 / ~ 2
~
b + (c3.0b)" (4.0 - pH)
(16)
CADMIUM
100
(14) 80
Sulfate production rates and growth rates were computed for the acidophilic species growing in a number of sludges and the v2 vs/~2 graph was plotted. Figure 4 is an example o f one such plot. F r o m these graphs, the average value of k 2 was found to be 4.0 + 0.3: v2 = 4.0/~2
(15)
where /~2 has units of h -l and v2 has units of (mg/l h)/(105CFU/ml).
Relation between medium sulfate concentration and p H in the acidophilic species' range d (SO4) BCI - d (pH) pH=4.0 ~" b
(8)
The slope of the p H vs SO4 concentration curve is modeled such that the slope of b at a pH of 4.0
~.~
60
-~
40
"~ 20
o 2
4
6
a
pH
Fig. 9. Solubility charts for cadmium. (a) Non-digested sludges; (b) anaerobically digested sludges; and (c) aerobically digested sludges.
Mathematical model for metal biol._thing
1659
CHROMIUM
NICKEL
100
100
8S
80
ss
=~
so
40
-a
40
20
~
0 0
' 2
20
' 4
' 6
0 8
' 2
0
pH
4
' 6
8
pH
Fig. 10. Solubility charts f o r chromium. (a) Non-digested
Fig.
sludges; Co) anaerobically digested sludges; and (c) aerobically digested sludges,
sludges; Co) anaerobically digested sludges; and (c) aerobically digested sludges.
sludge
Model input parameters The following input parameters have to be specified if the model is to be used to predict bacterial growth, pH vs time profiles and acid production: (i) initial number of viable iess-acidophilic bacterial cells in the sludge (N~) (ii) initial number of viable acidophilic bacterial cells in the sludge (N2) (iii) initial sludge pH (iv) initial concentration of sulfate in the
12. Solubility charts f o r nickel. (a) Non-digest=d
(v) specific growth rate of the less acidophilic species at a pH of 7.0 (p~) (vi) specific growth rate of the acidophilic species at a pH of 4.0 (p~) (vii) buffering capacity index for the sludge (b) (viii) value of a (slope of pH--SO4 curve at pH 7.0) and b (slope of pH SO4 curve at pH 4.0). The values of a and b were found to be related to the buffering capacity index and the following relation
COPPER
LEAD
100
100
80
80
5°0
so -
"~
40
~
20
0 --
2S
' 2
4
' 6
S 8
pH
Fig. 11. Solubility charts for copper. (a) Non-digested sludges; CO) anaerobically digested sludges; and (c) acre* bically digested sludges, WR 27/I I--E
40
2
' 4
' 6
8
pH
Fig. 13. Solubility charts for lead. (a) Non-digested sludgvs; Co) anaerobically digested sludges; and (c) aerobically digested sludges.
1660
R.D. TYAOI et aI.
ZINC
using various sludges were used to compare the model output with actual process performance. It was found that the model gives a reasonably good prediction of bacterial growth, and of temporal changes in pH and sulfate concentration in the reactor. Figure 6(a) and (b), Fig. 7(a) and Co) and Fig. 8(a) and (b) give the comparison between experimental and simulated data for cell growth, pH profile and sulfate concentration profile for experiments carried out in shake flasks.
100
80 e0 o
M o d e l shortcomings
40 c
Compared with the shake flask experimental data, one shortcoming for the model as presented is that it does not take into consideration any lag time for bacterial growth. According to the model, the less 0 acidophilic bacteria start growing exponentially at 0 2 4 6 s time t = 0 whereas in a shake flask, bacterial growth exhibits an initial lag phase. The lag time (time pH elapsed before the bacteria actually start growing Fig. 14. Solubility charts for zinc. (a) Non-digested sludges; exponentially) could easily be included in the model (b) anaerobically digested sludges; and (c) aerobically if exact information regarding the lag were available. digested sludges. The duration of the lag phase will depend on a number of parameters such as the nature of the was derived after examining a number of pH-SO4 sludge, inoculum age, the presence of toxic comconcentration profiles: pounds, etc. The specific growth rate of the bacteria is modeled a = Ab as a function of pH only. This is contrary to the where A varied over the range 0.01-0.025. A value of normal practice of modeling growth as a function of 0.025 was found appropriate for sludges having low limiting substrate concentration (or inhibitor concenBCI values, such as 200 or less; the value of A tration). We consider the change in medium pH and decreased with increasing BCI value. Similarly, its effect on growth rate to be more important than c = Cb any possible substrate limitation on bacterial growth. In fact, this was verified by raising the pH to 7.0 by where alkali addition after it had been lowered down to 1.5 C = 4.0; b > 500 by the bioleaching process; every time this was done, the bacteria started growing again, producing acid C = 10.0-20.0; 200 ~
Table 1. Validityrange for metal solubilizationcharts Validity range (rag metal/kg dry sludge initially present) Type of sludge Non-digested Anaerobically digested Aerobically digested
CA 0-24 0-31
Cr 0-475 0-650
Cu Ni Pb Zn 0-2100 0-222 0-278 0-1460 0-3690 0-68 0--650 0-1930
0-11
0-1720 0-1830 0-177 0-336 0-1200
Mathematical model for metal bioleaching metal solubilization process are not yet available. For this reason, we are not in a position, at least for the present, to extend the model to the metal solubilization process. To partially offset this shortcoming, metal solubilization can be predicted using empirical solubility charts, Metal solubility charts
Of all the factors that are seen to affect the metal solubilization process, the most difficult ones to incorporate in the model are the concentrations, relative as well as absolute, of each metal present in the sludge, and the metal speciation in the sludge. To circumvent this problem, we express metal solubilities in the form of a chart with metal concentration in the liquid phase, expressed as a percentage of the metal concentration initially present in the sludge, presented as a function of sludge pH. The effect of metal speciation on metal solubility is accounted for by using three different solubility charts for each metal depending on whether the sludge is non-digested, aerobically digested or anaerobically digested. These are shown in Figs 9-14 for the metals Cd, Cr, Cu, Ni, Pb and Zn. These correlation charts were prepared using actual data from laboratory experiments using 20 different types of sludges. These graphs were obtained by drawing a smooth curve through the solubilization values obtained experimentally. Based on the sludges used for the study, the solubilization data are valid for initial metal concentration levels as given in Table 1. However, these are not the concentration limits for metal solubilization from sewage sludges but are the limits for metal concentrations used in the present work. The validity of these charts for initial metal concentrations lying outside these limits will have to be verified before using these charts. Again, these charts are meant to provide only an approximate value for the solubilization to be expected, in the absence of exact mathematical relationships to predict solubilization values. As already mentioned, even though pH is the most important parameter to influence solubilization levels, it is not the only factor. Also, classification of sludges into three groups, i.e. non-digested, anaerobically digested and aerobically digested sludges, cannot take into account all the variations seen among sewage sludges from various sources. It can be seen, from these charts, that for Cr and Cu, the solubilization achieved for a given sludge pH was almost the same irrespective of the sludge being
1661
non-digested, anaerobically digested or aerobically digested. For Cd and Pb, solubilization was achieved more readily in aerobically digested sludges followed by anaerobically digested sludges and non-digested sludges, in that order. For Ni, initial solubilization occurred earlier in aerobically digested sludges compared to the other two sludge types. In the case of Zn, solubilization was easiest for aerobically digested sludges, followed by non-digested sludges and anaerobically digested sludges, in that order. SUMMARYAND CONCLUSION Mathematical relations, based on experimental data, were written for various steps in the metal bioleaching process. These relations were incorporated into a model that could satisfactorily predict the bacterial growth and acid production processes. The metal solubilization process could not be incorporated directly into the model due to the influence of too many unquantified factors on this step. Instead, correlation charts were developed which could, within some limits, predict the metal solubilization using the sludge pH profile output generated by the model and the type of sludge used. Acknowledgements--We thank the Natural Sciences and Engineering Research Council of Canada (Grants A4984 and STR0100710), the Qu6bec Fonds pour la Formation de chercheurs et l'Aide ~i la Recherche (Grants FCAR 90-AS9713)and the Universit~ due Quebec (FODAR) for providing financial assistance to carry out this work.
REFEnENCF..S BlaisJ. F., Tyagi R. D. and Auclair J. C. (1992) Bioleaching of trace metals from sewage sludge by indigenous sulfur oxidizingbacteria. J. envir. Engng 118, 690-707. Davis R. D. (1986) Cadmium in sludges used as fertilisers. Experient. Suppl. 50, 55. Jain D. K. and Tyagi R. D. (1992) Leaching of heavy metals from anaerobic sewage sludge by sulfur oxidizing bacteria. Enzym. Microb. Technoi. 14, 376--383. Lester J. N., Sterrit R. M. and Kirk P. W. W. (1983) Significanceand behaviour of heavy metals in wastewater treatment process--II: Sludge treatment and disposal. Sci. Total Envir. 30, 45-83. Sreekrishnan T. R., Tyagi and R. D., Blais J. F. and Campbell P. G. C. (1993) Kinetics of heavy metal bioleaching from sewage sludge---I. Effect of process parameters.War. Res. 27, 1641-1651. Tyagi R. D. and CouiUard D. (1989) Bacterial leaching of metals from sludge. In Encyclopedia of Environmental Control Technology, I/oi-3: Wastewater Treatment Technology (Edited by Cheremisinoff P. E.), pp. 557-590. Gulf Publishing Co., Texas, U.S.A.