Modelling and Optimal Control of a Sewer Network

Transcription

1 Modelling and Optimal Control of a Sewer Network C.1 Hydraulic application ( ) Georges Schutz, David Fiorelli, Stefanie Seiffert, Mario Regneri, Kai Klepiszewski

2 Overview Case study The system that is analyzed and controlled Modelling and calibration Use of different model types What are the models Model calibration & comparison Model predictive control MPC Principals Objectives of MPC underlying optimization Results based on simulation Implementation issues Conclusions 09/06/12 Modelling and Optimal Control of a Sewer Network 2

3 The studied sewer system Artificial Lake, mainly used for drinking water Combined sewer network to drain to central WWTP 24 controllable CSOTs 8 are in operation now Mix of pressured and free flow pipes Summer 2010 Population equivalents Impervious surface ha Flébour Bavigne Baschleiden Buurgfried Nothum Liefrange Mecher Haute-Sûre storage lake Insenborn Kaundorf Lultzhausen concrete dam Esch/Sauer Nocher- Route Büderscheid Schlirbach Goesdorf Dahl WWTP Nocher Heischtergronn Bockholtzmühle Tadlermühle Tadler River Sûre Ringel CSOT 8 Eschdorf Heiderscheid Total CSOT volume 1600 m 3 PS 4 Total transport sewer length Pressure conduits 19.7 km 4.1 km Boulaide River Sûre 2000 m Kuborn Hiereck Pumping station Combined sewage overflow tank Sewer Sewer system in operation 2010 Subcatchment / Village Catchment border 09/06/12 Modelling and Optimal Control of a Sewer Network 3

4 Modelling and calibration Use of different model types Time-line Project planing Realization Phase n Phase 2 Phase 1 Accompanying research project Network operation MPC application MPC pilot Hydrodynamic model Simple time delayed model Hydrological model Hydrodynamic model Planing, case studies, network analysis In the context of the research project and the project planning Creates data for the calibration of the other models (offline virtual reality) Simple time-delayed model Used inside to the model predictive control Implemented in Matlab for offline simulations and Python for the MPC pilot implementation Hydrological model Used for long time simulations and as virtual reality for testing the MPC approach Integrated Control Sewer & WWTP (PhD) 09/06/12 Modelling and Optimal Control of a Sewer Network 4

5 Modelling and calibration Flow time and profile test Q G Q out_n Test design and properties dry weather situation Impounding of the dry weather flow in CSOTs Release of the throttled volumes in a given temporal sequence for each CSOT Test goal Allocation of the Qin WWTP pattern to Qout CSOT,i identification of individual flow times from each CSOT to the WWTP Validation of the hydrodynamic model Evaluation of the other models Q Q out_g Q out_k K Qout_W t t WWTP h Q Δh K Q G N N Temporal Measurement t t Q in_w 09/06/12 Modelling and Optimal Control of a Sewer Network 5

6 Modelling and calibration Result and discussion Hydrodynamic model fits quite good to the monitored flows NL hydrological model much less detailed compared to the HD- Model but is able to reproduced most of the dynamics. Translation Model gets the timing of of the waves good enough for the use in the MPC context. 09/06/12 Modelling and Optimal Control of a Sewer Network 6

7 Model predictive control used principals System Measurements Historic data Controllable aggregates Objectives Optimiser Q A (t) = Qout_1(t-12) + Qout_2(t-8) Q B (t) = Q A (t-7) Q C (t) = Q B (t-9) Q C (t) = Qout_1(t-28) + Qout_2(t-24) Objectif Optimizer Input forecasting Model based output prediction Objective-function Optimization over prediction horizon Control loop Apply control for u(k+1) Run the optimization in real time (Δ t) Update to current system state Commands past k future Q out_g Q out_k Predicted Output y m Predicted control inputs u control horizon System Inside MPC k+h c prediction horizon Q out_n r(t) u (t) y (t) WWTP Measurements k+h p Output 09/06/12 Modelling and Optimal Control of a Sewer Network 7

8 Model predictive control for the proposed sewer systems Multi-Objective function t+ Hp minimize J= λ φ 1 (n)+βφ 2 (n)+αφ 3 (n) n= t subject to c i (x)=0 i E c i (x) 0 i I Homogenous distribution of the storage N φ 1 (n)= [V i (n) i=1 Constant inflow to the WWTP N i=1 V i max V i max N V i (n)]2 i=1 N * φ 2 (n)=[ y ref (n) Out i (n d i )]2 i =1 Minimum overflow N φ 3 (n)= i=1 [ Ov i (n ) NL] 2 09/06/12 Modelling and Optimal Control of a Sewer Network 8

9 Model predictive control some results C S O T # 2 5 C S O T # C S O T # T i m e ( h ) T i m e ( h ) T i m e ( h ) o u t f l o w ( m 3 / 1 0 m i n ) n o r m a l i z e d v o l u m e n o r m a l i z e d o v e r f l o w m a x i m u m o u t f l o w ( m 3 / 1 0 m i n ) m a x i m u m n o r m a l i z e d v o l u m e h i g h e s t n o r m a l i z e d o v e r f l o w R a i n I n t e n s i t y ( m m / h ) R a i n i n t e n s i t y a n d s e w e r o u f l o w t o t h e W W T P M a x R e f T i m e ( h ) F l o w ( m 3 / 1 0 m i n ) 09/06/12 Modelling and Optimal Control of a Sewer Network 9

10 Model predictive control some results Overflow [m 3 ] Ref Reduction of overflow volume... Compared to a static control -5% -12% -5% -9% -5% -19% physically possible 0% 20% 80% Static Static -24% Theoretic Theoretical GPC forecasting sensitivity GPC weighting sensitivity GPC Qwwtp_ref sensitivity GPC (weighting sensitivity) GPC (forecasting sensitivity) GPC (Qwwtp_REF sensitivity) 100% 09/06/12 10

11 Implementation Local PLC1 Control RT-Bus PLS OPC In 1 PLS SCADA centralize all network variables Via real time bus User and Admin interface for the GPC Out 1 OPC interface Industrial standard Exists for most SCADA hardware Software libraries available in FOSS Global Predictive Control implementation Matlab Python End-User system feedback / training / issues Fall-back strategies V 1 P 1 P x Local Controls In i Out i PLCi V i P i OPC Server RT-Bus OPC α Minimize overflow Global predictive Control CRP PC Weighted sub-goals β Constant WWTPinflow λ Homogeneous storage use Convex Optimization 09/06/12 Modelling and Optimal Control of a Sewer Network 11

12 Conclusions Different models used in different phases of the project Virtual reality important to demonstrate the possible gains of the GPC approach important to analyze different control approaches validate the effective gains of the controlled system after implementation Simple sewer model (time delayed / plug flow) linear, robust model calibration towards the real network affordable convex optimization Further works Integrate a global quality component in the simple model Handling structural network modifications in the GPC approach 09/06/12 Modelling and Optimal Control of a Sewer Network 12