Components of a model-based operation system for wastewater treatment plants

~

Pergamon

PI!: S0273-1223(99)00065-7

Wal. ScI. Ted. Vol. 39, No.4, pp. 103-111,1999. C 19991AWQ Published by Elsevier Science Ltd Pnnted in Great Britain. All rights reserved 0273-1223/99519.00 + 0.00

COMPONENTS OF A MODEL-BASED OPERATION SYSTEM FOR WASTEWATER TREATMENT PLANTS Frank Obenaus*, Karl-Heinz Rosenwinkel*, Jens Alex**, RalfTschepetzki** and Ulrich Jumar** • Institute ofSanitary Engineering and Waste Management. University ofHannover (ISAH), We/fengarten /. D-30/67 Hannover, Germany •• ifak. Institute ofAutomation and Communication. D-39/ 79 Magdeburg-Bar/eben, Germany

ABSTRACT This report presents the main components of a system for the model-based conlrol of aerobic biological wastewater treatment plants. The crucial component is a model which is linlced to the actual processes via several interfaces and which contains a unit which can immediately follow up the current process state. The simulation calculation of the model is based on data which are yielded by on-bne measuring devices. If the sensors should fail at times, there arc available a number of altemative concepts, some of which are based on the calculations of artificial neural networks or linear methods. C 1999 IA WQ Pubbshed by ElseVier Science Ltd. All nghts reserved

KEYWORDS Observer, model-based operation system; on-line measurements; correlation; alternative concepts; artificial neural networks; Least Square Method. INTRODUCTION The classic field for the use of simulation studies is supporting the planning of new plants or monitoring existing concepts, respectively. Recently, however, it has become possible to run on-line simulation models parallel to the operating wastewater treatment plant. As the plant model is available at any time, it is possible to run a detailed operation modification to a degree which cannot be achieved with conventional process control systems. In a research project funded by the Deutsche Forschungsgemeinschaft DFG (German Research Association), a virtual test system is used to gauge new ways of coupling and the demands on such a system. The measuring campaigns for the detailed examination or the influent composition were done at the wastewater treatment plant at Hildesheim, the operation technology of which will in the course of further analyses be integrated into the test system. In the folIowing, the first results ofthis project wilI be presented. DESCRIPTION OF THE SYSTEM Taking the terminology from the area of control engineering, we used the term observer. The observer consists of three main components. 103

F. OBENAUS 4!t at.

104

Influent generator: on-line measuring of some guiding parameters yield the basic data for the allocation of the influent fractionation for the topped observer model. For practical reasons, one should try to work with a small number of reliable parameters which can be ascertained on-line. The function of this component must be guaranteed by using integrated substitute value concepts in case the crucial sensors should fail during the revision period. "Observer" model: the observer model is a kinetic model of the biological processes that imitates the actual technology of the respective wastewater treatment plant. In the present case, the model plant was designed according to ASM I (Henze et al., 1987). Into this model is fed the influent with the detailed fractionation necessary for this model. Using this influent - which because of the available data and the methods used may actually be faulty - the observer model calculates the internal process ratios and the emuent data Model calibration, follow-up of state: initially. the parameters and the operation status of the observer model are not known. The comparison of the internal process ratios and the emuent parameters of the observer model to the values actually measured at the plant then yields the prerequisite data for designing a component for the immediate process state follow-up. SYNTHESIS OF THE INFLUENT Materials and Methods In order to facilitate profound prognoses at any time, the technology for on-line measuring needs to be sturdy, with low maintenance efforts, and it must work with adequate precision. Moreover, it is crucial that "dead time" between sampling and ascertaining the measuring data has to be very low in order to simulate "real time" and to react accordingly. Particularly relevant is the way the samples are processed. If the chosen measuring principle should necessitate sample processing by ultra-filtration, this would cause additional operational efforts. This report is based on measurements done in the emuent of the primary settling tank at the wastewater treatment plant at Hildesheim. Table 1 shows the devices used. None of these devices did necessitate processing of the samples. Parallel to the data from the on-line measuring devices, the composition of the discharge water was in several measuring campaigns dynamically ascertained in the laboratory by taking 2h mixed samples, in order to gain correlations to the on-line data. Table I. Measured Parameters in the emuent of the primary settling tank

On-line ParameterlUnlt Turbidity [TU] Spectral absorption coefficient SAC [11m] pH-value [-] Redox potential [mY] Inductivity [mS/cm] NH.-N [mgll] Parameters Carbon Phosphate Nitrogen

Principle according to EN 27027 UV-Absorption at a wavelength of254nm

ion-selective

Laboratory

Measuring period

0.5·300 s I - 30 Minutes on-line on-line on-line 10 minutes

Fraction CODlDb CODd'IIOlved, organic acids, easy biodegradable COD PlOto> ortho-Phosphate MI.-N, TKN

Model-based operation syslem

lOS

Analyses Qfthe measurinl: results

Because long-term experiences with the ASM No. I (Henze et al., 1987) were available, this model was the f1J'St tQ be used to the simulation calculations. Thus, it was crucial to get reliable data at least for the CODIot. and NH.-N as nitrogen parameter in the influent to the biological stage. By comparison with data gained in the laboratory, it is also possible to estimate the range of the concentration variatiQn of those parameters which arc relevant for the fractionation. The UV-sensor (for measuring SAC) used for ascertaining the CODtoL has by now been running for one and a half years without any hitches. The quality of the measuring data was checked against the results of the laboratory. These tests showed excellent congruity degrees to the COD measured in the laboratory. The test data were similarly excellent for the Nl!4-N measuring device. Because of the similar course of the SAC and turbidity in the effluent of the preliminary clarification, one can expect a distinct connection of the turbidity with the CODIot.. This can be proved by Figure 3. At least for the wastewater treatment plant at Hildesheim, it is thus possible with a decent degree of exactness to calculate the homogenised COD via the parameter turbidity. Moreover, this proves that in the daily concentration variation course the relation between CODn and COD in particle form is relatively constant. For the running of simulation calculation, this means that the fractionation of the CODIot. can be done via fixed factors at least between the amounts of dissolved COD and COD in particle fonn without this causing major inaccuracies.

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Crucial for the running of simulation calculations is that the easi!y degradable COD ~atio is d.escri.bcd as accurately as possible. As this ratio consists mainly of dissolved fractIons, such as sho~-string orgamc aCids, the impact of the connection between organic acids and dissolved C~D was e.xammed. Th~ results ~f the comparison between linear (correlation coefficient - 0.73) and non-hne~ functions ~corr~latlon coeffiCient • 0.74) were almost the same. Also the correlation between the concentra~ons of ~rg.anlc. aCids ~d .COD tol . w~ examined, because only the CODIOL is available online. The results of thiS analySts In Figure 4 mdlcate, that In

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regard to future simulation calculations it is therefore also possible for the examined catchment area to describe the concentration of organic acids as a major fraction of the easily degradable amount via a constant relation to the COD to, value. Difficulties arise for measuring the amount of organic nitrogen. Via the sum parameter TKN, it is possible to estimate the fraction of organic nitrogen, which in the inlet to the activation unit is not yet hydrolysed; the correlation coefficient is R = 0.79, which is comparatively high. The parameter Nlii-N, however, is only available from on-line measurements. Correlating the sum parameter TKN from the concentration of the Parameter NH 4 -N (correlation coefficient = 0.91) seems to be the best way. The difference between measured NH 4 -N and estimated TKN represents the fraction of organic nitrogen as an input for the observer model.

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ALTERNATIVE CONCEPTS Although the examined implements showed a high degree of operation safety, there have to be alternative concepts to cover possible cases of sensor failure, in order to gain plausible simulation results at all times. Initially, one has to exploit simple connections. It is possible, for instance, to alternatively ascertain the amount of COD via checking the development of the parameter turbidity. Figure 7 gives the results, using the relevant connections ascertained for this case. If it should be necessary to ascertain the parameter P04 -P for use in plants with integrated biological phosphorus removal, alternative estimations with an adequate correlation coefficient (R = 0.69 - 0.89 depending on the catchment area, Figure 5) can be done via comparing it to the concentration of the parameter NH 4-N (results in Figure 6). From the beginning, we expected that to ascertain the parameter

ModeJ-based operation system

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via its connection to parameters which are easy to measure would be problematic, because this parameter occurs only in completely dissolved form and is thus not coupled to the solids. Furthermore, it cannot be closely aligned to the carbon parameters by examining the occurrences of load peaks. For this reason, the estimation of this parameter was done with the help of calculations run in artificial neural networks, which by supervised training (learning) are able to recognise and use non-linear connections as well. There is a wide range of different kinds of neural network designs, that is of assumed models used for system identification, system identification meaning the ascertaining of all equations necessary to describe the system. One very popular neural network is the "Multilayer Perceptron" with a hidden layer, which was also used within the frame of the examinations presented here. Figure 8 shows the good quality of the results of these calculations. The dynamics were adequately recorded. Artificial neural networks can be regarded as non-linear parameter estimation methods. The quality of the results is crucially influenced by the setting of the defaults for the network-internal initialisation weights. In the present case, this was achieved by installing a random function. One consequence of this procedure is that it is difficult to reproduce the results with an untrained neural network. Furthermore, the practical use is impaired by the relatively high efforts for the implementation and the necessary iteration calculations. For these reasons, in the context of the present examinations the Least• Square-Method was tested as substitute value concept, to have an alternative to the non-linear approach. Advantages of this method are the simple implementation and a considerable sturdiness, as this system provides the mathematically unequivocal solution to the given problem. Figure 9 and Figure 10 show that it is generally possible with this method to cover a sensor failure (in this case that of the analysers for the parameters Nl4-N and P04-P) over a period of several days (in the Figures, 200 degrees on the time scale are equivalent to 100 h, that is approx. 4 days). ~

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Figure 10. Comperison of the measured curve and the results of celculatina the concentration of PO.-P with the parameteR SAC, Turbidity and Inductivity using the Leest Square Method.

Based on the analysis and calculation results, one gains definite requirements fo~ the me~uring devices which are to be installed at the influent of the biological stage in order to get the InformatIon necessary for the requirements of dynamic simulation. As the plant models which will be presented in the f~lIowing passages were developed on the basis of the Activated Sludge Mode.1 No..1 [Henze e/ al., .1987], ~e Influe~t ge~erator was at first designed with the utilisation of this model in mInd (FIgure. 11). If the IntegratIOn of a bIological P• elimination unit is planned, one must add a measuring device to ascertam the phosphate amounts.

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REALISATION OF THE CONCEPT ON A VIRTUAL LEVEL For test purposes, we started by creating a test system (model plant) on a virtual level, using the simulation implement SIMBA 3.2\ in order to check the interfaces and the different concepts for process state follow-up (Figure 12; Table 2). The model of the real plant is fed with nitrogen, the respective ratios of which were ascertained from measuring campaigns which yielded the TKN concentrations (NH4-N measured + organic nitrogen in varying concentrations, conveyed into the plant via the nitrogen-containing COD fraction). In contrast, the observer model only gets information about COD"". and Nl4-N. The concentrations of the organic nitrogen fractions of the observer model are coupled via fixed parameters to the concentration of COD"".. Tanks with surface aeration and circulating flow have particular hydraulic properties, which are crucial for the function of this plant. For the parameter oxygen, there emerges a distinct vertical concentration gradient, which makes for considerable consequences for the design of the model (cf. Alex et al., 1999). Because of these relations, it seemed to be reasonable as a first step to do some principal examinations about the function of the observer with a more simple process (pre-denitrification). The basic results yielded from the example should then be transferred to the treatment plant at Hildesheim, with regard to the specific flowing performances. This procedure allows for the separation of the tasks relating to the principal design of the observer model and for the consideration of the specific mixing and flowing conditions of the plant at Hildesheim; the effect is that the discussion of the results can be much more lucid. Table I. Process data and parameters for the follow-up of the example system

Process Parameter

adapted Parameter

Component

MLSS [gil]

aXi

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COD. [mg/l]

aSi

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SSe [mgll]

variable factor

Nl4-N. [mg/l]

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The following figures confinn that the presented system functions satisfactorily. The average variations of the growth rate are very low (Figure 13), which allows for a decent adaptation to the varying influent concentrations of the actual process for the parameter meN (Figure 14). Because of the varying ratios of organic nitrogen in the influent ofthe imitated wastewater treatment plant, however, there occurred a substantial mistake for the nitrate concentrations, because any variation of the organic nitrogen ratio from the ratio propounded by the observer will immediately impair the entire result of the simulated nitrate concentration in the effluent because of the favourable adaptation to the nitrification (Figure 15). The MLSS-contents in the aeration tank can to an adequate degree be followed up by using the inert COD ratio within the influent (Figure 16).

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F. OBENAUS et al.

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EXTENDED VERSION OF THE VIRTUAL SYSTEM Based on the first simulation results, an extended version of the the virtual system was created. Especially the specific model structure of the aeration tanks and other technical details like the power supply of the aerators has to be implemented. The flow sheed of the whole system is presented in Figure 17. In order to be more realistic the observer model has a less complicated structure than the so cal1ed "real WWTP". The second difference to the originally implemented follow-up method is a control function for an internal mix flow between the upper and lower level of the surface aerated tank for following up the rate ofdenitrification.

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CONCLUSIONS The test results have shown that it is possible with a smal1 number of parameters to describe the influent of a biological wastewater treatment plant to a sufficiently exact degree, even in regard to dynamic simulation. It

Model-based operation system

III

also became .apparent that it was possible to gain substitute results from non-specific data in case that the installed sensors should fail; these results are yielded partly from direct linear connections. The satisfying results of the calculations with artificial neural networks have also evinced the applicability of this nonlinear estimating method for the parameter NH..-N. Compared with the high efforts for the implementation and the necessary iteration calculations by using an artificial neural network a linear approach seems to be more suitable. The calculations done in the test system "wastewater treatment plant - observer", which was initially designed as a virtual model for the tests, have shown that one can successfully adapt the system to crucial parameters of the actual plant by using the variation of but a few parameters. Further Examinations with the extended version of the virtual system will be necessary to optimise the control functions. After the examinations on a virtual level a full scale experiment is planned. REFERENCES Henze, M., Grady, C. P., Gujer, W., Marais, G. V. R. and Matsuo, T. (1987). A general model for single-sludge wastewater treatment systems. lAWPRC Scientific and Technical Report No.1 ifak (1997). SIMBA 3.2' Simulation der biologischen Abwasserreinigun8, User's Manual, Institute of Automation and Communication e. V., Magdeburg, 1997 Alex, J., Tschepelzki, R., Jumar, U., Obenaus, F. and Rosenwinkel, K.-H. (1998). Analysis and design of suitable model.lrUctures for activated sludge tanks with circulating flow. Wal. Sci. Tech., 39(4) 55·60 (this Issue).