Event-­‐based  Modelling  Suppor5ng  Model  Predic5ve  Control  of  Chemical  

Dosing  in  Sewer  Networks  Yiqi  Liu;  Ramon  Ganigué  and  Zhiguo  Yuan      

Background Production of sulfide is a major concern in sewer network systems.

3.

2.

1. Pipe corrosion

Odour nuisance

Health hazards

Oxygen, nitrate, iron salts, alkali,…….

Complexity for Control

Approac:Event-based MPC

A predictive model 1

Objective function and control formulation 2

A predictive model 1

Coordination in order to identify our “elephant”

Pipe 1

Pipe 2

Pipe 3

Pipe 4

Pump 1

Pump 2

Pipe 1 Pipe 2 Pipe 3 Pipe 4 Pump 1 Pump 2

Pumpstation

1

Pumpstation

2

Pipe 1 Pipe 2

Pipe 3

Pipe 4

(a) A simple sewer network example (b) sewer network structure matrix

Chemicaldosage

Dilution

(Start)

Splitting

A predictive model 1

A predictive model 1

Pumpstation 2

Pumpstation 3

Pumpstation 1

T0: PS2 (ON, 1), PS3 (OFF,0)

T1: PS2 (ON, 1), PS3 (OFF,1)

T2: PS2 (ON, 0), PS3 (OFF,1)

Tank 1 Tank 2 Tank 3 Tank 4

Subjective to:

Objective function and control formulation 2

Chemical dosage

Case study-Tugun Elanora sewer network

q  Sewer network was modelled using SeweX model, which is a dynamic mathematical model for the simulation of physical, chemical and biological processes in sewer systems (Sharma et al. 2008).

q  -Mg(OH)2- is often dosed in sewer systems to increase the pH of the wastewater and reduce the transfer of hydrogen sulfide from sewage to sewer air, hence preventing corrosion and health problems

q  At pH 7.0, the percentage of hydrogen sulfide, the volatile fraction of dissolved sulfide, is approximately 50% of the total dissolved sulfide, whereas at pH 9.0 this value is reduced to less than 1% due to the shift of the sulfide equilibrium (Gutierrez et al. 2009). In this light, a suitable Mg(OH)2 dosing control should aim to keep sewage pH at 9.0. Lower pH values would lead to sulfide-related problems, while higher pH values would not provide any further improvement to the control, but would increase dosing costs.

Three scenarios: Scenario A: upper limit 15%, lower limit 10% Scenario B: upper limit 25%, lower limit 10% EMPC: Event-based model predictive control

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00010

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 001000 .5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00.5000

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 000000 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 00000

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 000000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 00000

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 000000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 00000

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 000000 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 00000

0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 000000 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 00000

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 000000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00001

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 001000 0 .5 0 0 0 0 0 0 .5 0 0 0 0 0 0 0 0 0 00000

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 000000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 00000

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 000000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 00000

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 00000

Pipe Pumpstation

5 6 7 8 9 10 11 12 13 144321

Pipe

Pumpstation

1

23

45

67

89

1011

1213

14BT

B29C1

B16B21

B8B17B25

BT B29 C1 B16 B21 B8 B17 B25

Results - Network state model

Results - Influent prediction and pump events prediction

Results - pH and H2S production

Thanks to Zhiguo Yuan, Ramon Ganigué and Keshab Sharma

Sewer group