Monitoring and control of openchannel flow in irrigation canals

Transcription

1 Monitoring and control of openchannel flow in irrigation canals Xavier Litrico Cemagref, UMR G-EAU, Montpellier Outline Presentation of Cemagref and UMR G-EAU Introduction Research projects: Gignac canal Control of algae in irrigation canals Simulation model Conclusion References 2

2 Public research institute for applied research in Agricultural and Environmental engineering 95 permanent positions 465 scientists and engineers 9 centers in France 3 Joint research unit (Cemagref, IRD, CIRAD, AgroParisTech, Montpellier SupAgro) located in Montpellier 45 researchers + 35 assistants + 45 PhD students 3 research groups: Water flow modeling and control Socio-economic and political issues of water management Water management in irrigated catchments and technical aspects of irrigation 4

3 UMR G-EAU Team «Modeling and control of water flow in canals and rivers» 5 The team: 9 researchers, 4 PhD students, 3 research engineers Research topics: Open-channel hydraulics Models for open-channel flow Data assimilation Modeling of hydraulic structures Modeling of sediment/algae transport Water Management Definition of management rules for reservoirs Controller design (theoretical and practical approaches Hydrology Rainfall-runoff relationship for real-time control 5 Research Projects 6 Real-time monitoring and control: Adour river (CACG) Gignac canal Algae in canals (Algequeau-ANR project) Rhône river (CNR) Flood prediction (SCHAPI) Management of large dams, global change scenarios and water management Niger river Sénégal river Seine river 6

4 Introduction Open-channels provide water for 4% of the world agricultural production: irrigated crops Drinking Water production purification plants Industries With increasing requirements for quality of service: Guaranteed flow distribution Respect of sanitary norms Real-time monitoring and control 7 Irrigation Canal 8

5 Modeling and Control of Canals and Rivers Modeling Control Saint-Venant (SIC) Simplified Models System Controller Manual Operation PID, Int. Mod. H, H 2, l 9 Main irrigation canal Series of canal pools Distributed system Nonlinear dynamics Multiple inputs-multiple outputs Control objectives: Water level control Flow control Various control architectures

6 Gignac Canal Gignac canal Montpellier Herault river Mediterranean sea 5 Kilometers A medium size irrigation canal Primary network 5 km Secondary network 27 km Nominal discharge 3.5 m 3 /s 2 8 ha irrigated 9 % surface irrigation, % pipe distribution Upstream control No inline volume available Low water efficiency Global = 7% Peak = 25% ASA : Canal manager Feeder canal : 8 km Right bank : 5 km GIGNAC Left bank : 27 km Secondary distribution channels : ~27 km 2

7 Gignac Canal 3 Significant hydraulic structures 4

8 Water distribution Open-channel distribution Pressurized Urban areas distribution 5 The Gignac canal : an old system 6

9 The Gignac canal : in a modernisation process 7 Gignac Canal experimental station Equipments ~ Sensors ~ Motorized actuators Radio network Pluviometers SCADA system Research interests Hydraulic modeling Automatic control Supervision Forecast 8

10 Equipement for scientific purposes : level and velocity sensors 9 Equipement for scientific purposes : gate motorisation 2

11 Equipement for scientific purposes : communication system 2 Equipement for scientific purposes : SCADA system 22

12 Practical results: online controller tuning Operational tools : SIC software Control modules SIC - SCADA link for real-time automatic control Water level (cm) Test du relais rejet de perturbation y suivi de consigne y y c Time (min) Automatic tuning of PI controllers Gate opening (cm) Time (min) u 23 Dealing with real data Automatic control of irrigation canals improve water efficiency and distribution. Needs a monitoring system to achieve sensor and actuator Fault Detection and Isolation (FDI) 24

13 Data reconciliation ) Apply Static Data Reconciliation (SDR) at regulation gates where the available models are static. 2) Apply Dynamic Data Reconciliation (DDR) along the pool where the model is dynamic. Gate Sensor y Sensor y2 Sensor y3 Gate2 Sensor y4 q Pool of canal SDR w Sensor w DDR Figure : Scheme of a pool w2 q 2 Sensor w2 25 Data reconciliation Inputs System Model outputs Sensors Measurements DR Coherent data Objective ) All state variables are measured (Validation+ FDI) 2) Some missing data (Validation +FDI +Data Reconstruction) Data reconciliation requirements ) Measurement statistical characteristics. 2) Process Model. 3) Statistical tests 26

14 2) Process Model Static model: MX = R Static Reconciliation = Optimization problem with static constraints Find such that: min X 2 MXˆ = Xˆ X R m Xˆ ˆ 2 V Dynamic model: k: time index X k + = AX k Dynamic Reconciliation = Optimization problem with dynamic constraints = Kalman filter Xˆ Find such that: N min X 2 k= Xˆ = AXˆ k+ Xm k k Xˆ ˆ k 2 V 27 Application at a regulation gate 4 sensors: Upstream water level sensor : y Downstream water level sensor y 2 Gate position sensor w Velocity sensor v ( y y w v ) T Xm = m 2m m m Sensor y Sensor w Gate Sensor y2 v q w Sensor v Schematic of the application site 3 discharge models: Gate equation : q Stage discharge rating curve: Velocity model: q ( k) = kww( k) + ky( k) k2 y2 ( k) q ( k ) k y 2 ( k ) v + ct = ct ( k) = k y2( k) k v( k) cv y + v 28

15 Global Test Models: q q q ( k) = kww( k) + k y( k) k2 y2( k) v + ( k) k y2( k) ct = ct ( k) = k y2( k) k v( k) cv y + Global residuals: v M X = R M 2X = R 2 r G = MX m r = M2Xm R2 R G Z Test: r = r G V r? > threshold r = 2 r b 2 V r 2? > threshold 29 Result : Validation of measurements 4 Residues --- r r r r Thresholds days Figure : Residuals in the no-faults case 3

16 Result 2: Fault detection Simulation of a bias on the sensor y Residues --- r --- r2 r r2 Thresholds Detection days Figure : Residuals in the biased case 3 Result 3: Detection of a model drift Residues r --- r r2 --- r2 2-2 Drift in the stage-discharge rating curve model days Figure : Residuals in the model drift case Due to aquatic weeds and algae development 32

17 Beyond quantitative water management: impacts on water quality Algae: physical nuisance Chemical nuisance - Copper sulfate used for treatment - Cyanobacteries Consequence: increased exploitation costs, impact on water quality (turbidity increase) Flushing flows to reduce the algal cover 33 Simulation of control strategies Hydrodynamics Simulation of flow velocities and water depths in the network (D modeling) Algal biomass modeling colonisation, growth - detachment, cutting of filaments Transport algae-solute Hydro-biochemical model of open-channels Simulation of the daily functioning, of flushing flows, Evaluation of the effect on the algal biomass and turbidity Algorithms for hydraulic regulation 34

18 Evolution of epilithic biomass Growth rate: Q m A:.5 m/s B:.25 m/s C:.5 m/s D:. m/s A 5% B 25% C 5 D % % μ ( T T ) () t = μ θ I I I( t) e I N lim N lim + K N lim B( t) Bmax 35 Detachment modeling 36

19 Application: Data collection Indirect measurement : Turbidity Drift algae ( cells/m3) Turbidity (NTU) Portable turbidity sensor Fixed turbidity sensor 37 Application: flush analysis Depletion effect on the fixed algae stock 3,5 25 N/m 2 - m 3 /s 2,5,5 NTU 5,5 5 Hours 3 5 Flush ( s / Discharge at Head (m 3 ( 2 Mean bottom shear stress at G5 (N/m ( NTU ) Turbidity at G5 38

20 Application: Results Turbidity G5 (NTU) Turbidity G7 (NTU) Turbidity VD (NTU) flush 5 Hours 3 Bimont: km G5: 9 km G7: 22.5 km Vallon Dol tank: 3 km calibration: variable initial fixed biomass B (R) =.9 kg DW/m 2 B (R2) =.2 kg DW/m 2 B (R3) =.7 kg DW/m 2 ( 4-5h ) α = 5 s β = Turbidity : Simulation Measurement 39 Conclusions Real-time monitoring and control of open-channel flow Increases hydraulic efficiency Reduces the impact of low flows Management tool Management of water quality Algal growth Detachment and tranport-dispersion model Design and test of flushing flows to manage the population of algae Research results are integrated in a simulation model SIC (Simulation of Irrigation Canals), used by researchers and private companies 4

21 References More details and applications in the book «Modeling and control of hydrosystems», Springer, Automatic tuning of PI controllers y y y c Water level (cm) relay experiment 74 offtake opened offtake closed Gate opening (cm) Time (min) Time (min) u 42