EEE Instrumentation and Measurement Technology Conference Brussels, Belgium, June 4-6, 1996

Fast On-Line Measurement of the Respiration Rate in Activated Sludge Systems

?Sebastian Y. C. Catunda (student member IEEE), TGurdip S. Deep (senior member IEEE), $Adrianus C. van Haandel and TRaimmdo C. S. Freire (member IEEE)

?UFPB/CCT/DEE, $UFPB/CCT/DEC, AV. Aprigio Veloso, 882, Bodocongo 58 109 -970, Campma Grande, PB, Brad

E-Mail: catunda@dee.@b.br

Abstract - The measurement and utilization of the respiration rate or oxygen uptake rate (OUR) is very important in activated sludge system control (31 It provides information about the plant loading, activated sludge quality and can indicate the presence of toxic elements that can poison the system. (7j The aisting methods for continuously measuring and estimating the respiration rate have, generally, a long sampling interval of a few minutes An alternative respiration rate meter with much shorter response time is proposed The results of simulation studies, as well as experimental results of a microcontroller based data acquisition system connected to a PC, around a laboratory scale biological reactor are presented

I. PJTRODUCTION ASTEWATER treatment in activated sludge systems is carried out by using aerobic bacteria that oxidize

organic matter. The oxygen consumed by these microorganisms is replaced in the system by aerators. The oxygen respiration rate or oxygen uptake rate (OUR) is the microorganism oxygen consumption per unit time and is one of the few accessible parameters to quantlfy the metabolism rate of the activated sludge. OUR is proportional to the microorganism concentration and depends on the quality of the incoming wastewater. Thus, this parameter is very suitable for monitoring and control of the activated sludge system. It is a measure for the quality of the activated sludge and may indicate the presence of toxic elements, since these tend to reduce the microorganism metabolism rate.

In practice, OUR can be estimated from the variation of the dmolved oxygen (DO) concentration, that can be measured with a specfic electrode. There are basically two configurations for estimating the respiration rate: (1) batch units and (2) continuous flow units. In the former a sample of mixed liquor (the contents of the activated sludge reactor, composed of microorganism flocks and influent) is

withdrawn and placed in a reactor. There, the sample is aerated during a certain time period and after interruption, OUR is calculated from the observed decrease of the OD concentration with time. In the second type, a closed vessel is used with a continuous flow of mixed liquor. OUR is estimated from the measured DO concentration of the incoming and of outgoing mixed liquor and its retention time in the vessel.

An alternative method to estimate OUR is proposed, which consists in controlling the DO concentration at some particular reference average value by using pulse width modulation (PWMJ of the aeration cycle. The OUR value is obtained from the relation between the “on” and “off’ intervals of the aeration cycle of the reactor. The main advantage of this method over others is its short and constant sampling interval. It can be used directly for the determination of OUR in small activated sludge plants in which the aerators can be switched on and off frequently. In order to use the method in large scale plants it is necessary to build and operate a reduced scale laboratory unit for measurement and control of DO concentration.

II. DYNAMICS OF THE DO CONCENTRATION The variation of the DO concentration with time in an activated sludge plant can be expressed as follows[l]:

dY(0 - --

(1) dt

q Y & ) V - At)]+ K,, ( 4 t ) X Y s m - Y(4- R(t)

where: y(t) yj,,(t) ysat Q(t) V

is the DO concentration in m a ; is the DO concentration in the influent flow, is the DO saturation concentration; is the influent flow rate in L/h; is the reactor volume in liters, L;

0-7803-331 2-8/96/$5.0001996 IEEE 1320

KLxu) t III. CLASSICAL METHOD FOR

ESTIMATING OUR The classical method for OUR estimation in reduced scale units is similar to the method for batch type units. It consists of aerating the mixed liquor in the reactor until the DO concentration attains a predetermined value, denominated the upper setpoint. At this point the aeration is interrupted and the decrease of DO with time is observed. When the DO concentration reaches the lower setpoint, aeration is reinitiated and a new cycle beg" OUR is calculated from the observed rate of decrease of the DO concentration. T h s decrease represents the respiration rate only, if the rate of change of DO concentration inside the reactor due to incoming and outgoing flows is negligible.

If the discrete DO concentration values with time (y,,, t,) are available, we can use linear regression to fit a straight line, gwen by y = a - Ret, to this data, in the LMS (least mean square) sense, with the cost hnction defined as:

N 2

U,,,, U, air flow rate

Fig 1 .Typical oxygen transfer rate versus air flow rate curve

&,(U) is the DO transfer rate h-'; is the air flow in L/h;

R(t) is the OUR in m g/L/h. The DO transfer rate, Kl,(u), varies with the air flow, U.

A typical K,, x U curve is shown in Fig. 1 [3]. In a reduced scale system aerators are of the on-off type and in that case the transfer rate has only two values: K,, when the aerator is on and zero when it is off (Fig. 1). By utilizing these aerators with PWM control and aeration cycle having a constant total period much shorter than the time constant associated with oxygen transfer one has:

U

where: K,(k) U,,

z@) T K,,

The discrete time model for the DO concentration given by (3) can be obtained from the continuous time model (1) by using a zero order hold (ZOH) [3].

is the new oxygen transfer rate; is the air flow rate when the aerator is on; is the interval in which the aerator is on; is the period of the aeration cycle; is the value of K,, corresponding to U,=.

(3) + K m (k)(~sat - d k ) ) - R( ')I

where h is the sampling interval.

The dissolved oxygen sensor response can be modeled as a 1st order system, with delay of tm [7]. The discrete model for this sensor can be written as: ym( k + 1) = cm.ym( k ) + (1 - cm)y(k) (4)

where y, is the measured DO concentration and em = ex&- hlhn).

i=l

Equating the partial derivatives of h w.r.t. a and Re to zero, one may obtain Re to be [6]:

N N N

A. Simulation Results A simulation run for evaluating the classical method was made using the MATLAB [4]. This was based on the solution of equations (3), (4) and (6) to calculate the values of y(k), y,(k) and Re@) respectively. The values of R e K, and tm were chosen to be close to the experimentally determined values. The DO concentration y , was quantized with 8 bits resolution and with 8mgQJL as full scale value. The upper and lower setpoints were chosen to be 2,5 e 1,5 respectively and the duration of the experiment was 120 minutes. At t = 60min, OUR was changed from 10 to 40 mgoz/L/h and the resulting changes in the DO concentration and values of R and Re with time are graphically shown in Figs. 2A and 2B.

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From (1 0) and (4), one has:

(9 - 1)(9 - cm)y,(k) = h(1- cm)[.(k) - qk)]

y m ( k ) = @ ( q - 1>(q - cm>

(11)

and combining (7) and (1 I), we have:

h( l -cm)(~ , .4 -ym(k)) h ( l - c m ) ~ ( k ) (12)

( q - 1)(q - cm) We observe from Fig. 3 that R(k) is not included in the

feedback loop and, thus, for determining the closed loop transfer function, we may ignore R(k) in (12). Thus, we have:

3

2 5

2

1 5

20 40 60 80 100 . 12C t ( m )

Fig. 2. Results of the classlcal method simulatiao.(a): the DO concentration, y, aud the measured value, ym.. @). the OUR. R, and the estimated OUR, R,.

In Fig. 2a, the time interval for the rising DO concentration corresponds to the aeration phase and the time interval for the falling DO concentration to the non aeration phase. The respiration rate is calculated using data acquired during the non aeration phase and is plotted in Fig.2b at the end of this phase.

IV. FAST RESPONSE MEASUREMENT METHOD (FRMM)

The FRMM consists in controlling the mean DO concentration at a pre-defined value. This is done by implementing a proportional controller. The control variable is the oxygen transfer rate, K,, as gven by (2) . For ths, it is necessary to determine the maximum value of K, and this will be discussed later. For the proportional controller, we have:

.( k ) = kp. e (k ) = kp. ( Y N f - Y m ( k ) ) > and ( 7 )

where yref is the reference value of the DO concentration

combining (3) and (S), we have: Considering the inflow and outflow to be negligible and

y( k + 1) = y(k ) + h[ U( k ) - x( k )]

( 4 - I)”@) = - Rp)] (10)

(9)

This equation can be written as:

where, q is the discrete time shifting operator, qy(k) =

Y(k+l).

In order to be able to use the maximum gain in the feedback loop, with the proportional controller, without overshoot, the system poles located at 1 and cm are re- allocated to be positioned at the same point. For this condition kp is given as [SI:

Combining (2) and (S), we obtain the value of ~ ( k ) as:

In equation ( 1 5 ) , we have used the DO concentration measured value, ym(k), and not the actual value, y(k), of DO in the reactor. The value of y(k) is not available in the actual experimental implementations. Typically, this approximation causes a maximum error of about 2%. This was verified during the simulation experiment discussed later.

In the controller design, the value of the reference of DO concentration, y,fi is chosen so that for y,,, > 0, K,(k) =

Kmu. This should maximize the measurement range of R. Thus, from (8) and (16), considering y,(k) = y,,, and K,(k) = KmM, we have.

(16) k;,mYmt - Ymin(Kmm - k p )

k p Yref =

An estimate of OUR, namely Re@), can be approximately obtained from (9) and (8) to be:

This estimate of OUR, as determined from (17), is not in error when the reactor is in steady state. For any change in the respiration rate or OUR, the tracking of R(k) by the estimate R,(k) depends on the location of the poles of the closed loop system. Fig. 3 shows the control loop for the DO concentration and estimation of OUR value.

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A. Estimation of K,,,, The value of K,, can be estimated using experimentally measured values of DO concentration and linear regression with this values in the LMS sense. This parameter varies slowly with time, thus, there is no need for its frequent estimate. Using w(k) = ysat - y(k) and neglecting the in and out flow rate effect, (3) can be written as: w(k + 1) = w(k)[ l - h.K,(k) (18)

Assume that the respiration rate (OUR) is known to be r and that it does not vary during the measurement interval. This kind of situation actually exists when respiration rate is measured during the endogenous phase (when the substrate is used up and the bacteria consumes oxygen only to keep themselves alive). With constant aeration, (1 8) can be rewritten as:

w ( k + l ) = w(k)[l-h.K,,]+h.r (19)

Equation (19) represents a straight line in the w(k+1) x w(k) plane and can be rewritten as:

where: Y = w(k+l), X = w(k), b = 1 - h.K,, and a = h.r.

The values b and thus Kmm can be estimated, if the value of a is known in (20), by using linear regression as done in case of (5) [6] , where:

Y = X . b + a (20)

N

i = l

1-b Kmm =-- h

The value of w(k) is calculated from the measured value of DO concentration, y,(k) and not y(k) . The error in the estimation of K,, due to this approximation is small, since the response time associated with oxygen transfer rate is much longer than the sensor response time. It is observed in practice, that the value of this is close to unity and ;a small

error in h will cause a large error in b. Thus, it is very important to use several points when estimating KmM.

B. Simulation results A simulation study for the FRMM was carried out using MATLAB program [4] and from the discrete models described in (3), (4), (7), (S), (15) e (17). The values of R, K, and tm employed were close to the experimental values. The y, values were discretized with a resolution of 8 bits and full scale value of 8mgQL. The reference value for DO concentration was set to 2mg%/L, and the proportional gain, kp, was chosen to be 45, yielding the ymln value to be around 0.5mgQIL. The PWM repetition period was 2s and the total experiment duration was chosen to be 120 minutes. At t560min, an abrupt increase in the OUR from 10 to 40 mgQ/L/h was forced on the system. The results of the simulation are shown in Fig. 4, which depicts the respiration rate and DO concentration variations.

The filtered OUR curve, RA in Fig. 4b, tracks the estimated OUR curve, Re, while this one tracks the real OUR curve, R.

2 5 t i

10

'0 20 40 60 80 100 120 t(&)

Fig. 4. Simulation results for the FRMM. (a): DO concentration and measured DO concentration. (b): OUR, estimated OUR and filtered OIJR.

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V. ACTIVATED SLUDGE TREATMENT PROTOTYPE

An activated sludge treatment prototype was constructed in the laboratory in order to carry out the OUR estimation tests (Fig. 5) . The prototype is constituted of a 6 liter reactor, a stirrer and an aerator. A microcontroller was used to acquire data and control the external devices like aeration pump and stirrer. A real time executive was designed and implemented for the supervision of the microcontroller operation [2]. The microcontroller is serially connected to a 486-PC where the acquired data and other estimated parameters are graphically presented on the monitor screen. The activated sludge was obtained naturally without initial inoculation and adding artlficial substrate (food) in the rector with continuous aeration.

VI. EXPERZMENTAL RESULTS Experiments were carried out using the prototype schematically shown in Fig. 5, for testing the two methods described for the OUR estimation. The OUR was estimated during the endogenous phase and after 60 minutes, substrate was added in the reactor. The experimental results for the classical method are shown in Fig. 6.

In the above figure, the sampling interval in the endogenous phase is almost constant at 6 minutes. After addition of substrate, the sampling interval is decreased due to the microorganisms increased consumption of OD.

The experimental results for the F R M M are shown in Fig. 7.

The DO curve in Fig. 7a is noisy and the respiration rate curve, Fig. 7b, is still more noisy. Ths is primarily due to the

,.._..._.__.._____._.____...___. I I : I 1 1-4 microcontroller 1 microcomputer I 486

motor

j I 1 I It

................................

Fig 5. Activated sludge treatment syiean prdckype and data acquisition system

3

2.5

2

1.5

I 0 20 40 60 80 100 120

1L

m g O z m t(min>

substrate additim Re(k) 0 0 0 0 0 0 0

@> 0 0 0 0 0 0

'0 20 40 60 80 100 120

Fig 6. Fzperimmtal r& for the classical m & d (a): DO concentratim measured values. @): estimated OUR.

quantization of the DO signal and the choice of the mean value to be % of the full scale of the A D conversion. This noise has however been filtered and is shown in Fig. 7c.

From the simulation and experimental results one can make the following comparative observations.

The proposed method (FRMM) has a short and constant sampling interval, equal to the PWM period. While the classical method has a long and varying sampling interval (usually longer than 5 minutes), depending on the respiration rate and oxygen transfer rate.

- 1 I

0 20 40 60 80 100 120 11

mg9fL'h t(min>

20

10

O: i o i o 60 i o 160 1:o t(min)

Fig 7. Expai"tal results for the FRMM, (a): Do cancu~tratim measured values. @): estimated OUR. (e): filtered OUR.

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The F R M M covers a larger range of Re than the classical method. In the case of FRMM, Rmar = Kmar.@sot - ymzn).p and for the classical method, Rm, = Km,.bsat - yusp). Once we know that ymt, is generally smaller than y,,, this justifies the above observation.

The response curve of the estimated OUR with FRMM presents relatively rapid variations (Fig. 7b), which can be minimized using a digital filter. The filtered response Rf is shown in Fig. 7c.

The response time obtainable with the FRMM method, is determined by the combination of the control system1 and filter response times. The classical method response time is close to its sampling interval, which is variable.

Both methods can be applied for measuring the OUR directly in the reduced scale activated sludge treatment plants, in whch the aerators can be switched on and off frequently.

VII. CONCLUSIONS We have presented the design considerations and the implementation of DO concentration acquisition system with the objective of estimating the oxygen uptake rate. A classical method and a proposed alternative method for measuring the OUR have been described, implemented and compared. Our experience has clearly demonstrated the superiority, in terms of response time, of the proposed method over the classical method. The proposed method has demonstrated less immunity to noise than the classical one. However this noise can be filtered using a digital filter.

Vm. ACKNOWLEDGMENTS The authors would like to thank the CNPq for the financial support in terms of study and research fellowships. Thanks are also due to many colleagues of the laboratory for many discussions.

IX. REFERENCES [a] Bastin, G. and D. Dochain (1990). On-line Estimation

and Adaptative Control of Bioreactors. Elsevier, Amsterdan

[2] Catunda, S. Y. Executivo em Tempo Real para o Microcontrolador 68HC 11 (Real time executive for the 68HC11 microcontroller). Research Report. COPELE-

[3] Lindberg, C.F. (1995) Control of Wastewater Treatment Plants. Doctoral Thesis, Uppsala Univerity, Uppsala, Sweden

[4] MATLAB Version 4: User’s Guide. The Mathworks inc., Prentice Hall, 1995

[5 ] Ogata, K. Modern Control Engennering, Prentice Hall, 1990.

[6] Press, W. H., et al. Numerical Receipes in C. Cambridge University Press, 1988 - Cambridge, USA.

[7] Spanjers, H.( 1993) Respirometry in Activated Sludge. Doctoral Thesis, Wageningen Agricutural University, Wageningen, the Netherlands - 1993.

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