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Supervisory Control of Integrated Wastewater Treatment Systems
ELSEVIER
Copyright © IFAC Large Scale Systems: Theory and Applications, Osaka, Japan, 2004
IFAC PUBLICATIONS www.elsevier.comllocatelifac
SUPERVISORY CONTROL OF INTEGRATED WASTEWATER TREATMENT SYSTEMS M. Grochowskj(1), M.A. Brd~'s(Z), K. DuzinkiewicZ
(1)
Department afAlltomatic Contral, Gdansk University afTeclmology, III G. Nanttowicza 11/12, 80952 Gdansk, Poland,
email:[email protected]. kdltzill([[email protected]
m School ofEngineering, Department af Electrical, Electronic alld C01l7plller
Engineering, The University of Binllingham, Binllingham B 15 2TT, UK, e1l7ail: [email protected]
Abstract: Control of integrated wastewater treatment plant-sewer systems under full range of disturbance inputs enforces employing advanced optimising control algorithms. The control activities are hierarchically structured into a multi levelmultilaver form. Model Predictive Control is chosen as a core control technology . As it is im~ssible to handle sutliciently well all operating conditions of this system, the operating states of the system are distinguished and these are : normal, disturbed and emergency. Formulation of operational states requires introducing the concepts of the core and secondary objectives that divide the system objectives into obligatory and additionally ones. DiITerent control trajectories are designed for corresponding operationai states in order to best adopt control actions to current plant conditions. In order to effectively control such complex system the Supervisory Level (SuCL) is introduced: Its main functionality is: to coordinate activities of the control structure, to asses operational states of the ~ystem, to asses selection of the control strategies, to asses imposed control setpoints realisation and risk of not fulfilling the objectives. Paper describes units adjacent to SuCL that major function is to provide the information needed by the SuCL and decision making process. Copyright © 200-l1FAC Keywords: Supervisory control, decision making, neural networks, hierarchical control optimal control, risk, waste treatment.
of three levels (Brdys, et al., 2002b; Grochowski, et al., 2004) . The upper control level - Supervisory Control Level (SuCL) performs supervisory control actions. The optirnising control actions are generated at the second - Optirnising Control Level (OCL). The Model Predictive Control (MPC) is proposed as a control technology to be applied at this level. The optirnised manipulated variable trajectories are set points for the third level where the system is actuated forcing the process to follow the prescribed trajectories. There is a multiple time scale structure in a wastewater system internal dynamics. This structure is used in order to distribute the control objectives defined over ditTerent time horizons among ditTerent control layers of the optirnising control level. Three control layers are distinguished slo",' (SCL), medium (MCL) and fast (FCL). Structuring the optirnising control level into the three control layers allows reducing complexity of the
I. INTRODUCTION Nowadays, both European and Nationwide regulations force high standards on nitrogen and phosphate contents in treated sewage, including nitrogen and phosphate removal. In order to manage fulfilling these standards, Wastewater Treatment plants (WWTPs) become more and more technologically complicated systems. One of the main problems is high variation of sewage inlluent that requires fast enough proacti"e response of a control system The variations caused by the rain, storms and snowmelt lead to decreasing the etnuent quality and increasing the operational cost, if not properly handled. In the accompanying paper (Grochowski et aI., 2oo4a) structure and methods for integrated WWfP and Sewer Network (SN) system control are proposed. The control action generating is organised within a hierarchical structure composed
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optimising control problem. The local controllers/and manipulators at Follow up Control Level force the setpoints prescribed at Optimising Level onto the plant In order to etlectively control such complex system, the described control structure has to be able to efficiently operate. This is the reason for introducing Supervisory Level. In general , the control strategy selection, soft switching between the control strategies and overriding actions at different le\'els and layers of supervised structure are its main functionalities In this paper MPC technology is extended and supplemented by concepts of operational states, prioritising the control means and objective sets separation. Apart from this the functionalities of SuCL, such as risk and performance assessment, are described. Another task of SuCL, the switching between the control strategies is presented in (Grochowski et al.. 2004b) . As it is impossible to handle sufficiently well all running conditions of integrated wastewater treatment system (IWWTS), three operating states of a process are distinguished: normal, disturbed and emergency (Brdys et al.. 2002b; Grochowski, 2003). The state assessment is based on most important for WWT plants, examining in a predictive manner the core objectives. Apart from these minimum requirements, possibility of meeting the additional objectives (secondary objectives) is also investigated. Hence, different control strategies are proposed and tested for ditlerent operating conditions (Gminski, et al. . 2004).
The solid lines m Fig. 1 symbolise information now such us : o~iectives , setpoints, constraints, measurements, risk and performance indicators etc, whiles doted lines represent information exchange during dialogue processes. Certain mam parts in this structure can be distinguished: the monitoring system, Follow up Control Level, Optimising Control Level (broken into SCL MCL and FCL). Supervisory Level. There are dedicated units in that control system that major function is to pro\'ide the information needed by the SuCL These are the Situation Assessment Unit (SAU) (that includes Risk Assessment Unit-RAU and Knowledge Discovery Unit-KDU) and Performance Assessment Unit- PAD Interaction and communication behveen the layers and units as well as performance assessment, situation assessment coordination, control strategy selection are the tasks that should be carried out bv the supervisor and its agents The SAU and PAU agents play a supporting role in this structure The SAU carries out routine activities but it also needs to be prepared to get involved in a dialogue with the SuCL and to quickly answer questions stated by the SuCL. Based on an assessment of an operational situation of rwWTS system the SuCL aIJocates suitable control strategies to control layers by employing the mechanisms to be still designed. Regardless on a quality of the SuCL mechanisms employed to make the final selection of a control strategy to be applied the strategy is selected based on a prediction of the system operational performance. However, due to uncertainty the predicted performance is not the same as the performance that will be achieved in the system. The achieved performance is on line monitored and assessed by PAU. The MPC mechanisms at the control layers check the achieved performance indirectly by comparing the model responses with the WWTP - SN states at discrete time instants ending the MPC's time steps. PAU generates more accurate information about achieved performance and based on this information SuCL may halt applying current control strategies during these time steps
2. FUNDAMENTALS OF THE CONTROL STRUCTURE The proposed structure is a multilevel and multilayer. There are two ways of its decompositions (or one can say synthesis). Namely the control system is broken down into so called, Levels and Layers. The Levels emerge as a result of a functional decomposition of the control system, The Layers are produced by employing time decomposition based on ditlerent time scales in dynamics of the controlled processes. The resulting information structure is illustrated in Fig. I.
2.1 Control means
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Practice at the WWT plants has shown that certain control handles or ways of operating the plants are less favourable than others. Hence, there is a need to distinguish behveen the available ways of controlling the plant It can be done by selecting different levels of usage of control nfeans or by selecting different control method. A prioritisation of the control means into the technologically preferred and technological not preferred is introduced in order to capture an operational reality of a wide class of wastewater systems. The technologically not preferred control means are the control means that are utilized more intensively then the operator (specialist, WWTP designers etc) would like. Their wishes can then be quantified by specifying certain desired operating ranges . The tec/mologically preferred control memlS operate inside these desired ranges . Note, that the above has nothing to do with technical possibilities
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Fig. I. Hierarchical intelligent control structure.
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of i.e. actuators. It is because whether or not a control handle or a way of operating system is technologically preferred entirely depends on a degree of utilization of the control handle or the way of operating a system and the technical lirnitaho~s are assumed to be respected. The excessive usage of chemicals for phosphorus removal, wastewater bypassing route or keeping almost fully filled equalization tanks can serve as the exa~ples of technologically not preferred control means. These control means can ditler at ditlerent WWTP. It is because they depend on existing technological constraints, operators technological experience and attitude regarding the ways of treating process at different WWTP. The technologically preferred control means always have the highest priority and it is tried to manage the process by entirely using these control means. Apart from the technologically 110t preferred control means usage, certain ways of controlling the plant are not preferred although their use is justified under certain operational conditions. Running the plant, for a while, with small overtlowing when rain suddenly occurs can serve the example of such case. It can be justified if ensures benefits over longer time horizon or if it prevents from sludge washout. However, permanently operating with even small level of overtlow is definitely not welcome. In practise, it leads to keeping the plant within preferable operating region in a space of control inputs and plant states. However, sometimes operators are forced to run the plant outside this region and run the plant in not preferred operating region but still remaining inside the legal one. Summarising, prioritising the control means gives the operator a room for running the plant in a number of ways. The preferred and not preferred operating regions are illustrated in Fig. 2. Regarding the control means the denotations "max" in Fig. 2 means that this is the level of maximum exploitation of control means (read: maximum attainable).
legislators the sewage concentration discharge requirements and the technological constraints. The other issue is concerned with the ohjectives that are desired by the plant operators. Not all of them can be completely achieved under all possible disturbances regarding weather conditions, hence hydraulic and pollutant loads, at reasonable operating cost and capital cost of building and maintaining the plant Hence, there is a need for adopting the objectives to current situation that is described by the current state of the plant and prediction of disturbances over suitable horizon. To ensure the fulfilment of the obligatory ones. the objectives are split into the core objectives and secondary objectives The core objectives are concerned with the obligatory demands and they represent minimum requirements to be met regarding the etlluent discharge and technological limitations. Fulfilling of the core objectives means that WWT plant works properly but with minimum set of requirements. As the WWTP capabilities are different then the core objectives are plant dependent. Listing them should be jointly done by technological and scientitic experts by trading between process safety and financial bene tits. An example of such core list is presented in Tab. I. The core list consists of the main control objectives, only . In practice more of them could be considered but it is, of course, plant depended. Apart from the core objectives, there are also other objectives that operators would be glad to meet, if possible, knowing that the core objectives have been already fulfilled. They can consist of more ambitious then legal eft1uent constraints or i.e. running the process without chemical dosing. It leads to modification of the core list into more demanding one. It might consist in strengthening or/and extending the core objectives. The resulting objectives are called secondary objective As distinct from the core objective list, there could be a lot of the secondary objectives lists and they are depended on the plant intluent conditions, its range of changing, accessibility of control means, structure of the plant and, tinally on operating stutT experience and their ingenuity.
u,(t)
Table I Example of Core objectives list Con objedives to be fulfilled 1. Keep effluent TN {total mtrogm):S:1 0 mgldm' 2. Keep ellIumt 1P (total phosphorus):S:1 mgidm' l . Keep effluent COD Q5 mgO,/dm' .a Keep eftluent BOO, :S:15 mgO,/dm' S. Keep ellIuent TSS (total suspmded solids) ~5 mg/dm' 6. Keep maxunum hourly inflow Q,.,a:S: I 000 [m' tb I 7. Keep ma'
Fig. 2. Possible operational regIOns of WWTP operating. Operating regions constitute a base for defming the operational states for the system assessment purposes.
The idea of secondary objective lists comes from the reason of giving the plant operators a chance to improve the plant operation under many different conditions and comes across permanent forcing the requirements by the legislators and costs saving, of course.
2.2 Core and secondary objectives
There are two issues when considering the objectives to be met by the WWT plants. One is the imposed by
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2.3 Operational states
that provides the best support for these activities. The supervisory level (SuCL) needs suitable information regarding operational situation in the system and mechanisms to transfer this information into commands and/or advises to be sent to other control levelsllayers over long time horizon . Thanks to skilful ~sage of appropriate units (agents) and data . SuCL possesses all information needed to estimate current state of the system Information trom all control structure units is available for SuCL at every time step with a time resolution that is adequate to the unit operating time scale. Thanks to that, SuCL has global knowledge about current activity of entire system and is able to coordinate operations of the ~ther levels and layers and suitably respond to unwelcome or "unse~n" at some layers' events ,,,ithin the plant. Short duration e.g., one or two hours, but very heavy rain can serve as an example of such sit~tion . As a time step at Slow Control Layer is greater than the rain duration period, the layer is not able to see these kinds of events. It is a task of a Medium Control Layer to respond to these events . The MCL is capable of handling this kind of disturbance, but consequences of the rain may considerably exceed a prediction horizon of this layer. Hence, the SuCL needs to act jointly with the MCL and SCL in order to properly handle this situation. This situation serves an example of a short duration disturbance event with long-term consequences. For each of the operational states there are appropriate control strategies (Grninski et al., 2004; Grochowski, 2003) that improve selecting the best at the moment control actions regarding actual and predicted state of the plant. Selecting proper control strategy and scheduling the switching between them is the main activity at the SuCL. The switching process should be carried out very carefully because of constraints on state variables changiu'g rate and uncertainty existence. Ways of smooth changing the current control strategy, the soft switching, in case of MPC control technology are described in the accompanying paper (Grochowski et al., 2004). There are units designed in order to help SuCL in the decision making process. These are : Situation Assessment Unit (SAU) and Performance Assessment Unit (PAU).
As it is impossible to handle suniciently well all nlIlling conditions of WWTSN system, in order to best fit the control actions into the actual and predicted conditions, different operational states of a process are distinguished. The main thing is to ensure that all of the obligatory objectives will be fultilled . In order to do that, the control trajectories entirely based on core control objecti"es are computed, by MPC over the prediction honzon. There is a need to account for an uncertainty when planmng (choosing) the control strategy . A described in (Brdys et al., 2002a; Rutkowski, 2004; Rutkowski, et aI., 2004) set membership uncertainty modelling allows for the robust output predictions (SMAC D7, 2002 ; Duzinkiewicz et aI., 2003). Knowing the current process state and predicting the uncertain outputs the possibility of fulfilling all the core control objectives over a considered time horizon is robustly assessed. The result of this assessment implies the operational state of the system (Brdys et aI., 2002b; Grochowski, 2003). Paper considers three operational states: nonnai, disturbed and emergency. If there is a guarantee of achieving all the core control goals by running the plant inside preferred operating region, then the process is said to be in a nonl/al operating state. If there is no possibility of achieving all core control objectives over considered time horizon even if the plant operates in not preferred regiol/, then the process is said to be in an emergency operational state . Finally, if there is no guarantee of achieving core control objectives over considered time horizon without entering the not preferred operating region, then the process is said to be in a disturbed operational state. There are two possible actions when being in the distllrbed operating state : to continue with the current control strategy or to increase the levels of utilizing the particular control means beyond the desired one such as PIX or overflow below the threshold level in order to regain a guarantee of meeting the core control objectives. The operational states are assessed on a base of examining the possibility of fulfilling the objectives from the core control list (Tab. I). It is done using the Robust Model Predictive Control algorithm (RMPC). The RMPC was successfully applied to drinking water distribution systems (Brdys et al. . 2001a, b; Duzinkiewicz et al.. 2003). Violation of eft1uent constraints from the core objectives list can be expressed by risk level of not meeting the objectives (Brdvs et al., 2002a; Grochowski, 2003). Such risk can be very useful factor helping SuCL in selecting the control strategy to apply at current operational state.
3.1 Situation assessment IInil. SAU is an intermediate unit between SuCL and OCL, which translat~ SuCL requests into ' language ' that is understood by optirniser, what means: objective functions, constraints, control means, etc and returns to SuCL operational cost, controlled and output variables trajectories and associated risk As it is shown in Fig. 1, SAU consists of subunits: RAU (Risk Assessment Unit), KDU (Knowledge Discovery Unit). A kno,,,n level of risk of not fulftlling' the prescribed core control objectives (computed by RAU) is a base for the situation assessment. The risk level is an important, if not decisive, factor taken into account by SuCL when setting a control strategy and choosing an operational state for the plant to be eventually transferred into.
3. SUPERVISORY CONTROL LEVEL A supervisory control level is located at the top of the control system hierarchy. The supervisory activities are distinguished trom other control and monitoring activities as a centralised control level
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The situation assessment gives an answer in which operating state a process is at present and to which operational states it could be transferred in the coming future if the current control strategy is maintained_ RAU along with SAU, on a command from SuCL, activates an optimismg control layer to generate control actions for investigated control objecti\-es expressed by MPC performance indices and constraint functions and by using im'estigated control means_ Produced by the optimiser control actions are assessed by the RAU and they are transferred to SAU with the information about level of risk of not fulfilling the control objectives over a time horizon. In a qualitative form the task, which is allocated to SAU by SuCL, can be: "I am considering this controi strategy 10 be used by AICL over coming horizon. Please, find Ol/t what is the associated risk. If the risk level is greater than 0.3 do not bother. OthentJise, let me Imow. ., In a case of obtaining requested control with the risk not exceeding 0_3 the positive information is transferred to the Supervisor where decision about applying or not the screened by SuCL, control strategy at MCL is taken. It is alway's a dilemma if to appiy safer but less effective control strategy, or rulUling a system with more etTective, if a real disturbance input is favourable, but also more risky , as the real disturbance input may not be favourable , control strategy _ For this reason, the described above procedure is iterative_ Risk level, priority of the objectives, control means, and "in general" situation assessment, ,vhich are carried out by RAU and SAU support an operation of SuCL. It is popular in decision support theory and practise to base situation assessment on a disturbance scenario assessment. Sounds simple. One has already produced one or a set of disturbance scenarios and the corresponding set of control strategies_ Now, at a time instant one or more scenarios are selected from the set based on the measurement data. In other words the relevant at the time instant disturbance scenario is identified. The control strategy is picked up next from the list.
on-line risk assessment that IS carried out automatically_ The risk assessment unit plays a role of a decisio~ support facility In a case when such nonzero risk exists, variety of the risk level measures is considered. One possibility of such measure is a ratio of an area that is covered by predicted values lying outside the desired region that quantitati~'ely represents the constraint violation to the area 01 the predicted tube _The risk level measures are descnbed and widely discussed in (Brdys et al., 2002a: Grochowski, 2003 , SMAC D7, 2002) 3.1.2 Knowledge DiscovelY Unit
Generation of a new control strategy requires high computational effort. It is because of necessity of generating optimal control variable trajectories over long time horizon (for SCL) and with high resolution (for FCL). Computed for ' lower' layer solutions must suite (contribute) to the one from 'higher' level. To overcome this problem (minimise) there are special rules to preliminarily locate a list with, for example, wanted risk level or wanted effluent quality The rules and knowledge are designed in advance off line (by process operators and other specialists) and then uP
The activities of RAU and SAU are carried out at a programming phase of an overall operation leading to setting up adequate control strategies for the control layers o,'er next period_ The strategy selection i~ based on a robust prediction of the control performance. If a strategy without risk was selected then the performance is guaranteed and one can safely carry out applying the MPC till end of its predictio~ horizon. When a risky control strategy is chosen then it is necessary to morutor the controller performance during its o~ration as real disturba~ces may not be favourable and serious nolatlon ot an ac~eptable performance level may occur within a specific control layer. Not only the objectives allocated to this layer will suffer from reaching the desired level. This' may have significant impact on meeting the control objectives allocated to a higher layer even if the control strategy selected at the hi~er layer was not ris\"'-y. It is simply because the set points prescribed by the higher layer are not reached by the lower layer. Therefore, a dedicated unit the 'Performance Assessment Unit (pAD) is needed that would carry out on line performance assessment of the controllers at the control layers and
3.1. J Risk Assessment Unit
Due to uncertainty·, one calUlot be sure if the current control strategy a~hieves desired state of the process or leads to operating a process under a risk of not meeting all the control goals. A number of ditTerent sets of control objectives, thus control strategies, can be designed depending on the risk level in order to best achieve the control goals under "arying operating conditions of a process. Risk Assessment Unit (RAU) computes measures corresponding to the levels of not fulfilling the desired control objectives over specified time horizon and then a process operator may select on-line, based on a current risk level, suitable control strategy. For example, the selection may be driven by the risk level minimisation policy or by attempting meeting in a guaranteed manner as many as possible control objectives according to their priorities. In this approach, the process operator makes the decisions regarding currently used control strategies based on
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sewer network and septic tank include quality parameters: chemical oxygen demand (COD), total phosphate (fP) and total nitrogen (IN) State of the plant includes 20 parameters for each of the zones (anoxic , anaerobic, aerobic) and 4 parameters for septic and equalization tanks .
also would preprocess this infonnation into aggregated indicators. For example, when dissolved oxygen (DO) level prescribed by MCL is not reached over slot of ten minutes by the FCL then at least two questions arises: why th~ FCL is not able to better track the prescribed DO trajectory and what impact this may have on an m 'eral1 system perfonnance. Having'the desired setpoints and actual state from monitoring system, such situations can be noticed and the early warning to SuCL can be transferred . Described a'bove serves an example of interlayer contlicts (problems) and such situations should be found out and examined ('predicted' ) regarding the consequences on entire system. Smal1 deviations in tracking one of control1ed variables wouldn't have significant impact on the system but it could happen that several control1ed variables are tracked not quite wel1 as it should, and in such case, it is highly probable that it would have consequences on control quality . It is very ditllcult to predict or calculate such situations via traditional analytical tools . There are several AI methods that could help in this and it seems reasonable to use neural networks (NN) . Disadvantage of NN is that it is a black box structure. However, its very flexible structure and easiness of qualitative and heuristical1y knowledge utilization in model building process is the advantage . Certainly, not only monitoring the constraints and perfonnance functions of the MPC control1ers but also the overal1 system state monitoring IS necessary in order to gather infonnation needed to ~ssess the overal1 system operational performance. Moreover, dedicated measures, models and criteria are needed to transfer the directly monitored by hard and soft sensors variable and function values into the indicator values that constitute right infonnation for the control implementation phase mechanisms at SuCL . The criteria should link deviations in meeting the prescribed control objectives and the resulting consequences. The ditTerence between the SAU and PAU is that SAU is used for on line assessment of predicted perfonnance when the PAU is for on line assessment of achieved perfonnance. In addition PAU is capable of performing on line prediction. In summary, the PAU system is responsible for assessing on line fulfilment of the desired control objectives in a predictive manner, when MPC setpoints are not exactly realised by lower layers, and reporting that to the SuCL. Based on the on-line infonnation from PAU, the SuCL may decide to : stop using current control strategy, assess current operational situation of wwr system and select another strategy that is better adopted to the situation or continue with the old control strategy
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Fig. 3. Perfonnance assessment unit structure. The control1er performance history is a ditTerence between the calculated by optimiser control signals (i.e. dissolved oxygen concentration) and achieved in the real plant, in a few past periods of time . Outputs from this unit consist of indicators trajectories over 24 hours ahead . The indicators that represent each of the etlluent quality parameters (COD, BOO (biological oxygen demand), TP, IN and SS (suspended solids» are levels of discharge limits violation fonnulated in percents. In example, when total phosphorous at plant etlluent is 1. 23 [kg/m3] and the legal limit is 1 [kglm3] it means that the associated indicator will be 0.3 and this is because the level of limit violation is more then 20 % and less then 30 %. The range within the 0 .3 \'~!ue is active and it is limited by 1.3 value, what gives maximum 30% violation requirement. Trajectories regarding each of outflow parameters are generated by separate neural network, responsible for onc particular quality parameter. The situation is presented in Fig. 3. PAU unit consists of five neural networks responsible for five etlluent quality parameters. Each of the neural networks is trained separately on historical data . Fig. 4 presents the results ofPAU activity in phosphorus removal case. . - '" ~ flV~" ''''' _ '1
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The dynamic Elman network was used to create the PAU (Grochowski, 2003 ; Langowski, 2003). During learning process network is fed by : predictions of intlow from sewer network and septic tank o\'er 24 hours, actual state of the control1ed plant and control1er perfonnance history Both intlows from
Line I represents plant output with MPC setpoints applied: line 2 represents plant output with MPC
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denected setpoints applied and line 3 represents the maximum phosphate PAU prediction Notice how big is the difference between the predicted bv MPC plant output (line I) and reached by the plant (line I 2). However, PAU prediction (line 3) of possible discharge requirement \'iolation (lglm3 for phosphate case) performance is quite high, what might serve a great help for Supervisory Level when planning the control strategy to apply . Summing up, PAU based on available information produces on line for the SuCL information about possible behaviour of controlled plant outputs, when one or more manipulated variables are not able to reach the predicted by MPC engine levels over certain period of time . Unfortunately, PAU can not give SuCL the answer why the prescribed control values ha\'en' t been reached. It is not diagnostic unit. yet. PAU doesn ' t judge quality of given controls because it is not a control unit, also. It simply assesses the possible consequences of given control actions according to its best possessed knowledge . Neural networks placed in PAU gain and extend its knowledge when the control process operates. The properly learned and tuned neural network having the actual state of the plant, predicted iruluent parameters and computed controls can advise SuCL to apply (continue) or to reject the considered control actions.
objecti\·es. The secondary objectives allocated to a particular operational state are grouped into the sets of objectives. The sets are prioritised. Inside the particular list there are several objectives (typically constraints) to be met. Adding a set of the secondary objectives to the core objectives forms the complete MPC strategy . When examining the MPC strategy a risk factor plays a key role for normal and disturbed operational states. Sets (lists) allocated to nomlal and disturbed states are prioritised in such a way that a set (list) with the highest priority is some kind of "wishful thinking" set and a set with the minimum priority represents an absolutely mmimum what it is intended to achieve. In same cases a joint list of objectives is entirely composed of the core objectives. When being in 110111101 (or distllrbed) operational state the operator has potentially enough time to examine several objective lists so it can start with the most demanding one. In case when none of the lists checked so far is sufficiently good (high risk level of not fulfilling the desired objectives) and there is no time to continue the examining process the control trajectory that was found during the core objectives considering can be applied. When being in distllrbed operational state the operator has at least two ways of proceeding. Knowing that even the core objectives cannot be met running in the preferable manner, to operate the plant so that the preferable levels of control means and state variables are exceeded just enough to meet the core objectives. Alternatively , the operator knowing that it is inevitable to operate plant outside not preferable region accepts this and tries to fulfil certain additional objectives preparing the plant for better operation in a longer run. A secondary objective list can be less restrictive then the core one, but it can be more demanding as well . This results from the fact of not preferable extend of the control means usage possibilities. A situation when only because of phosphorus removal problems the core objectives list can not be fulfilled can serve an example. In such case one can use the chemical means I.e. PIX what probably would cause a signiticant (it of course depends on the quantity of PIX dosage) decrease of the phosphate level. Knowing this operator can fultil the rest of objectives in a more restrictive way or even extend their list. When being in emergency operational state a time the operator can atTord to examine available options is limited for obvious reasons. Also the risk factor is of less importance. The core objectives cannot be used as it is guaranteed that they will not be fultilled, prioritising the secondary objective lists is different than for the normal and disturbed states. Namely , the list \"'ith the highest priority it is the one with only few main goals just sutlicient for the plant protection. The successive lists are more and more demanding. There are occurrences, as for example toxic discharge, that a decision must be made immediately . Hence, running MPC but in real time in order to optimise the control actions to be undertaken may, in this situation, would not be feasible . This calls for employing a concept of prepared in advance
3.3 Decision making process From a formal design point of view, the control strategy selection problem can be formulated as a synthesis of mechanisms that takes the relevant measurements in a system, estimates and predictions and returns the strategies. Optimising certain one criterion over long enough time period would pursue an optimal mechanism for on-line control strategy selection. This approach was found not feasible due to complexity of system dynamics, truly multiobjective structure of the control problem and also due to the fact that long term predictions of hydraulic and pollutant loads are not available with sufIicient accuracy that includes the uncertainty bounds and/or statistical information. Instead, an approach is proposed that seems to be a good mix between existing operational expenence, understanding of what happens in the system from measurement data and more formal , but still based on understanding of operational situation, concepts and techniques . The algorithm consists of two stages (Brdys et al., 2002b; Grochowski, 2003) • Stage I-Operational State Assessment, and • Stage 11- Control Strategy Selection. In Stage I the operational state of the plant IS assessed (identitied) based on the core objective list examining. In order to assess the operational state, the SuCL activates SAU and RAU to determine a risk of not meeting the core objectives by a control trajectory that is generated by MPC controller with the core objectives embedded. With the operational state known, the control strategy is selected by examining all possible control strategies allocated to the operational state via the secondary control
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and get ready to use rules (say for simplicity bans and orders). The prepared and extensively tested earlier by simulation, based on very representative data records, sequels of such bans and orders can be named Rule Based Control. As a routine action a control layer uses the selected control strategy till the end of its control horizon unless SuCL assesses different operational state of the WWTP-SN system, thus changing the control obJectives is required. or/and PAU identifies unwanted consequences of the risk undertaken in thc system operation. Applying the selected control strategy is then stopped and the new strategy is searched for. As a whole a suboptimal tracking of the unknown globally optimised control strategy is achieved by switching behveen the control strategies. Problem of proper decision that has to be taken when choosing the control strategy to be applied is very complex and ditlicult problem. Supervisor must find the best possible compromise between the operation cost, process safety and ct1luent quality. Specialised models are needed to help the SuCL in decision making process. Building such models and their tuning still remains open and challenging problem.
plant- sewer systems. I Intcrnational Conference on Technology. Automation and Control of Wastewater and Drinking Water SystemsTiASWiK'02 , Gdansk-Sobieszewo, June 19-21 . 2002 - Poland. Duzinkiewicz, K., M A Brdys. and T. Chang, (2003) . Hierarchical model predictive control of integrated quality and quantity in drinking water distribution systems Urban Water (in press) Gminski T. , M.A Brdys M Grochowski and M. Drewa (2004) . Model Predictive Controller of mtegrated wastewater systems. IF AC 10th Symposium Large Scale Systems Theory and Applications July 26-28 2004, Osaka - Japan Grochowski M. (2003). Intelligent control of integrated \vastewater treatment system under full range of operating conditions. PhD thesis, Gdansk University of Technology. Grochowski M. M. A Brdys and T. Gminski (2004a). Intelligent control structure of integrated wastewater treatment systems. IF AC 10th Symposium Large Scale Systems : Theory and Applications. July 26-28 2004, Osaka Japan. Grochowski M, M. A Brd)' , T. Gminski, and P De inry ch. (2004b). Softly switched model predictive control for control of integrated wastewater treatment systems at medium time scale. IF AC 10th Symposium Large Scale Systems: Theory and Applications. July 26-28 2004, Osaka - Japan Langowski, R., (2003) . Jednostka oceny jakosci sterowania w hierarchicznej struk1urLe sterowania zintegrowanym systemem oczyszczania sciekow (l\fSC diploma in Polish) . Olsson, G. and R. Newell (1999). TFastewater Treatment Systems. Afodelling. Diagnosis and C olltro/. IW A Publishing, London. Rutkowski T . (2004). Modele typu 'szara skrzynka' dla potrzeb estymacJi zmiennych i sterowania predykcyjnego z zastosowaniem w zintegrowanych systemach sciekowych. PhD thesis. Gdansk University of Technology Rutkowski T. , M. A, Brdys, K. Konarczak, and T. Gminski (2004) . Set-bounded joined parameter and state estimation for model predictive control of integrated wastewater treatment plant systems at medium time scale. IF AC 10th Symposium Large Scale Systems: Theory and Applications. July 26-28 2004, Osaka - Japan . SMAC D7 (2002) SMArt Control of wastewater systems, Deliverat'>le 7: Report on Measures and Algorithins for Risk and Situation Assessment. Editor: UBlMietek A Brdys. EU project Smart Control of Wastewater Systems - SMAC, EVK)CT -2000-00056. Wierzbicki. A.P .. M. Makowski and 1. Wessc\s. Eds (2000) . Model-based Decision Support .\/ethodology with El1vironmental Applications. Series: Mathematical Modelling and Applicatiol1s. Kluwer Academic Publishers, Dordrecht.
ACKNOWLEDGMENT This work was supported by the European Commission under contract number EVK I-CT -200000056 SMAC and by the Polish State Conul1ittee for Scientific Research under grants No . 8TlIA-021-18 and No. 8TlIA-009-24. The authors wish to express their thanks for the support. REFERENCES Brdys, M. A, T. Chang and K. Duzinkiewicz (200 I a). Ir:telligent model predictive control of chlorine residuals in water distribution systems. Proc. of the 4th ASCE Annual Water Distribution Systems Analysis, 200 I World Water and Environmental Resources Congress. Orlando. May 20-24, 2001. Brdys, M A and T. Chang (200Ib) Robust Model Predictive Control of Chlorine Residuals in Water Systems Based on a State Space Modelling. In: B. Ulanicki, B. Coulbeck and J Rance, Eds . Water Software Systems: theO/yand applications, Research Studies Press Ltd , Baldock, Hertfordshire, England. Vol I, pp.HI245. Brdys. M. A , M. Grochowski, K. Duzinkiewicz. and Y. Liu (2002a) . On line risk assessment for control: methodology, algoritluns and applications to wastewater systems. I International Conference on Technology , Automation and Control of Wastewater and Drinking Water Systems-TiASWiK'02, GdanskSobieszewo, June 19-21 , 2002 - Poland Brdys, M. A , M Grochowski, K. Duzinkiewicz and W. Chotkowski, (2002b) Design of control structure for integrated wastewater treatment
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Copyright © IFAC Large Scale Systems: Theory and Applications, Osaka, Japan, 2004
IFAC PUBLICATIONS www.elsevier.comllocatelifac
SUPERVISORY CONTROL OF INTEGRATED WASTEWATER TREATMENT SYSTEMS M. Grochowskj(1), M.A. Brd~'s(Z), K. DuzinkiewicZ
(1)
Department afAlltomatic Contral, Gdansk University afTeclmology, III G. Nanttowicza 11/12, 80952 Gdansk, Poland,
email:[email protected]. kdltzill([[email protected]
m School ofEngineering, Department af Electrical, Electronic alld C01l7plller
Engineering, The University of Binllingham, Binllingham B 15 2TT, UK, e1l7ail: [email protected]
Abstract: Control of integrated wastewater treatment plant-sewer systems under full range of disturbance inputs enforces employing advanced optimising control algorithms. The control activities are hierarchically structured into a multi levelmultilaver form. Model Predictive Control is chosen as a core control technology . As it is im~ssible to handle sutliciently well all operating conditions of this system, the operating states of the system are distinguished and these are : normal, disturbed and emergency. Formulation of operational states requires introducing the concepts of the core and secondary objectives that divide the system objectives into obligatory and additionally ones. DiITerent control trajectories are designed for corresponding operationai states in order to best adopt control actions to current plant conditions. In order to effectively control such complex system the Supervisory Level (SuCL) is introduced: Its main functionality is: to coordinate activities of the control structure, to asses operational states of the ~ystem, to asses selection of the control strategies, to asses imposed control setpoints realisation and risk of not fulfilling the objectives. Paper describes units adjacent to SuCL that major function is to provide the information needed by the SuCL and decision making process. Copyright © 200-l1FAC Keywords: Supervisory control, decision making, neural networks, hierarchical control optimal control, risk, waste treatment.
of three levels (Brdys, et al., 2002b; Grochowski, et al., 2004) . The upper control level - Supervisory Control Level (SuCL) performs supervisory control actions. The optirnising control actions are generated at the second - Optirnising Control Level (OCL). The Model Predictive Control (MPC) is proposed as a control technology to be applied at this level. The optirnised manipulated variable trajectories are set points for the third level where the system is actuated forcing the process to follow the prescribed trajectories. There is a multiple time scale structure in a wastewater system internal dynamics. This structure is used in order to distribute the control objectives defined over ditTerent time horizons among ditTerent control layers of the optirnising control level. Three control layers are distinguished slo",' (SCL), medium (MCL) and fast (FCL). Structuring the optirnising control level into the three control layers allows reducing complexity of the
I. INTRODUCTION Nowadays, both European and Nationwide regulations force high standards on nitrogen and phosphate contents in treated sewage, including nitrogen and phosphate removal. In order to manage fulfilling these standards, Wastewater Treatment plants (WWTPs) become more and more technologically complicated systems. One of the main problems is high variation of sewage inlluent that requires fast enough proacti"e response of a control system The variations caused by the rain, storms and snowmelt lead to decreasing the etnuent quality and increasing the operational cost, if not properly handled. In the accompanying paper (Grochowski et aI., 2oo4a) structure and methods for integrated WWfP and Sewer Network (SN) system control are proposed. The control action generating is organised within a hierarchical structure composed
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optimising control problem. The local controllers/and manipulators at Follow up Control Level force the setpoints prescribed at Optimising Level onto the plant In order to etlectively control such complex system, the described control structure has to be able to efficiently operate. This is the reason for introducing Supervisory Level. In general , the control strategy selection, soft switching between the control strategies and overriding actions at different le\'els and layers of supervised structure are its main functionalities In this paper MPC technology is extended and supplemented by concepts of operational states, prioritising the control means and objective sets separation. Apart from this the functionalities of SuCL, such as risk and performance assessment, are described. Another task of SuCL, the switching between the control strategies is presented in (Grochowski et al.. 2004b) . As it is impossible to handle sufficiently well all running conditions of integrated wastewater treatment system (IWWTS), three operating states of a process are distinguished: normal, disturbed and emergency (Brdys et al.. 2002b; Grochowski, 2003). The state assessment is based on most important for WWT plants, examining in a predictive manner the core objectives. Apart from these minimum requirements, possibility of meeting the additional objectives (secondary objectives) is also investigated. Hence, different control strategies are proposed and tested for ditlerent operating conditions (Gminski, et al. . 2004).
The solid lines m Fig. 1 symbolise information now such us : o~iectives , setpoints, constraints, measurements, risk and performance indicators etc, whiles doted lines represent information exchange during dialogue processes. Certain mam parts in this structure can be distinguished: the monitoring system, Follow up Control Level, Optimising Control Level (broken into SCL MCL and FCL). Supervisory Level. There are dedicated units in that control system that major function is to pro\'ide the information needed by the SuCL These are the Situation Assessment Unit (SAU) (that includes Risk Assessment Unit-RAU and Knowledge Discovery Unit-KDU) and Performance Assessment Unit- PAD Interaction and communication behveen the layers and units as well as performance assessment, situation assessment coordination, control strategy selection are the tasks that should be carried out bv the supervisor and its agents The SAU and PAU agents play a supporting role in this structure The SAU carries out routine activities but it also needs to be prepared to get involved in a dialogue with the SuCL and to quickly answer questions stated by the SuCL. Based on an assessment of an operational situation of rwWTS system the SuCL aIJocates suitable control strategies to control layers by employing the mechanisms to be still designed. Regardless on a quality of the SuCL mechanisms employed to make the final selection of a control strategy to be applied the strategy is selected based on a prediction of the system operational performance. However, due to uncertainty the predicted performance is not the same as the performance that will be achieved in the system. The achieved performance is on line monitored and assessed by PAU. The MPC mechanisms at the control layers check the achieved performance indirectly by comparing the model responses with the WWTP - SN states at discrete time instants ending the MPC's time steps. PAU generates more accurate information about achieved performance and based on this information SuCL may halt applying current control strategies during these time steps
2. FUNDAMENTALS OF THE CONTROL STRUCTURE The proposed structure is a multilevel and multilayer. There are two ways of its decompositions (or one can say synthesis). Namely the control system is broken down into so called, Levels and Layers. The Levels emerge as a result of a functional decomposition of the control system, The Layers are produced by employing time decomposition based on ditlerent time scales in dynamics of the controlled processes. The resulting information structure is illustrated in Fig. I.
2.1 Control means
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.--__,--______ I----::::;--I!---~~t-----.,
Practice at the WWT plants has shown that certain control handles or ways of operating the plants are less favourable than others. Hence, there is a need to distinguish behveen the available ways of controlling the plant It can be done by selecting different levels of usage of control nfeans or by selecting different control method. A prioritisation of the control means into the technologically preferred and technological not preferred is introduced in order to capture an operational reality of a wide class of wastewater systems. The technologically not preferred control means are the control means that are utilized more intensively then the operator (specialist, WWTP designers etc) would like. Their wishes can then be quantified by specifying certain desired operating ranges . The tec/mologically preferred control memlS operate inside these desired ranges . Note, that the above has nothing to do with technical possibilities
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Fig. I. Hierarchical intelligent control structure.
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of i.e. actuators. It is because whether or not a control handle or a way of operating system is technologically preferred entirely depends on a degree of utilization of the control handle or the way of operating a system and the technical lirnitaho~s are assumed to be respected. The excessive usage of chemicals for phosphorus removal, wastewater bypassing route or keeping almost fully filled equalization tanks can serve as the exa~ples of technologically not preferred control means. These control means can ditler at ditlerent WWTP. It is because they depend on existing technological constraints, operators technological experience and attitude regarding the ways of treating process at different WWTP. The technologically preferred control means always have the highest priority and it is tried to manage the process by entirely using these control means. Apart from the technologically 110t preferred control means usage, certain ways of controlling the plant are not preferred although their use is justified under certain operational conditions. Running the plant, for a while, with small overtlowing when rain suddenly occurs can serve the example of such case. It can be justified if ensures benefits over longer time horizon or if it prevents from sludge washout. However, permanently operating with even small level of overtlow is definitely not welcome. In practise, it leads to keeping the plant within preferable operating region in a space of control inputs and plant states. However, sometimes operators are forced to run the plant outside this region and run the plant in not preferred operating region but still remaining inside the legal one. Summarising, prioritising the control means gives the operator a room for running the plant in a number of ways. The preferred and not preferred operating regions are illustrated in Fig. 2. Regarding the control means the denotations "max" in Fig. 2 means that this is the level of maximum exploitation of control means (read: maximum attainable).
legislators the sewage concentration discharge requirements and the technological constraints. The other issue is concerned with the ohjectives that are desired by the plant operators. Not all of them can be completely achieved under all possible disturbances regarding weather conditions, hence hydraulic and pollutant loads, at reasonable operating cost and capital cost of building and maintaining the plant Hence, there is a need for adopting the objectives to current situation that is described by the current state of the plant and prediction of disturbances over suitable horizon. To ensure the fulfilment of the obligatory ones. the objectives are split into the core objectives and secondary objectives The core objectives are concerned with the obligatory demands and they represent minimum requirements to be met regarding the etlluent discharge and technological limitations. Fulfilling of the core objectives means that WWT plant works properly but with minimum set of requirements. As the WWTP capabilities are different then the core objectives are plant dependent. Listing them should be jointly done by technological and scientitic experts by trading between process safety and financial bene tits. An example of such core list is presented in Tab. I. The core list consists of the main control objectives, only . In practice more of them could be considered but it is, of course, plant depended. Apart from the core objectives, there are also other objectives that operators would be glad to meet, if possible, knowing that the core objectives have been already fulfilled. They can consist of more ambitious then legal eft1uent constraints or i.e. running the process without chemical dosing. It leads to modification of the core list into more demanding one. It might consist in strengthening or/and extending the core objectives. The resulting objectives are called secondary objective As distinct from the core objective list, there could be a lot of the secondary objectives lists and they are depended on the plant intluent conditions, its range of changing, accessibility of control means, structure of the plant and, tinally on operating stutT experience and their ingenuity.
u,(t)
Table I Example of Core objectives list Con objedives to be fulfilled 1. Keep effluent TN {total mtrogm):S:1 0 mgldm' 2. Keep ellIumt 1P (total phosphorus):S:1 mgidm' l . Keep effluent COD Q5 mgO,/dm' .a Keep eftluent BOO, :S:15 mgO,/dm' S. Keep ellIuent TSS (total suspmded solids) ~5 mg/dm' 6. Keep maxunum hourly inflow Q,.,a:S: I 000 [m' tb I 7. Keep ma'
Fig. 2. Possible operational regIOns of WWTP operating. Operating regions constitute a base for defming the operational states for the system assessment purposes.
The idea of secondary objective lists comes from the reason of giving the plant operators a chance to improve the plant operation under many different conditions and comes across permanent forcing the requirements by the legislators and costs saving, of course.
2.2 Core and secondary objectives
There are two issues when considering the objectives to be met by the WWT plants. One is the imposed by
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2.3 Operational states
that provides the best support for these activities. The supervisory level (SuCL) needs suitable information regarding operational situation in the system and mechanisms to transfer this information into commands and/or advises to be sent to other control levelsllayers over long time horizon . Thanks to skilful ~sage of appropriate units (agents) and data . SuCL possesses all information needed to estimate current state of the system Information trom all control structure units is available for SuCL at every time step with a time resolution that is adequate to the unit operating time scale. Thanks to that, SuCL has global knowledge about current activity of entire system and is able to coordinate operations of the ~ther levels and layers and suitably respond to unwelcome or "unse~n" at some layers' events ,,,ithin the plant. Short duration e.g., one or two hours, but very heavy rain can serve as an example of such sit~tion . As a time step at Slow Control Layer is greater than the rain duration period, the layer is not able to see these kinds of events. It is a task of a Medium Control Layer to respond to these events . The MCL is capable of handling this kind of disturbance, but consequences of the rain may considerably exceed a prediction horizon of this layer. Hence, the SuCL needs to act jointly with the MCL and SCL in order to properly handle this situation. This situation serves an example of a short duration disturbance event with long-term consequences. For each of the operational states there are appropriate control strategies (Grninski et al., 2004; Grochowski, 2003) that improve selecting the best at the moment control actions regarding actual and predicted state of the plant. Selecting proper control strategy and scheduling the switching between them is the main activity at the SuCL. The switching process should be carried out very carefully because of constraints on state variables changiu'g rate and uncertainty existence. Ways of smooth changing the current control strategy, the soft switching, in case of MPC control technology are described in the accompanying paper (Grochowski et al., 2004). There are units designed in order to help SuCL in the decision making process. These are : Situation Assessment Unit (SAU) and Performance Assessment Unit (PAU).
As it is impossible to handle suniciently well all nlIlling conditions of WWTSN system, in order to best fit the control actions into the actual and predicted conditions, different operational states of a process are distinguished. The main thing is to ensure that all of the obligatory objectives will be fultilled . In order to do that, the control trajectories entirely based on core control objecti"es are computed, by MPC over the prediction honzon. There is a need to account for an uncertainty when planmng (choosing) the control strategy . A described in (Brdys et al., 2002a; Rutkowski, 2004; Rutkowski, et aI., 2004) set membership uncertainty modelling allows for the robust output predictions (SMAC D7, 2002 ; Duzinkiewicz et aI., 2003). Knowing the current process state and predicting the uncertain outputs the possibility of fulfilling all the core control objectives over a considered time horizon is robustly assessed. The result of this assessment implies the operational state of the system (Brdys et aI., 2002b; Grochowski, 2003). Paper considers three operational states: nonnai, disturbed and emergency. If there is a guarantee of achieving all the core control goals by running the plant inside preferred operating region, then the process is said to be in a nonl/al operating state. If there is no possibility of achieving all core control objectives over considered time horizon even if the plant operates in not preferred regiol/, then the process is said to be in an emergency operational state . Finally, if there is no guarantee of achieving core control objectives over considered time horizon without entering the not preferred operating region, then the process is said to be in a disturbed operational state. There are two possible actions when being in the distllrbed operating state : to continue with the current control strategy or to increase the levels of utilizing the particular control means beyond the desired one such as PIX or overflow below the threshold level in order to regain a guarantee of meeting the core control objectives. The operational states are assessed on a base of examining the possibility of fulfilling the objectives from the core control list (Tab. I). It is done using the Robust Model Predictive Control algorithm (RMPC). The RMPC was successfully applied to drinking water distribution systems (Brdys et al. . 2001a, b; Duzinkiewicz et al.. 2003). Violation of eft1uent constraints from the core objectives list can be expressed by risk level of not meeting the objectives (Brdvs et al., 2002a; Grochowski, 2003). Such risk can be very useful factor helping SuCL in selecting the control strategy to apply at current operational state.
3.1 Situation assessment IInil. SAU is an intermediate unit between SuCL and OCL, which translat~ SuCL requests into ' language ' that is understood by optirniser, what means: objective functions, constraints, control means, etc and returns to SuCL operational cost, controlled and output variables trajectories and associated risk As it is shown in Fig. 1, SAU consists of subunits: RAU (Risk Assessment Unit), KDU (Knowledge Discovery Unit). A kno,,,n level of risk of not fulftlling' the prescribed core control objectives (computed by RAU) is a base for the situation assessment. The risk level is an important, if not decisive, factor taken into account by SuCL when setting a control strategy and choosing an operational state for the plant to be eventually transferred into.
3. SUPERVISORY CONTROL LEVEL A supervisory control level is located at the top of the control system hierarchy. The supervisory activities are distinguished trom other control and monitoring activities as a centralised control level
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The situation assessment gives an answer in which operating state a process is at present and to which operational states it could be transferred in the coming future if the current control strategy is maintained_ RAU along with SAU, on a command from SuCL, activates an optimismg control layer to generate control actions for investigated control objecti\-es expressed by MPC performance indices and constraint functions and by using im'estigated control means_ Produced by the optimiser control actions are assessed by the RAU and they are transferred to SAU with the information about level of risk of not fulfilling the control objectives over a time horizon. In a qualitative form the task, which is allocated to SAU by SuCL, can be: "I am considering this controi strategy 10 be used by AICL over coming horizon. Please, find Ol/t what is the associated risk. If the risk level is greater than 0.3 do not bother. OthentJise, let me Imow. ., In a case of obtaining requested control with the risk not exceeding 0_3 the positive information is transferred to the Supervisor where decision about applying or not the screened by SuCL, control strategy at MCL is taken. It is alway's a dilemma if to appiy safer but less effective control strategy, or rulUling a system with more etTective, if a real disturbance input is favourable, but also more risky , as the real disturbance input may not be favourable , control strategy _ For this reason, the described above procedure is iterative_ Risk level, priority of the objectives, control means, and "in general" situation assessment, ,vhich are carried out by RAU and SAU support an operation of SuCL. It is popular in decision support theory and practise to base situation assessment on a disturbance scenario assessment. Sounds simple. One has already produced one or a set of disturbance scenarios and the corresponding set of control strategies_ Now, at a time instant one or more scenarios are selected from the set based on the measurement data. In other words the relevant at the time instant disturbance scenario is identified. The control strategy is picked up next from the list.
on-line risk assessment that IS carried out automatically_ The risk assessment unit plays a role of a decisio~ support facility In a case when such nonzero risk exists, variety of the risk level measures is considered. One possibility of such measure is a ratio of an area that is covered by predicted values lying outside the desired region that quantitati~'ely represents the constraint violation to the area 01 the predicted tube _The risk level measures are descnbed and widely discussed in (Brdys et al., 2002a: Grochowski, 2003 , SMAC D7, 2002) 3.1.2 Knowledge DiscovelY Unit
Generation of a new control strategy requires high computational effort. It is because of necessity of generating optimal control variable trajectories over long time horizon (for SCL) and with high resolution (for FCL). Computed for ' lower' layer solutions must suite (contribute) to the one from 'higher' level. To overcome this problem (minimise) there are special rules to preliminarily locate a list with, for example, wanted risk level or wanted effluent quality The rules and knowledge are designed in advance off line (by process operators and other specialists) and then uP
The activities of RAU and SAU are carried out at a programming phase of an overall operation leading to setting up adequate control strategies for the control layers o,'er next period_ The strategy selection i~ based on a robust prediction of the control performance. If a strategy without risk was selected then the performance is guaranteed and one can safely carry out applying the MPC till end of its predictio~ horizon. When a risky control strategy is chosen then it is necessary to morutor the controller performance during its o~ration as real disturba~ces may not be favourable and serious nolatlon ot an ac~eptable performance level may occur within a specific control layer. Not only the objectives allocated to this layer will suffer from reaching the desired level. This' may have significant impact on meeting the control objectives allocated to a higher layer even if the control strategy selected at the hi~er layer was not ris\"'-y. It is simply because the set points prescribed by the higher layer are not reached by the lower layer. Therefore, a dedicated unit the 'Performance Assessment Unit (pAD) is needed that would carry out on line performance assessment of the controllers at the control layers and
3.1. J Risk Assessment Unit
Due to uncertainty·, one calUlot be sure if the current control strategy a~hieves desired state of the process or leads to operating a process under a risk of not meeting all the control goals. A number of ditTerent sets of control objectives, thus control strategies, can be designed depending on the risk level in order to best achieve the control goals under "arying operating conditions of a process. Risk Assessment Unit (RAU) computes measures corresponding to the levels of not fulfilling the desired control objectives over specified time horizon and then a process operator may select on-line, based on a current risk level, suitable control strategy. For example, the selection may be driven by the risk level minimisation policy or by attempting meeting in a guaranteed manner as many as possible control objectives according to their priorities. In this approach, the process operator makes the decisions regarding currently used control strategies based on
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sewer network and septic tank include quality parameters: chemical oxygen demand (COD), total phosphate (fP) and total nitrogen (IN) State of the plant includes 20 parameters for each of the zones (anoxic , anaerobic, aerobic) and 4 parameters for septic and equalization tanks .
also would preprocess this infonnation into aggregated indicators. For example, when dissolved oxygen (DO) level prescribed by MCL is not reached over slot of ten minutes by the FCL then at least two questions arises: why th~ FCL is not able to better track the prescribed DO trajectory and what impact this may have on an m 'eral1 system perfonnance. Having'the desired setpoints and actual state from monitoring system, such situations can be noticed and the early warning to SuCL can be transferred . Described a'bove serves an example of interlayer contlicts (problems) and such situations should be found out and examined ('predicted' ) regarding the consequences on entire system. Smal1 deviations in tracking one of control1ed variables wouldn't have significant impact on the system but it could happen that several control1ed variables are tracked not quite wel1 as it should, and in such case, it is highly probable that it would have consequences on control quality . It is very ditllcult to predict or calculate such situations via traditional analytical tools . There are several AI methods that could help in this and it seems reasonable to use neural networks (NN) . Disadvantage of NN is that it is a black box structure. However, its very flexible structure and easiness of qualitative and heuristical1y knowledge utilization in model building process is the advantage . Certainly, not only monitoring the constraints and perfonnance functions of the MPC control1ers but also the overal1 system state monitoring IS necessary in order to gather infonnation needed to ~ssess the overal1 system operational performance. Moreover, dedicated measures, models and criteria are needed to transfer the directly monitored by hard and soft sensors variable and function values into the indicator values that constitute right infonnation for the control implementation phase mechanisms at SuCL . The criteria should link deviations in meeting the prescribed control objectives and the resulting consequences. The ditTerence between the SAU and PAU is that SAU is used for on line assessment of predicted perfonnance when the PAU is for on line assessment of achieved perfonnance. In addition PAU is capable of performing on line prediction. In summary, the PAU system is responsible for assessing on line fulfilment of the desired control objectives in a predictive manner, when MPC setpoints are not exactly realised by lower layers, and reporting that to the SuCL. Based on the on-line infonnation from PAU, the SuCL may decide to : stop using current control strategy, assess current operational situation of wwr system and select another strategy that is better adopted to the situation or continue with the old control strategy
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Fig. 3. Perfonnance assessment unit structure. The control1er performance history is a ditTerence between the calculated by optimiser control signals (i.e. dissolved oxygen concentration) and achieved in the real plant, in a few past periods of time . Outputs from this unit consist of indicators trajectories over 24 hours ahead . The indicators that represent each of the etlluent quality parameters (COD, BOO (biological oxygen demand), TP, IN and SS (suspended solids» are levels of discharge limits violation fonnulated in percents. In example, when total phosphorous at plant etlluent is 1. 23 [kg/m3] and the legal limit is 1 [kglm3] it means that the associated indicator will be 0.3 and this is because the level of limit violation is more then 20 % and less then 30 %. The range within the 0 .3 \'~!ue is active and it is limited by 1.3 value, what gives maximum 30% violation requirement. Trajectories regarding each of outflow parameters are generated by separate neural network, responsible for onc particular quality parameter. The situation is presented in Fig. 3. PAU unit consists of five neural networks responsible for five etlluent quality parameters. Each of the neural networks is trained separately on historical data . Fig. 4 presents the results ofPAU activity in phosphorus removal case. . - '" ~ flV~" ''''' _ '1
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The dynamic Elman network was used to create the PAU (Grochowski, 2003 ; Langowski, 2003). During learning process network is fed by : predictions of intlow from sewer network and septic tank o\'er 24 hours, actual state of the control1ed plant and control1er perfonnance history Both intlows from
Line I represents plant output with MPC setpoints applied: line 2 represents plant output with MPC
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denected setpoints applied and line 3 represents the maximum phosphate PAU prediction Notice how big is the difference between the predicted bv MPC plant output (line I) and reached by the plant (line I 2). However, PAU prediction (line 3) of possible discharge requirement \'iolation (lglm3 for phosphate case) performance is quite high, what might serve a great help for Supervisory Level when planning the control strategy to apply . Summing up, PAU based on available information produces on line for the SuCL information about possible behaviour of controlled plant outputs, when one or more manipulated variables are not able to reach the predicted by MPC engine levels over certain period of time . Unfortunately, PAU can not give SuCL the answer why the prescribed control values ha\'en' t been reached. It is not diagnostic unit. yet. PAU doesn ' t judge quality of given controls because it is not a control unit, also. It simply assesses the possible consequences of given control actions according to its best possessed knowledge . Neural networks placed in PAU gain and extend its knowledge when the control process operates. The properly learned and tuned neural network having the actual state of the plant, predicted iruluent parameters and computed controls can advise SuCL to apply (continue) or to reject the considered control actions.
objecti\·es. The secondary objectives allocated to a particular operational state are grouped into the sets of objectives. The sets are prioritised. Inside the particular list there are several objectives (typically constraints) to be met. Adding a set of the secondary objectives to the core objectives forms the complete MPC strategy . When examining the MPC strategy a risk factor plays a key role for normal and disturbed operational states. Sets (lists) allocated to nomlal and disturbed states are prioritised in such a way that a set (list) with the highest priority is some kind of "wishful thinking" set and a set with the minimum priority represents an absolutely mmimum what it is intended to achieve. In same cases a joint list of objectives is entirely composed of the core objectives. When being in 110111101 (or distllrbed) operational state the operator has potentially enough time to examine several objective lists so it can start with the most demanding one. In case when none of the lists checked so far is sufficiently good (high risk level of not fulfilling the desired objectives) and there is no time to continue the examining process the control trajectory that was found during the core objectives considering can be applied. When being in distllrbed operational state the operator has at least two ways of proceeding. Knowing that even the core objectives cannot be met running in the preferable manner, to operate the plant so that the preferable levels of control means and state variables are exceeded just enough to meet the core objectives. Alternatively , the operator knowing that it is inevitable to operate plant outside not preferable region accepts this and tries to fulfil certain additional objectives preparing the plant for better operation in a longer run. A secondary objective list can be less restrictive then the core one, but it can be more demanding as well . This results from the fact of not preferable extend of the control means usage possibilities. A situation when only because of phosphorus removal problems the core objectives list can not be fulfilled can serve an example. In such case one can use the chemical means I.e. PIX what probably would cause a signiticant (it of course depends on the quantity of PIX dosage) decrease of the phosphate level. Knowing this operator can fultil the rest of objectives in a more restrictive way or even extend their list. When being in emergency operational state a time the operator can atTord to examine available options is limited for obvious reasons. Also the risk factor is of less importance. The core objectives cannot be used as it is guaranteed that they will not be fultilled, prioritising the secondary objective lists is different than for the normal and disturbed states. Namely , the list \"'ith the highest priority it is the one with only few main goals just sutlicient for the plant protection. The successive lists are more and more demanding. There are occurrences, as for example toxic discharge, that a decision must be made immediately . Hence, running MPC but in real time in order to optimise the control actions to be undertaken may, in this situation, would not be feasible . This calls for employing a concept of prepared in advance
3.3 Decision making process From a formal design point of view, the control strategy selection problem can be formulated as a synthesis of mechanisms that takes the relevant measurements in a system, estimates and predictions and returns the strategies. Optimising certain one criterion over long enough time period would pursue an optimal mechanism for on-line control strategy selection. This approach was found not feasible due to complexity of system dynamics, truly multiobjective structure of the control problem and also due to the fact that long term predictions of hydraulic and pollutant loads are not available with sufIicient accuracy that includes the uncertainty bounds and/or statistical information. Instead, an approach is proposed that seems to be a good mix between existing operational expenence, understanding of what happens in the system from measurement data and more formal , but still based on understanding of operational situation, concepts and techniques . The algorithm consists of two stages (Brdys et al., 2002b; Grochowski, 2003) • Stage I-Operational State Assessment, and • Stage 11- Control Strategy Selection. In Stage I the operational state of the plant IS assessed (identitied) based on the core objective list examining. In order to assess the operational state, the SuCL activates SAU and RAU to determine a risk of not meeting the core objectives by a control trajectory that is generated by MPC controller with the core objectives embedded. With the operational state known, the control strategy is selected by examining all possible control strategies allocated to the operational state via the secondary control
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and get ready to use rules (say for simplicity bans and orders). The prepared and extensively tested earlier by simulation, based on very representative data records, sequels of such bans and orders can be named Rule Based Control. As a routine action a control layer uses the selected control strategy till the end of its control horizon unless SuCL assesses different operational state of the WWTP-SN system, thus changing the control obJectives is required. or/and PAU identifies unwanted consequences of the risk undertaken in thc system operation. Applying the selected control strategy is then stopped and the new strategy is searched for. As a whole a suboptimal tracking of the unknown globally optimised control strategy is achieved by switching behveen the control strategies. Problem of proper decision that has to be taken when choosing the control strategy to be applied is very complex and ditlicult problem. Supervisor must find the best possible compromise between the operation cost, process safety and ct1luent quality. Specialised models are needed to help the SuCL in decision making process. Building such models and their tuning still remains open and challenging problem.
plant- sewer systems. I Intcrnational Conference on Technology. Automation and Control of Wastewater and Drinking Water SystemsTiASWiK'02 , Gdansk-Sobieszewo, June 19-21 . 2002 - Poland. Duzinkiewicz, K., M A Brdys. and T. Chang, (2003) . Hierarchical model predictive control of integrated quality and quantity in drinking water distribution systems Urban Water (in press) Gminski T. , M.A Brdys M Grochowski and M. Drewa (2004) . Model Predictive Controller of mtegrated wastewater systems. IF AC 10th Symposium Large Scale Systems Theory and Applications July 26-28 2004, Osaka - Japan Grochowski M. (2003). Intelligent control of integrated \vastewater treatment system under full range of operating conditions. PhD thesis, Gdansk University of Technology. Grochowski M. M. A Brdys and T. Gminski (2004a). Intelligent control structure of integrated wastewater treatment systems. IF AC 10th Symposium Large Scale Systems : Theory and Applications. July 26-28 2004, Osaka Japan. Grochowski M, M. A Brd)' , T. Gminski, and P De inry ch. (2004b). Softly switched model predictive control for control of integrated wastewater treatment systems at medium time scale. IF AC 10th Symposium Large Scale Systems: Theory and Applications. July 26-28 2004, Osaka - Japan Langowski, R., (2003) . Jednostka oceny jakosci sterowania w hierarchicznej struk1urLe sterowania zintegrowanym systemem oczyszczania sciekow (l\fSC diploma in Polish) . Olsson, G. and R. Newell (1999). TFastewater Treatment Systems. Afodelling. Diagnosis and C olltro/. IW A Publishing, London. Rutkowski T . (2004). Modele typu 'szara skrzynka' dla potrzeb estymacJi zmiennych i sterowania predykcyjnego z zastosowaniem w zintegrowanych systemach sciekowych. PhD thesis. Gdansk University of Technology Rutkowski T. , M. A, Brdys, K. Konarczak, and T. Gminski (2004) . Set-bounded joined parameter and state estimation for model predictive control of integrated wastewater treatment plant systems at medium time scale. IF AC 10th Symposium Large Scale Systems: Theory and Applications. July 26-28 2004, Osaka - Japan . SMAC D7 (2002) SMArt Control of wastewater systems, Deliverat'>le 7: Report on Measures and Algorithins for Risk and Situation Assessment. Editor: UBlMietek A Brdys. EU project Smart Control of Wastewater Systems - SMAC, EVK)CT -2000-00056. Wierzbicki. A.P .. M. Makowski and 1. Wessc\s. Eds (2000) . Model-based Decision Support .\/ethodology with El1vironmental Applications. Series: Mathematical Modelling and Applicatiol1s. Kluwer Academic Publishers, Dordrecht.
ACKNOWLEDGMENT This work was supported by the European Commission under contract number EVK I-CT -200000056 SMAC and by the Polish State Conul1ittee for Scientific Research under grants No . 8TlIA-021-18 and No. 8TlIA-009-24. The authors wish to express their thanks for the support. REFERENCES Brdys, M. A, T. Chang and K. Duzinkiewicz (200 I a). Ir:telligent model predictive control of chlorine residuals in water distribution systems. Proc. of the 4th ASCE Annual Water Distribution Systems Analysis, 200 I World Water and Environmental Resources Congress. Orlando. May 20-24, 2001. Brdys, M A and T. Chang (200Ib) Robust Model Predictive Control of Chlorine Residuals in Water Systems Based on a State Space Modelling. In: B. Ulanicki, B. Coulbeck and J Rance, Eds . Water Software Systems: theO/yand applications, Research Studies Press Ltd , Baldock, Hertfordshire, England. Vol I, pp.HI245. Brdys. M. A , M. Grochowski, K. Duzinkiewicz. and Y. Liu (2002a) . On line risk assessment for control: methodology, algoritluns and applications to wastewater systems. I International Conference on Technology , Automation and Control of Wastewater and Drinking Water Systems-TiASWiK'02, GdanskSobieszewo, June 19-21 , 2002 - Poland Brdys, M. A , M Grochowski, K. Duzinkiewicz and W. Chotkowski, (2002b) Design of control structure for integrated wastewater treatment
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