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A novel control strategy for improved nitrogen removal in an alternating activated sludge process—part II. Control development
Wat. Res. Vol. 28, No. 3, pp. 535--542, 1994 Copyright © 1994 ElsevierScienceLtd Printed in Great Britain. All rights reserved 0043-1354/94 $6.00 + 0.00
Pergamon
A NOVEL CONTROL STRATEGY FOR IMPROVED NITROGEN REMOVAL IN AN ALTERNATING ACTIVATED SLUDGE PROCESS---PART II. CONTROL DEVELOPMENT H. ZHAO, S, H. ISAACS*, H. SOEBERa and M. KOMMEL~ Department of Chemical Engineering, Technical University of Denmark, Building 229, DK-2800 Lyngby, Denmark (First received January 1993; accepted in revised form June 1993) Abstract--The first part of this two part contribution dealt with an analysis of nitrification and denitrification in an alternating activated sludge nutrient removal process for the case of constant process conditions. The existence of an optimal cycle length, which is a function of the process conditions, was demonstrated and discussed. Based on these findings, this paper examines a control strategy by which the cycle length of an alternating activated sludge nutrient removal process is automatically adjusted in order to compensate for changing process conditions. Two control algorithms are proposed. One involves proportional-integral-derivative (PID) control which requires minimal process information and computational effort. The second is a model based predictive control (MBPC) technique, which introduces a feedforward element into the control strategy. The MBPC technique is examined using a relatively simple process model. The results of experimental demonstrations of these two algorithms in a pilot plant facility indicate their potential towards improving nitrogen removal in the alternating process. Key words--activated sludge process, biological nitrogen removal, nitrification, denitrification, modeling, process control, waste water treatment
NOMENCLATURE Throughout the paper, NH4-N and NOx-N denote the concentration of nitrogen as ammonia and as nitrate plus nitrite, respectively, Cat = NH4-N in tank T1 (mgl -t) Cnt = NOx-N in tank TI (mg 1-~) e = error employed in MBPC control (rag l -t) k = index (--) Kc = controller gain (minlmg -~) KD=controller parameter (--) Kt = controller parameter (--) LNC = lowest NO~-N concentration achieved in each cycle (used as feedback signal) (mgl -t) r d = denitrification rate [rag (1 min)- t] r n = nitrification rate [mg (1 min)- ~] to=cycle length (rain) t~ n =minimum allowable cycle length (min) t ~ x = maximum allowable cycle length (min) td = denitrification time per tank (min) t~ = nitrification time per tank (rain)
nitrogen and phosphorus from wastewaters in which nitrification and denitrification are performed in a cyclic semi-batch fashion in each of two aeration basins (Arvin, 1985; Einfeldt, 1992). Part I demonstrated the existence of an optimal operation cycle length for maximum nitrogen removal. It was shown that, for a given set of constant process conditions, a cycle length can be found where denitrification just comes to completion (i.e. nitrate is completely consumed) at the time when the next aerated nitrification phase is scheduled to begin. This corresponds to the cycle length which minimizes the concentration of nitrogen in the process effluent stream. Part I dealt with the case in which process conditions are constant over a time period long enough to allow a dynamic "steady state" to occur. In this context, "steady state" refers to the situation in which no further changes in the dynamics occur from cycle to cycle. In this second part the focus is on the dynamic case with the goal of improving process performance in the face of daily, weekly and seasonal variations in process conditions. These variations include diurnal and weekly fluctuations in nitrogen loading as well as variations in the nitrification and denitrification rates. The latter can be attributed to changes in, for example, seasonal temperatures, sludge activity and composition, and
INTRODUCTION The first part of this two part communication focused on an analysis o f the nitrogen dynamics in an alternating type activated sludge process (Zhao et al., 1994). This is the B I O - D E N I P H O f process for the biologically mediated removal of organic matter, *Author to whom all correspondence should be addressed, fThe BIO-DENPHO process is a patented process developed by I. Kruger Systems in cooperation with the department of Environmental Engineering at the Technical University of Denmark.
organic content of the incoming wastewater. As a consequence of these variations, the cycle length which maximizes nitrogen removal will change 535
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with time. Hence, a control strategy is developed in which the cycle length is automatically adjusted as process conditions vary in an attempt to maintain optimal conditions, In this paper, two control algorithms are presented together with demonstrations of their performance on a pilot plant scale. The first is a proportionalintegral-derivative (PID) feedback control algorithm (Smith, 1972) which requires minimal process information and is easily implemented. The PID algorithm has certain disadvantages, however, which include a need to choose suitable controller parameters, its non-adaptive nature, and the response delay associated with a purely feedback strategy. The second algorithm involves model based predictive control (MBPC) and is developed using a relatively simple process model. Implementation of the MBPC algorithm requires more process information and a greater degree of computational effort. However, due to its feedforward and adaptive nature, the MBPC algorithm provides for a more efficient control, THE CONTROL PROBLEM In conjunction with a description of the pilot plant facility, Part I illustrated the particular process control problem addressed in this investigation. It was shown that, as a result of diurnal increases in the wastewater's nitrogen content, the denitrification could not always come to completion when employing an operational strategy with a fixed cycle length, This resulted in an accumulation of NO~-N in the pilot plant's two tanks Tl and T2 with a subsequent increase in the process effluent NOx-N concentration, The observance of this behavior resulted in the more detailed analysis of nitrogen dynamics undertaken in Part I and the conclusion that this accumulation could be reduced or avoided through suitable adjustment of the cycle length. Summarizing the findings of Part I, the cycle length t¢ should be increased if insufficient time is available for dentrification. Although a larger t¢ will result in a higher nitrogen loading per tank per cycle, the dynamics are such that dentrification will come closer to completion (or will be completed) in the allotted time. If, on the other hand, NO~-N is totally consumed before the next nitrification phase is scheduled to begin again, tc should be decreased, as this will lead to a lesser concentration of total nitrogen in the process effluent. These findings are based on the operational strategy by which the aeration of a nitrifying tank is discontinued when ammonia is consumed. Hence, the aeration time per tank per cycle, t., also changes as t~ is adjusted. The two algorithms to automatically adjust t~ examined in this study both comply with the situation encountered in the pilot plant, in which concentration measurements in only one of the two aeration tanks are available. For both algorithms, a change in t¢ is allowed only at the end of each process cycle, and the
phase lengths are adjusted in order to maintain constant relative lengths during the next cycle. This insures that the incoming wastewater is equally proportioned among the two tanks each cycle. This is essential to maintain symmetry with respect to the general concentrations in both tanks. It is for this reason that a perhaps more obvious strategy involving immediate switching of phases at the time of disappearance of NO~-N in a tank is not considered. PlO CONTROL The first algorithm is that of PID feedback control. Consider the case in which the applied cycle length tc had been too short. According to the analyses of Part I, this would result in a situation by which denitrification does not come to completion in the time available. Hence, NOx-N would still remain in a tank when aeration is to resume. The lowest NOx-N concentration achieved each cycle, henceforth to be given the acronym LNC, can be viewed as a dynamic variable whose value ideally should be zero. Accordingly, LNC can be selected as a control variable. To include the case of an excessively large to, in which NOx-N is completely consumed before aeration is due to begin again, LNC is defined as a pseudo NOx-N concentration which can take on negative values. In this case, LNC is taken as the lowest negative NO~-N concentration found by extrapolating the course of NO~-N below zero using as slope the current estimate of the denitrification rate. Figure l illustrates LNC for both cases. Defined in this manner, LNC serves as control signal, and the purpose of the control strategy is to drive LNC to zero. A conventional discrete PID algorithm in velocity form (Smith, 1972) can be expressed in terms of feedback errors e as follows: [ T~ Au(k) = Kc Le(k) - e(k - l) + ~ (e(k) + e(k - l)) q
+ ~ - ( e ( k ) - 2e(k - l ) + e(k - 2 ) ) | (l) 1, J with proportional gain Kc , sampling interval TS, reset time q , derivative time "co and Au is the change to be
~
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,
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~. L~c~+~) "
\ \
I
"~ Lt~c~)
tc(k+l)
:~
Fig. 1. The approximate course of NOx-N in a tank over two cycles illustrating the process variable LNC for the case that the time allocated to denitrification is too long (first cycle, LNC(k) is negative) and too short (second cycle, LNC(k) is positive).
Control strategy for activated sludge---II
c.~0o ~
N,4-N /~Ox-N "~", / • ,/ ~ / \ , ~ ' ~ / "'t"..... tclk-1 )
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537
of the lowest NOx-N concentration of the next cycle based on nitrification of only that ammonia which enters the tank during the next cycle and denitrification of the resultant nitrate (cf. dashed curve in Fig. 2). The value of ~Li(k + 1) is obtained using a simple model by which the decrease in NOx-N is described using an average volumetric denitrification rate rd: C.1" L' (k + 1) = ( ~ (k + 1) - r d(k + l)t d (k + 1)
Fig. 2. The approximate course oQf NH4-N and NOx-N in a tank illustrating the MBPC algorithm. The algorithm is described assuming that information up to to(k) is available.
= ~'~(k + 1) - rd(k + l)[tc(k + 1) -- tn(k + 1)] r-
made in the control variable u. In this application, T s represents the cycle length which changes due to the control action. For the sake of simplicity, T~ is given a representative constant value, and the change in the
= Ca~ (k) - rd(k + l)Lt¢(k C~(k)-ll r~(k + l)J
ra(k + 1) = C ~ ( k ) + CU(k) r, tr + 1)
cycleCurrentwithCyclethelengthfollowingiS calculatedformula:at the end of each Ate(k) = / ~ " {LNC(k) - LNC(k - 1)
- rd(k -- 1)tc(k + 1).
+ KI" [LNC(k) + LNC(k - 1)] + KD[LNC(k)- 2 L N C ( k - 1) + LNC(k - 2)]},
(2)
where Ate is the change to be made to the cycle length of the current cycle and the arguments k, k - l , k - 2 denote the value pertaining to the current, the previous and the second previous cycle, respectively. Kc is the proportional gain, and the remaining controller parameters r~, ZD have been combined with the sample time T~ to form the two parameters K~ and Ko. The only information required for calculating the change in cycle length using the PID algorithm is a value for LNC in the measured tank for the current and previous cycles. This in turn requires periodic measurements of NOx-N concentration in one (or both) tank(s). It should be noted that both the PID as well as the MBPC controls are implemented in parallel to the operational strategy where aeration to a tank is terminated at the completion of nitrification. Hence some means to determine when NH4-N disappears in at least one tank is also required,
MODELBASEDPRED|CTIVECONTROL The second algorithm is a model predictive technique, and is described below with the help of the sketch appearing as Fig. 2. To be consistent with the phase scheduling diagram of Fig. 2 in Part I, the algorithm is presented for the case where tank T1 is the tank in which concentrations are measured. A prediction for the lowest NO~-N concentration in the measured tank at the end of the next cycle is given by c'L](k + 1) = C'LI(k + 1) + cL~(k). (3) C~.(k, is the lowest, or residual, NOx-N concentration of the current cycle, t~L'~(k + 1) is a prediction
+ 1)
(4)
C~;(k + 1) is a prediction oftbe highest or maximum NOx-N concentration which would occur in the next cycle if the residual NO~-N of the current cycle, C~l(k), had been zero. This is approximated as being equal to the maximum ammonia concentration at the end of the current cycle, C~ (k). In the first equality, rd(k + 1) and td(k + 1) are the denitrification rate and the time available for denitrification of the new cycle, respectively. In the second equality, the relationship t¢ = t, + td has been employed, where tn is the time required for nitrification and td is the time available for denitrification. In the third equality, t~(k + 1) is estimated as the maximum ammonia concentration divided by the nitrification rate r~(k + 1). Letting rd(k + 1) = fd; r~(k + 1) = ~,, (5) where fd and in are the current best estimates of the denitrification and nitrification rates, and substituting equation (4) as well as the relationship t~(k + 1) = t~(k) + At~(k)
(6)
in equation (3), the following formula for (~t (k + 1) is obtained:
"L
C~,(k + l ) = C a ( k )
(
~n)
l +id
- rd t~(k) +cLI (k) -- fd Ate(k). (7) Ideally, cL~(k + 1) should be zero. That is, there should be no NO,-N remaining in the tank at the end of the denitrification phase of the next cycle. By setting the prediction cL~(k + 1)= 0 in equation (7) and rearranging, a one-step compensation formula for the change in cycle length, Ate, based on model prediction is obtained: "'~,~,=--' rc,~,~,r, ?dL \
,~, rn,,/
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H. ZHAOet al.
Due to the many model approximations involved in the above formulation, a feedback error adjustment is added to the controller based on closed-loop prediction mechanism in order to improve the robustness of the algorithm (Rouhani et al., 1982): Ate(k) = A/'¢(k) + Kc[e(k) + K~(e(k) + e(k - 1))], (9) where e(k) = LNC(k) - (~t(k);
e (k - 1) = LNC(k - 1) - 6"~ (k - 1).
(10)
The variable LNC defined earlier in conjunction with the PID controller appears in the definition of the prediction error e(k) in equation (10). This is to include the case when the actual NOx-N concentration in the current or previous cycle has reached zero before the end of the cycle.
Of course it is not feasible to produce a diagram such as Fig. 3 for each possible combination of process and reaction rate parameters. The method to determine t~ '~ employed here involves an iterative search technique using the model equations describing the NH4-N dynamics only. These equations are repeatedly solved for the "steady state" using the current process parameters until that value of tc is found. The resulting value of tc is then taken as t~=~ . By approximating the nitrification and denitrification rates with constant values, the model equations can be integrated analytically, so that t¢m~ can be found quickly with the aid of a computer. The value of t max found in this fashion is based on constant process conditions. Hence t~ a~ will tend to be overly restrictive for the dynamic case involving diurnal load changes, as effluent criteria are normally based on averages over time (e.g. daily) rather than point concentrations. PILOT PLANT DEMONSTRATION
CONTROL CONSTRAINTS As the cycle length is reduced, transitional periods between anoxic and aerobic conditions as well as that associated with changes in flow path will be a more significant part of the overall cycle time. In addition, operation and control strategies which are based on measurements taken intermittently will function poorly if the phase and cycle lengths become too short. In the pilot plant, for example, the measure-
A
demonstration
of
the
PID
and
MBPC
algorithms on the pilot plant facility is now provided. While carrying out these demonstrations, the pilot plant was operated as follows: inlet waste water and recycled sludge flow rates were both held constant at 1.5 1min-~. The nominal cycle length was 90 min. The flow and aeration scheduling described in Part I was employed, consisting of the four phases 2, 3, 5 and 6 and with an ammonia setpoint concentration of
Csp = 0 . 2 m g 1 - 1 (below which aeration during a ments of NH4-N and NOx-N concentrations in tank nitrification phase is terminated). T2 are obtained at a rate of every 3 min with a delay Both the PID and MBPC control algorithms were of from 1.5 to 3 min (Isaacs et al., 1992; Pedersen implemented using the same basic steps: et al., 1985). Consequently, practical considerations suggest a lower limit on the cycle length which can be (1) Estimation of the rates of nitrification and feasibly implemented. This minimal cycle length, t mi", denitrification. In this work, these rates were will depend on the particular characteristics of the described as constant values over the course plant and its measurement system. In this study, t rain of a cycle. The rates were taken as the slope was given the value of 60 min. An upper limit on the cycle length, t m"~, is defined here in terms of steady state behavior under constant 400 minutes in cycle process conditions. As exemplified in Fig. 6 of Part \.~ ~ ~"~effluent I, NH4-N concentration in the effluent rises steadily _ ~ \-. " ..... ~ -"...... ~ "~'~'~ moll with t~. This is due to the higher concentration levels a2o ~ -~-~ 3.0 attained in the two aeration tanks as t, is increased . . . . . . . . . . - ~ ...... ~72:~--~. ~ 2.5 The relationship between averaged effluent ammonia a4o " ~'----. i concentration, inlet ammonia concentration and \ \ ~ " ~ ' " ~ ' - ~~ 2.0 cycle time for the case of constant process conditions ~-'--~ !: ~ ~ is illustrated in Fig. 3 for the process parameters listed leO -~-~.~ : --. 1.5 in the figure caption. The figure was produced ] - ~ - ~ i " ~ .... ~ : 1.0 through repeated solution of the dynamic model a given as equations (la)-(3d) in Part I for the "steady ~ ~ 05 state" case. From the figure one can determine a ....................... ", . . value for t~ ~x corresponding to the nitrification rate 2o a0 4o so 6o re and inlet flow rate employed in producing the Inlet NH4-N, m011 diagram. For example, if an effluent NH4-N Fig. 3. The relationship between inlet NH4-N concentration concentration of 2.0 mg 1-~ or less is to be assuming constant process conditions and for an inlet maintained, t¢ can be no greater than 270 min (of. flowrate of 1.51 min-~, tank volume of 800 I. and a nitrifidashed line segments), cation rate of r, = 0.17 mg I min)- t.
Control strategy for activated sludge---II
(2)
(3)
(4)
(5)
of a linear fit of the measurement data in tank T2 as a function of time. For the PID algorithm, the rate of nitrification is required only for determination of t ~ x. Both rates are required for the MBPC algorithm, Calculation of t max based on the estimated nitrification rate and current process conditions, Calculation of Ate using either equation (2) for the PID algorithm or equation (9) for the model predictive strategy, Check to see if the new tc exceeds the limits tcrain o r t ~ ax . If so, tc is set to the exceeded limit, Implementation of the new tc for the next cycle.
In all demonstrations, t ma~ had been calculated based on a maximum "steady state" effluent NH4-N concentration of 2.0 mg 1- ~. However, this upper constraint was not attained during any of the trials. PID control The intention of the first demonstration was to examine the effect of the cycle length control under normal operation. For this purpose, the demonstration was performed over 2 adjacent days exhibiting similar diurnal increases in inlet NH4-N concentration. The inlet NH4-N concentrations for both days are shown respectively in Fig. 4(a) and (b). In Fig. 4(c) and (d) appear the NH4-N and NO~-N measurements recorded in tank T2 over the day employing a fixed cycle length and the day by which the cycle length was adjusted according to the PID algorithm, respectively. The PID controller was initialized at 7.5 h using three sequential values of LNC derived from the current and previous two operation cycles. The PID controller parameters eraployed in this implementation were Kc = 10, K~ = 0.5 and KD = 0.5. The controller was terminated at the 20th hour. A comparison of Fig. 4(c) and (d) shows that a significant reduction in the magnitude of the feedback control signal LNC (the minimum NOx-N concentration attained each cycle) occurred when using PID control. The peak total nitrogen concentration in the process effluent obtained with the PID control was about 1.2 mg I-~ less than that obtained with the fixed cycle length [Fig. 4(e)]. The PID control resulted in an increase in the NH4-N concentration peak heights in tank T2, but this had no noticeable effect on the NH4-N effluent concentration. Figure 4(f) shows the applied cycle length as a function of time for both days of this demonstration. Had the PID controller been continued beyond the 20th hour, a further reduction in the cycle length would have occurred, due to the negative values for the feedback signal LNC.
539
PID and MBPC control
In order to compare the PID and MBPC control algorithms under comparable process conditions, a similar nitrogen overload was induced in the plant over 3 separate days. This was done by adding a solution of NH4-N in tap water to the top of the anaerobic zone in a controlled fashion for a 6 h period starting at about the 450th min of each trial. The same PID controller parameters as in the first demonstration were employed here. The adjustment term in the MBPC algorithm employed the parameters K~ = I0 and KI = 1. Both the PID and MBPC algorithms were started at the 7th hour using current and previous process information for initialization. The NH4-N and NOx-N measurements of tank T2 are shown in Fig. 5(a), (h) and (c) for operation employing a fixed cycle length, the PID algorithm and the MBPC algorithm, respectively. The effect of the PID controller was similar to that seen in the first demonstration, namely a significant reduction in the variable LNC. The speed at which LNC decreases can be varied by adjusting the controller parameters. However, the usual tuning precautions to avoid excessive overshoots in the controlled parameters tc must be adhered to. In addition, the nature of the PID control is such that deviations in the value of LNC from zero first must occur before control actions are taken. Hence, the PID control serves to reduce LNC as compared to operation employing a fixed cycle length but cannot maintain LNC at zero in the face of changing process conditions. The advantage of adding a feedforward element to the control algorithm can be seen in the results obtained with the MBPC algorithm appearing in Fig. 5(c). In spite of the rather simple process description employed in the algorithm, LNC was maintained very close to zero throughout the course of the day. In Fig. 5(d) are plotted the effluent concentrations of NH4-N and NOx-N for all 3 days of the demonstration. Once again the PID control resulted in a slight decrease of NOx-N peak concentration on the order of about 1 mg l- ~ as compared to using a fixed cycle length. For both cases the NH4-N concentration remained close to zero. It is difficult to make a definitive statement regarding the effluent results pertaining to the MBPC control, as here the NH4-N measurements, although remaining below 1.5 mg 1-m, were higher than expected. It appears that an insufficient aeration time was applied to the unmeasured tank TI. This occurred due to the current pilot plant aeration control, by which aeration is applied to tank T1 for the same amount of time as that applied to tank T2 in the previous half cycle. However, as the cycle length is increased at the start of the cycle, tank TI is the first tank to experience the higher ammonia load. This factor was not accounted for in this implementation. Based on total effluent nitrogen concentration, the effluent
540
H. ZHAO et al.
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Control off 3 ~
i
'~
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~~ -
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0,. ............. r............... •. . . . . . . . . . . 6 10 14 18
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h Fig. 4. Results of the first control demonstration involving normal plant operation and by which the cycle length was fixed or adjusted using the PID algorithm. results using MBPC control are about the same as that obtained with PID control, The course of the cycle length for all 3 days of this demonstration appear in Fig. 5(e). Here it can be seen that the predictive element of the MBPC algorithm resulted in an earlier adjustment in tc than the PID algorithm. The MBPC algorithm also resulted in larger initial increases in tc, but this may be due to an
overly cautious parameters.
tuning
of the
PID
algorithm
DISCUSSION Compared to nutrient removal plants based on the recirculation principle, the alternating type process allows for a simple means to adjust the retention
Control strategy for activated sludge---II times of nitrification and denitrification, namely by adjusting the aeration and flow scheduling. The strategy proposed and demonstrated here, by which the total cycle length is adjusted while maintaining constant relative phase lengths, can be viewed as a first step towards achieving a controlled and flexible switching of phases, while simultaneously
maintaining symmetry between the two nitrification/denitrification zones. With the principle aim of the control strategy to minimize the effluent nitrogen concentration through adjustment of the cycle length, the analyses of Part I have allowed the control task to be defined in terms of maintaining a readily measurable process variable,
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H. ZHAOet al.
LNC, as close to zero as possible. From the results of the few pilot plant demonstrations provided, the following statements regarding the effect of both control algorithms on LNC can be made: (1) The PID algorithm has the advantage of minimal requirements for process information. However, the controller parameters have to be properly chosen and some retuning may be necessary as process conditions vary. Also, perfect control is not attainable, as the feedback signal LNC must first deviate from its desired value of zero before a control action is taken, (2) The MBPC algorithm requires more process information including the on-line estimation of time variable model parameters (here, the nitrification and denitrification rates), However, the algorithm is inherently adaptive, and its feedforward nature makes it feasibly possible to hold LNC close to zero. The results shown in Fig. 5(c) illustrate this attribute very clearly. It appears that a rather simple process model is sufficient for this strategy. With regards to effluent nitrogen concentration, the improvements in peak concentrations obtained with the control strategy are of the order anticipated, based on the simulations in Fig. 6 of Part 1. A 1-2 mg 1-l reduction in the effluent total nitrogen concentration is significant towards maintaining a daily average effluent criteria of, for example, 8 m g l -~. The corresponding reduction in the nitrate content of the return sludge will reduce the amount of wastewater organics which are consumed for denitrification within the anaerobic zone. This in turn will maximize the amount of organics available for uptake by the phosphate accumulating organisms within this zone, thus improving the biological phosphate abilities of the plant. The demonstrations presented in this paper all involved a temporary step up in nitrogen loading, and hence do not illustrate two aspects of the strategy: a more significant improvement is effluent quality is expected to occur when the strategy results in a reduction in cycle length due to a reduction in nitrogen load (for example due to changing weather). This is due to the strong relationship between cycle length and effluent nitrogen content when the cycle length is longer than the optimal. Secondly, the strategy provides a means to continually tune the
process to compensate for more slowly changing (i.e. seasonal) process conditions. Finally it is pointed out that the algorithms have been developed for the case where only one of the two nitrification/denitrification basins are monitored. Certainly more efficient algorithms can be developed if information from both nitrification/denitrification zones are available. CONCLUSION Based on the analysis of process dynamics undertaken in Part I, a control strategy involving automatic adjustment of cycle length has been developed and demonstrated in a pilot plant facility using two types of algorithms. The demonstration results are very promising, as they indicate that the two algorithms are effective in reducing deviations of the defined process variable LNC from its desired value of zero. In addition, the algorithms resulted in a peak effluent nitrogen concentration on the order of 1-1.5 mg 1-~ less than that of the uncontrolled case. Acknowledgements--The financial assistance from Zhejiang
University, P. R. China, through the Pao Yu-kong and Pao Zhao-long scholarships, and from the Department of ChemicalEngineering, Technical University of Denmark are gratefully acknowledged. Special thanks is given to Ms U. Eenholt and environmental technician Ms A. Nielsen for their help in the experiments. REFERENCES Arvin E. (1985) Biological removal of phosphorous from wastewater.CRC crit. Rev. envir. Control 15, 25~4. Einfeldt J. (1992) The implementation of biological phosphorus and nitrogen removal with the Bio-Denipho processon a 265,000 PE treatment plant. Wat. Sci. Technol. 25, 161-168. IsaacsS. H, Zhao H., Sgeberg H. and Kfimmel M. (1992) On the monitoring and control of a biological nutrient removalprocess. In Proceedings of the 6th Forum of Applied Biotechnology, Brugge, Belgium, 24-25 September. Pedersen K. M., Kiimmel M and S~eberg H. (1990) Monitoring and control of biological removal of phosphorous and nitrogen by flow-injectionanalysers in a municipal pilot-scale waste-water treatment plant. Analyt. chim. Acta 238, 191--199. Rouhani R. and Mehra R. K. (1982) Model algorithmic control (MAC): basic theoretical properties. Automatica 18, 401-414. Smith C. L. (1972) Digital Computer Process Control. International Textbook Company, Scranton, Pa. Zhao H., Isaacs S. H., S~eberg H. and Kiimmel M. (1994) A novel control strategy for improved nitrogen removal in an alternating activated sludge process--Part I. Process analysis.Wat. Res. 28, 521-534.
Pergamon
A NOVEL CONTROL STRATEGY FOR IMPROVED NITROGEN REMOVAL IN AN ALTERNATING ACTIVATED SLUDGE PROCESS---PART II. CONTROL DEVELOPMENT H. ZHAO, S, H. ISAACS*, H. SOEBERa and M. KOMMEL~ Department of Chemical Engineering, Technical University of Denmark, Building 229, DK-2800 Lyngby, Denmark (First received January 1993; accepted in revised form June 1993) Abstract--The first part of this two part contribution dealt with an analysis of nitrification and denitrification in an alternating activated sludge nutrient removal process for the case of constant process conditions. The existence of an optimal cycle length, which is a function of the process conditions, was demonstrated and discussed. Based on these findings, this paper examines a control strategy by which the cycle length of an alternating activated sludge nutrient removal process is automatically adjusted in order to compensate for changing process conditions. Two control algorithms are proposed. One involves proportional-integral-derivative (PID) control which requires minimal process information and computational effort. The second is a model based predictive control (MBPC) technique, which introduces a feedforward element into the control strategy. The MBPC technique is examined using a relatively simple process model. The results of experimental demonstrations of these two algorithms in a pilot plant facility indicate their potential towards improving nitrogen removal in the alternating process. Key words--activated sludge process, biological nitrogen removal, nitrification, denitrification, modeling, process control, waste water treatment
NOMENCLATURE Throughout the paper, NH4-N and NOx-N denote the concentration of nitrogen as ammonia and as nitrate plus nitrite, respectively, Cat = NH4-N in tank T1 (mgl -t) Cnt = NOx-N in tank TI (mg 1-~) e = error employed in MBPC control (rag l -t) k = index (--) Kc = controller gain (minlmg -~) KD=controller parameter (--) Kt = controller parameter (--) LNC = lowest NO~-N concentration achieved in each cycle (used as feedback signal) (mgl -t) r d = denitrification rate [rag (1 min)- t] r n = nitrification rate [mg (1 min)- ~] to=cycle length (rain) t~ n =minimum allowable cycle length (min) t ~ x = maximum allowable cycle length (min) td = denitrification time per tank (min) t~ = nitrification time per tank (rain)
nitrogen and phosphorus from wastewaters in which nitrification and denitrification are performed in a cyclic semi-batch fashion in each of two aeration basins (Arvin, 1985; Einfeldt, 1992). Part I demonstrated the existence of an optimal operation cycle length for maximum nitrogen removal. It was shown that, for a given set of constant process conditions, a cycle length can be found where denitrification just comes to completion (i.e. nitrate is completely consumed) at the time when the next aerated nitrification phase is scheduled to begin. This corresponds to the cycle length which minimizes the concentration of nitrogen in the process effluent stream. Part I dealt with the case in which process conditions are constant over a time period long enough to allow a dynamic "steady state" to occur. In this context, "steady state" refers to the situation in which no further changes in the dynamics occur from cycle to cycle. In this second part the focus is on the dynamic case with the goal of improving process performance in the face of daily, weekly and seasonal variations in process conditions. These variations include diurnal and weekly fluctuations in nitrogen loading as well as variations in the nitrification and denitrification rates. The latter can be attributed to changes in, for example, seasonal temperatures, sludge activity and composition, and
INTRODUCTION The first part of this two part communication focused on an analysis o f the nitrogen dynamics in an alternating type activated sludge process (Zhao et al., 1994). This is the B I O - D E N I P H O f process for the biologically mediated removal of organic matter, *Author to whom all correspondence should be addressed, fThe BIO-DENPHO process is a patented process developed by I. Kruger Systems in cooperation with the department of Environmental Engineering at the Technical University of Denmark.
organic content of the incoming wastewater. As a consequence of these variations, the cycle length which maximizes nitrogen removal will change 535
536
H. ZHAOet al.
with time. Hence, a control strategy is developed in which the cycle length is automatically adjusted as process conditions vary in an attempt to maintain optimal conditions, In this paper, two control algorithms are presented together with demonstrations of their performance on a pilot plant scale. The first is a proportionalintegral-derivative (PID) feedback control algorithm (Smith, 1972) which requires minimal process information and is easily implemented. The PID algorithm has certain disadvantages, however, which include a need to choose suitable controller parameters, its non-adaptive nature, and the response delay associated with a purely feedback strategy. The second algorithm involves model based predictive control (MBPC) and is developed using a relatively simple process model. Implementation of the MBPC algorithm requires more process information and a greater degree of computational effort. However, due to its feedforward and adaptive nature, the MBPC algorithm provides for a more efficient control, THE CONTROL PROBLEM In conjunction with a description of the pilot plant facility, Part I illustrated the particular process control problem addressed in this investigation. It was shown that, as a result of diurnal increases in the wastewater's nitrogen content, the denitrification could not always come to completion when employing an operational strategy with a fixed cycle length, This resulted in an accumulation of NO~-N in the pilot plant's two tanks Tl and T2 with a subsequent increase in the process effluent NOx-N concentration, The observance of this behavior resulted in the more detailed analysis of nitrogen dynamics undertaken in Part I and the conclusion that this accumulation could be reduced or avoided through suitable adjustment of the cycle length. Summarizing the findings of Part I, the cycle length t¢ should be increased if insufficient time is available for dentrification. Although a larger t¢ will result in a higher nitrogen loading per tank per cycle, the dynamics are such that dentrification will come closer to completion (or will be completed) in the allotted time. If, on the other hand, NO~-N is totally consumed before the next nitrification phase is scheduled to begin again, tc should be decreased, as this will lead to a lesser concentration of total nitrogen in the process effluent. These findings are based on the operational strategy by which the aeration of a nitrifying tank is discontinued when ammonia is consumed. Hence, the aeration time per tank per cycle, t., also changes as t~ is adjusted. The two algorithms to automatically adjust t~ examined in this study both comply with the situation encountered in the pilot plant, in which concentration measurements in only one of the two aeration tanks are available. For both algorithms, a change in t¢ is allowed only at the end of each process cycle, and the
phase lengths are adjusted in order to maintain constant relative lengths during the next cycle. This insures that the incoming wastewater is equally proportioned among the two tanks each cycle. This is essential to maintain symmetry with respect to the general concentrations in both tanks. It is for this reason that a perhaps more obvious strategy involving immediate switching of phases at the time of disappearance of NO~-N in a tank is not considered. PlO CONTROL The first algorithm is that of PID feedback control. Consider the case in which the applied cycle length tc had been too short. According to the analyses of Part I, this would result in a situation by which denitrification does not come to completion in the time available. Hence, NOx-N would still remain in a tank when aeration is to resume. The lowest NOx-N concentration achieved each cycle, henceforth to be given the acronym LNC, can be viewed as a dynamic variable whose value ideally should be zero. Accordingly, LNC can be selected as a control variable. To include the case of an excessively large to, in which NOx-N is completely consumed before aeration is due to begin again, LNC is defined as a pseudo NOx-N concentration which can take on negative values. In this case, LNC is taken as the lowest negative NO~-N concentration found by extrapolating the course of NO~-N below zero using as slope the current estimate of the denitrification rate. Figure l illustrates LNC for both cases. Defined in this manner, LNC serves as control signal, and the purpose of the control strategy is to drive LNC to zero. A conventional discrete PID algorithm in velocity form (Smith, 1972) can be expressed in terms of feedback errors e as follows: [ T~ Au(k) = Kc Le(k) - e(k - l) + ~ (e(k) + e(k - l)) q
+ ~ - ( e ( k ) - 2e(k - l ) + e(k - 2 ) ) | (l) 1, J with proportional gain Kc , sampling interval TS, reset time q , derivative time "co and Au is the change to be
~
T
,
_
_
~. L~c~+~) "
\ \
I
"~ Lt~c~)
tc(k+l)
:~
Fig. 1. The approximate course of NOx-N in a tank over two cycles illustrating the process variable LNC for the case that the time allocated to denitrification is too long (first cycle, LNC(k) is negative) and too short (second cycle, LNC(k) is positive).
Control strategy for activated sludge---II
c.~0o ~
N,4-N /~Ox-N "~", / • ,/ ~ / \ , ~ ' ~ / "'t"..... tclk-1 )
/ " ~ . / ~:.~o~+t) '~ . / L' "/ / y , / e ~ + l ~ " . : A ' ? e;~+l, / \.\ ../ "" r 'I.'" I ~ t¢lk) tclk+ 1)
,"'~, / /i"-,
537
of the lowest NOx-N concentration of the next cycle based on nitrification of only that ammonia which enters the tank during the next cycle and denitrification of the resultant nitrate (cf. dashed curve in Fig. 2). The value of ~Li(k + 1) is obtained using a simple model by which the decrease in NOx-N is described using an average volumetric denitrification rate rd: C.1" L' (k + 1) = ( ~ (k + 1) - r d(k + l)t d (k + 1)
Fig. 2. The approximate course oQf NH4-N and NOx-N in a tank illustrating the MBPC algorithm. The algorithm is described assuming that information up to to(k) is available.
= ~'~(k + 1) - rd(k + l)[tc(k + 1) -- tn(k + 1)] r-
made in the control variable u. In this application, T s represents the cycle length which changes due to the control action. For the sake of simplicity, T~ is given a representative constant value, and the change in the
= Ca~ (k) - rd(k + l)Lt¢(k C~(k)-ll r~(k + l)J
ra(k + 1) = C ~ ( k ) + CU(k) r, tr + 1)
cycleCurrentwithCyclethelengthfollowingiS calculatedformula:at the end of each Ate(k) = / ~ " {LNC(k) - LNC(k - 1)
- rd(k -- 1)tc(k + 1).
+ KI" [LNC(k) + LNC(k - 1)] + KD[LNC(k)- 2 L N C ( k - 1) + LNC(k - 2)]},
(2)
where Ate is the change to be made to the cycle length of the current cycle and the arguments k, k - l , k - 2 denote the value pertaining to the current, the previous and the second previous cycle, respectively. Kc is the proportional gain, and the remaining controller parameters r~, ZD have been combined with the sample time T~ to form the two parameters K~ and Ko. The only information required for calculating the change in cycle length using the PID algorithm is a value for LNC in the measured tank for the current and previous cycles. This in turn requires periodic measurements of NOx-N concentration in one (or both) tank(s). It should be noted that both the PID as well as the MBPC controls are implemented in parallel to the operational strategy where aeration to a tank is terminated at the completion of nitrification. Hence some means to determine when NH4-N disappears in at least one tank is also required,
MODELBASEDPRED|CTIVECONTROL The second algorithm is a model predictive technique, and is described below with the help of the sketch appearing as Fig. 2. To be consistent with the phase scheduling diagram of Fig. 2 in Part I, the algorithm is presented for the case where tank T1 is the tank in which concentrations are measured. A prediction for the lowest NO~-N concentration in the measured tank at the end of the next cycle is given by c'L](k + 1) = C'LI(k + 1) + cL~(k). (3) C~.(k, is the lowest, or residual, NOx-N concentration of the current cycle, t~L'~(k + 1) is a prediction
+ 1)
(4)
C~;(k + 1) is a prediction oftbe highest or maximum NOx-N concentration which would occur in the next cycle if the residual NO~-N of the current cycle, C~l(k), had been zero. This is approximated as being equal to the maximum ammonia concentration at the end of the current cycle, C~ (k). In the first equality, rd(k + 1) and td(k + 1) are the denitrification rate and the time available for denitrification of the new cycle, respectively. In the second equality, the relationship t¢ = t, + td has been employed, where tn is the time required for nitrification and td is the time available for denitrification. In the third equality, t~(k + 1) is estimated as the maximum ammonia concentration divided by the nitrification rate r~(k + 1). Letting rd(k + 1) = fd; r~(k + 1) = ~,, (5) where fd and in are the current best estimates of the denitrification and nitrification rates, and substituting equation (4) as well as the relationship t~(k + 1) = t~(k) + At~(k)
(6)
in equation (3), the following formula for (~t (k + 1) is obtained:
"L
C~,(k + l ) = C a ( k )
(
~n)
l +id
- rd t~(k) +cLI (k) -- fd Ate(k). (7) Ideally, cL~(k + 1) should be zero. That is, there should be no NO,-N remaining in the tank at the end of the denitrification phase of the next cycle. By setting the prediction cL~(k + 1)= 0 in equation (7) and rearranging, a one-step compensation formula for the change in cycle length, Ate, based on model prediction is obtained: "'~,~,=--' rc,~,~,r, ?dL \
,~, rn,,/
538
H. ZHAOet al.
Due to the many model approximations involved in the above formulation, a feedback error adjustment is added to the controller based on closed-loop prediction mechanism in order to improve the robustness of the algorithm (Rouhani et al., 1982): Ate(k) = A/'¢(k) + Kc[e(k) + K~(e(k) + e(k - 1))], (9) where e(k) = LNC(k) - (~t(k);
e (k - 1) = LNC(k - 1) - 6"~ (k - 1).
(10)
The variable LNC defined earlier in conjunction with the PID controller appears in the definition of the prediction error e(k) in equation (10). This is to include the case when the actual NOx-N concentration in the current or previous cycle has reached zero before the end of the cycle.
Of course it is not feasible to produce a diagram such as Fig. 3 for each possible combination of process and reaction rate parameters. The method to determine t~ '~ employed here involves an iterative search technique using the model equations describing the NH4-N dynamics only. These equations are repeatedly solved for the "steady state" using the current process parameters until that value of tc is found. The resulting value of tc is then taken as t~=~ . By approximating the nitrification and denitrification rates with constant values, the model equations can be integrated analytically, so that t¢m~ can be found quickly with the aid of a computer. The value of t max found in this fashion is based on constant process conditions. Hence t~ a~ will tend to be overly restrictive for the dynamic case involving diurnal load changes, as effluent criteria are normally based on averages over time (e.g. daily) rather than point concentrations. PILOT PLANT DEMONSTRATION
CONTROL CONSTRAINTS As the cycle length is reduced, transitional periods between anoxic and aerobic conditions as well as that associated with changes in flow path will be a more significant part of the overall cycle time. In addition, operation and control strategies which are based on measurements taken intermittently will function poorly if the phase and cycle lengths become too short. In the pilot plant, for example, the measure-
A
demonstration
of
the
PID
and
MBPC
algorithms on the pilot plant facility is now provided. While carrying out these demonstrations, the pilot plant was operated as follows: inlet waste water and recycled sludge flow rates were both held constant at 1.5 1min-~. The nominal cycle length was 90 min. The flow and aeration scheduling described in Part I was employed, consisting of the four phases 2, 3, 5 and 6 and with an ammonia setpoint concentration of
Csp = 0 . 2 m g 1 - 1 (below which aeration during a ments of NH4-N and NOx-N concentrations in tank nitrification phase is terminated). T2 are obtained at a rate of every 3 min with a delay Both the PID and MBPC control algorithms were of from 1.5 to 3 min (Isaacs et al., 1992; Pedersen implemented using the same basic steps: et al., 1985). Consequently, practical considerations suggest a lower limit on the cycle length which can be (1) Estimation of the rates of nitrification and feasibly implemented. This minimal cycle length, t mi", denitrification. In this work, these rates were will depend on the particular characteristics of the described as constant values over the course plant and its measurement system. In this study, t rain of a cycle. The rates were taken as the slope was given the value of 60 min. An upper limit on the cycle length, t m"~, is defined here in terms of steady state behavior under constant 400 minutes in cycle process conditions. As exemplified in Fig. 6 of Part \.~ ~ ~"~effluent I, NH4-N concentration in the effluent rises steadily _ ~ \-. " ..... ~ -"...... ~ "~'~'~ moll with t~. This is due to the higher concentration levels a2o ~ -~-~ 3.0 attained in the two aeration tanks as t, is increased . . . . . . . . . . - ~ ...... ~72:~--~. ~ 2.5 The relationship between averaged effluent ammonia a4o " ~'----. i concentration, inlet ammonia concentration and \ \ ~ " ~ ' " ~ ' - ~~ 2.0 cycle time for the case of constant process conditions ~-'--~ !: ~ ~ is illustrated in Fig. 3 for the process parameters listed leO -~-~.~ : --. 1.5 in the figure caption. The figure was produced ] - ~ - ~ i " ~ .... ~ : 1.0 through repeated solution of the dynamic model a given as equations (la)-(3d) in Part I for the "steady ~ ~ 05 state" case. From the figure one can determine a ....................... ", . . value for t~ ~x corresponding to the nitrification rate 2o a0 4o so 6o re and inlet flow rate employed in producing the Inlet NH4-N, m011 diagram. For example, if an effluent NH4-N Fig. 3. The relationship between inlet NH4-N concentration concentration of 2.0 mg 1-~ or less is to be assuming constant process conditions and for an inlet maintained, t¢ can be no greater than 270 min (of. flowrate of 1.51 min-~, tank volume of 800 I. and a nitrifidashed line segments), cation rate of r, = 0.17 mg I min)- t.
Control strategy for activated sludge---II
(2)
(3)
(4)
(5)
of a linear fit of the measurement data in tank T2 as a function of time. For the PID algorithm, the rate of nitrification is required only for determination of t ~ x. Both rates are required for the MBPC algorithm, Calculation of t max based on the estimated nitrification rate and current process conditions, Calculation of Ate using either equation (2) for the PID algorithm or equation (9) for the model predictive strategy, Check to see if the new tc exceeds the limits tcrain o r t ~ ax . If so, tc is set to the exceeded limit, Implementation of the new tc for the next cycle.
In all demonstrations, t ma~ had been calculated based on a maximum "steady state" effluent NH4-N concentration of 2.0 mg 1- ~. However, this upper constraint was not attained during any of the trials. PID control The intention of the first demonstration was to examine the effect of the cycle length control under normal operation. For this purpose, the demonstration was performed over 2 adjacent days exhibiting similar diurnal increases in inlet NH4-N concentration. The inlet NH4-N concentrations for both days are shown respectively in Fig. 4(a) and (b). In Fig. 4(c) and (d) appear the NH4-N and NO~-N measurements recorded in tank T2 over the day employing a fixed cycle length and the day by which the cycle length was adjusted according to the PID algorithm, respectively. The PID controller was initialized at 7.5 h using three sequential values of LNC derived from the current and previous two operation cycles. The PID controller parameters eraployed in this implementation were Kc = 10, K~ = 0.5 and KD = 0.5. The controller was terminated at the 20th hour. A comparison of Fig. 4(c) and (d) shows that a significant reduction in the magnitude of the feedback control signal LNC (the minimum NOx-N concentration attained each cycle) occurred when using PID control. The peak total nitrogen concentration in the process effluent obtained with the PID control was about 1.2 mg I-~ less than that obtained with the fixed cycle length [Fig. 4(e)]. The PID control resulted in an increase in the NH4-N concentration peak heights in tank T2, but this had no noticeable effect on the NH4-N effluent concentration. Figure 4(f) shows the applied cycle length as a function of time for both days of this demonstration. Had the PID controller been continued beyond the 20th hour, a further reduction in the cycle length would have occurred, due to the negative values for the feedback signal LNC.
539
PID and MBPC control
In order to compare the PID and MBPC control algorithms under comparable process conditions, a similar nitrogen overload was induced in the plant over 3 separate days. This was done by adding a solution of NH4-N in tap water to the top of the anaerobic zone in a controlled fashion for a 6 h period starting at about the 450th min of each trial. The same PID controller parameters as in the first demonstration were employed here. The adjustment term in the MBPC algorithm employed the parameters K~ = I0 and KI = 1. Both the PID and MBPC algorithms were started at the 7th hour using current and previous process information for initialization. The NH4-N and NOx-N measurements of tank T2 are shown in Fig. 5(a), (h) and (c) for operation employing a fixed cycle length, the PID algorithm and the MBPC algorithm, respectively. The effect of the PID controller was similar to that seen in the first demonstration, namely a significant reduction in the variable LNC. The speed at which LNC decreases can be varied by adjusting the controller parameters. However, the usual tuning precautions to avoid excessive overshoots in the controlled parameters tc must be adhered to. In addition, the nature of the PID control is such that deviations in the value of LNC from zero first must occur before control actions are taken. Hence, the PID control serves to reduce LNC as compared to operation employing a fixed cycle length but cannot maintain LNC at zero in the face of changing process conditions. The advantage of adding a feedforward element to the control algorithm can be seen in the results obtained with the MBPC algorithm appearing in Fig. 5(c). In spite of the rather simple process description employed in the algorithm, LNC was maintained very close to zero throughout the course of the day. In Fig. 5(d) are plotted the effluent concentrations of NH4-N and NOx-N for all 3 days of the demonstration. Once again the PID control resulted in a slight decrease of NOx-N peak concentration on the order of about 1 mg l- ~ as compared to using a fixed cycle length. For both cases the NH4-N concentration remained close to zero. It is difficult to make a definitive statement regarding the effluent results pertaining to the MBPC control, as here the NH4-N measurements, although remaining below 1.5 mg 1-m, were higher than expected. It appears that an insufficient aeration time was applied to the unmeasured tank TI. This occurred due to the current pilot plant aeration control, by which aeration is applied to tank T1 for the same amount of time as that applied to tank T2 in the previous half cycle. However, as the cycle length is increased at the start of the cycle, tank TI is the first tank to experience the higher ammonia load. This factor was not accounted for in this implementation. Based on total effluent nitrogen concentration, the effluent
540
H. ZHAO et al.
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h Fig. 4. Results of the first control demonstration involving normal plant operation and by which the cycle length was fixed or adjusted using the PID algorithm. results using MBPC control are about the same as that obtained with PID control, The course of the cycle length for all 3 days of this demonstration appear in Fig. 5(e). Here it can be seen that the predictive element of the MBPC algorithm resulted in an earlier adjustment in tc than the PID algorithm. The MBPC algorithm also resulted in larger initial increases in tc, but this may be due to an
overly cautious parameters.
tuning
of the
PID
algorithm
DISCUSSION Compared to nutrient removal plants based on the recirculation principle, the alternating type process allows for a simple means to adjust the retention
Control strategy for activated sludge---II times of nitrification and denitrification, namely by adjusting the aeration and flow scheduling. The strategy proposed and demonstrated here, by which the total cycle length is adjusted while maintaining constant relative phase lengths, can be viewed as a first step towards achieving a controlled and flexible switching of phases, while simultaneously
maintaining symmetry between the two nitrification/denitrification zones. With the principle aim of the control strategy to minimize the effluent nitrogen concentration through adjustment of the cycle length, the analyses of Part I have allowed the control task to be defined in terms of maintaining a readily measurable process variable,
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LNC, as close to zero as possible. From the results of the few pilot plant demonstrations provided, the following statements regarding the effect of both control algorithms on LNC can be made: (1) The PID algorithm has the advantage of minimal requirements for process information. However, the controller parameters have to be properly chosen and some retuning may be necessary as process conditions vary. Also, perfect control is not attainable, as the feedback signal LNC must first deviate from its desired value of zero before a control action is taken, (2) The MBPC algorithm requires more process information including the on-line estimation of time variable model parameters (here, the nitrification and denitrification rates), However, the algorithm is inherently adaptive, and its feedforward nature makes it feasibly possible to hold LNC close to zero. The results shown in Fig. 5(c) illustrate this attribute very clearly. It appears that a rather simple process model is sufficient for this strategy. With regards to effluent nitrogen concentration, the improvements in peak concentrations obtained with the control strategy are of the order anticipated, based on the simulations in Fig. 6 of Part 1. A 1-2 mg 1-l reduction in the effluent total nitrogen concentration is significant towards maintaining a daily average effluent criteria of, for example, 8 m g l -~. The corresponding reduction in the nitrate content of the return sludge will reduce the amount of wastewater organics which are consumed for denitrification within the anaerobic zone. This in turn will maximize the amount of organics available for uptake by the phosphate accumulating organisms within this zone, thus improving the biological phosphate abilities of the plant. The demonstrations presented in this paper all involved a temporary step up in nitrogen loading, and hence do not illustrate two aspects of the strategy: a more significant improvement is effluent quality is expected to occur when the strategy results in a reduction in cycle length due to a reduction in nitrogen load (for example due to changing weather). This is due to the strong relationship between cycle length and effluent nitrogen content when the cycle length is longer than the optimal. Secondly, the strategy provides a means to continually tune the
process to compensate for more slowly changing (i.e. seasonal) process conditions. Finally it is pointed out that the algorithms have been developed for the case where only one of the two nitrification/denitrification basins are monitored. Certainly more efficient algorithms can be developed if information from both nitrification/denitrification zones are available. CONCLUSION Based on the analysis of process dynamics undertaken in Part I, a control strategy involving automatic adjustment of cycle length has been developed and demonstrated in a pilot plant facility using two types of algorithms. The demonstration results are very promising, as they indicate that the two algorithms are effective in reducing deviations of the defined process variable LNC from its desired value of zero. In addition, the algorithms resulted in a peak effluent nitrogen concentration on the order of 1-1.5 mg 1-~ less than that of the uncontrolled case. Acknowledgements--The financial assistance from Zhejiang
University, P. R. China, through the Pao Yu-kong and Pao Zhao-long scholarships, and from the Department of ChemicalEngineering, Technical University of Denmark are gratefully acknowledged. Special thanks is given to Ms U. Eenholt and environmental technician Ms A. Nielsen for their help in the experiments. REFERENCES Arvin E. (1985) Biological removal of phosphorous from wastewater.CRC crit. Rev. envir. Control 15, 25~4. Einfeldt J. (1992) The implementation of biological phosphorus and nitrogen removal with the Bio-Denipho processon a 265,000 PE treatment plant. Wat. Sci. Technol. 25, 161-168. IsaacsS. H, Zhao H., Sgeberg H. and Kfimmel M. (1992) On the monitoring and control of a biological nutrient removalprocess. In Proceedings of the 6th Forum of Applied Biotechnology, Brugge, Belgium, 24-25 September. Pedersen K. M., Kiimmel M and S~eberg H. (1990) Monitoring and control of biological removal of phosphorous and nitrogen by flow-injectionanalysers in a municipal pilot-scale waste-water treatment plant. Analyt. chim. Acta 238, 191--199. Rouhani R. and Mehra R. K. (1982) Model algorithmic control (MAC): basic theoretical properties. Automatica 18, 401-414. Smith C. L. (1972) Digital Computer Process Control. International Textbook Company, Scranton, Pa. Zhao H., Isaacs S. H., S~eberg H. and Kiimmel M. (1994) A novel control strategy for improved nitrogen removal in an alternating activated sludge process--Part I. Process analysis.Wat. Res. 28, 521-534.