Optimization of the Aeration of Sewage Treatment Plants with Advanced Process Control

17 Pages·2015·1.22 MB·English

Siemens AG, 26.11.2015
Optimization of the Aeration of Sewage Treatment Plants
with Advanced Process Control
Dr. Andreas Pirsing
© Siemens AG 2015 All rights reserved.
Sewage Treatment Plants cause
High Energy Consumption
An increase in energy efficiency of 5% would create a value of approx. 580 Mio. USD
OPEX costs of WWTP Advanced Process Control
Energy costs of approx. 11.6 Mrd. USD p.a. APC is state-of-the-art in several industries, esp.
Chemicals, O&G à standard tools available
Customer Benefits
§ Reduced energy consumption (typically by
3- to 10%) and the utilization of raw materials
§ Increased capacity (typically by 1 to 5%)
while maintaining consistent water quality
§ Reduced number of manual interventions
required by the operator – thus reducing
operator workload and improving operator
efficiency
§ Better understanding of the process through
modeling and prediction
à Transfer of established approach to W&W
Source: GWI, Global WaterMarket, 2015
Page 2 2015-11-25 Dr. Pirsing, Advanced Process Control
© Siemens AG 2015 All rights reserved.
Embedded APC Functions are Part of
Modern Automation Systems
Advanced Process Control (APC) is a broad term for different process control methods designed to
minimize the usage of resources and/or to maximize the profit of a plant
Typical (embedded)
APC functions:
(cid:127) PID Tuning
(cid:127) Override Control
(cid:127) Smith Predictor
(cid:127) Gain Scheduling
(cid:127) Model Predictive
Control
(cid:127) Control Performance
Monitoring
Source: Siemens, SIMATIC PCS 7 catalogue
Page 3 2015-11-25 Dr. Pirsing, Advanced Process Control
© Siemens AG 2015 All rights reserved.
Model Predictive Control: Controller Reacts already
before Deviations Occur
Model Predictive Control is able to detect future deviations
Model
“Internal model control”
i.e. controller contains
complete process model Set point
Internal process model Controlled Prediction of free movement:
identified by recording of variable (CV) Controller detects future
Controller reacts
learning data deviations (e.g. exceeding of
already before
maximum permissible quality
deviation occurs!
Predictive value)
Driving process according Manipulated
to predictions of near variable (MV)
future
Control
k+n k+n
Control problem is formu- c p
past
future
lated as optimization Time t
(measured values)
(predicted values)
problem and solved
online
Actual time k
Page 4 2015-11-25 Dr. Pirsing, Advanced Process Control
© Siemens AG 2015 All rights reserved.
Model Predictive Control: Internal Process Model
Identified by Using Engineering Tools
Linearized Internal Process Model is automatically created based on operating data
Operate plant in constant conditions
Transfer data model to MPC
and wait until steady state is
function block
reached
Excitate all relevant manipulated Implement MPC controller in
variables separately automation system
Verification
Steady State Model Control Loop
Excitation Data Logging Data Model via
Operation Transfer Design
Simulation
Create Internal Process Model with
MPC Configurator
Log all relevant controlled variables
(CV)
Page 5 2015-11-25 Dr. Pirsing, Advanced Process Control
© Siemens AG 2015 All rights reserved.
Project Example:
WWTP using Upstream Denitrification
Pilot plant is located in northern part of Germany: Customer was interested in benchmarking his control
concept in respect to energy efficiency and effluent quality
Project Example:
Large WWTP, 900.000 PE
(cid:127) Private operating
company
(cid:127) Plant with upstream
denitrification
(cid:127) High level of energy
efficiency due to several
EE projects in the past
(cid:127) Simulation model
available, based on
Matlab/Simulink
(cid:127) SIMATIC PCS 7 already
in place
Page 6 2015-11-25 Dr. Pirsing, Advanced Process Control
© Siemens AG 2015 All rights reserved.
Project Example:
WWTP using Upstream Denitrification
Pilot plant is located in northern part of Germany: Customer was interested in benchmarking his control
concept in respect to energy efficiency and effluent quality
Previous Control Concept
Independent control loops for important
controlled variables:
(cid:127) DO concentration: PI controller is used to
manipulate the blower speed
(cid:127) Nitrate concentration: Characteristic Line
controller to manipulate recirculation flow
(cid:127) Ammonium concentration only monitored,
not used for feedback control
Disadvantages/Limitations/Challanges
(cid:127) Influent fluctuations can not be handled
properly
(cid:127) Complex heuristic control logic difficult to
maintain and modify
(cid:127) Further energy/cost savings expected
Page 7 2015-11-25 Dr. Pirsing, Advanced Process Control
© Siemens AG 2015 All rights reserved.
Model Predictive Control: Data Model Identified by
Recording of Learning Data
Comparison of real values and internally modelled values shows good fit although linearized model is
based „only“ on data, example: ammonium concentration
0.8
CV: Ammonium
-1mgL 0.7
C_NH4/ 0.6
NK_B 0.5
0.4
0 5 10 15 20 25 30 35
Zeit/Tagen
8.2
CV: Nitrate
-1O3/mgL 7.88
N
C_ 7.6
NK_B 7.4
7.2
0 5 10 15 20 25 30 35
Zeit/Tagen
2.3
MV: DO
-1mgL 2.2
O2/ 2.1
C1_sp 2
NI_B 1.9
1.8
0 5 10 15 20 25 30 35
Zeit/Tagen
x 105
3.3
MV: Recirculation
3.2
3-1Rezi_sp/mTag 23..913
2.8
2.7
0 5 10 15 20 25 30 35
Zeit/Tagen
x 105
1.45
DV: Influent flow
1.4
RLS 1.35
Q_RV_ 1.12.35
1.2
1.15
0 5 10 15 20 25 30 35
Zeit/Tagen
Page 8 2015-11-25 Dr. Pirsing, Advanced Process Control
© Siemens AG 2015 All rights reserved.
Project Example:
WWTP using Upstream Denitrification
Pilot plant is located in northern part of Germany: Customer was interested in benchmarking his control
concept in respect to energy efficiency and effluent quality
Previous Control Concept New Control Concept, based on MPC
Independent control loops for important Implementation of 2x2x1 MPC controller
controlled variables: (cid:127) New controlled variables (CV):
(cid:127) DO concentration: PI controller is used to - Ammonia
manipulate the blower speed - Nitrate
(cid:127) Nitrate concentration: Characteristic Line (cid:127) Manipulated variables (MV):
controller to manipulate recirculation flow - DO concentration
(cid:127) Ammonium concentration only monitored, - recirculation flow
not used for feedback control (cid:127) Disturbance variable: Influent flow
Previous DO controller and recirculation
Disadvantages/Limitations controller used as slave controllers for cascade
(cid:127) Influent fluctuations can not be handled structure, can be used as back up as well
properly
(cid:127) Complex heuristic control logic difficult to Advantages
maintain and modify (cid:127) Easy implementation of Model Predictive
Control, based on standard function blocks
(cid:127) Early consideration of influent fluctuations,
due to dynamic disturbance compensation
Page 9 2015-11-25 Dr. Pirsing, Advanced Process Control
© Siemens AG 2015 All rights reserved.
Results of Model Predictive Control
Quality Limits are Fulfilled or Increased…
Red: Previous
Effluent Quality control concept
2200
Performance of the plant
kkllaassssiisscchhee RReeggeelluunngg
in respect to effluent 11 Blue: MPC
--LL MMPPCC
quality is increased à gg control concept
mm 1100
quality limits are fulfilled //
44
HH
NN
00
00 5500 110000 115500 220000
ZZeeiitt//TTaaggee
3300
11
Influent --LL 2200
gg
mm
xx 110055
3-13-1mTagmTag 2424 O3/O3/ 1100
Zulauf/Zulauf/ 00 NN
00 5500 110000 115500 220000
ZZeeiitt//TTaaggee 00
3300 00 5500 110000 115500 220000
Temp./°CTemp./°C 12120000 ZZeeiitt//TTaaggee
00
00 5500 110000 115500 220000
ZZeeiitt//TTaaggee
Page 10 2015-11-25 Dr. Pirsing, Advanced Process Control
© Siemens AG 2015 All rights reserved.