Powitec Power en BHEL 2011-11-22
description
Transcript of Powitec Power en BHEL 2011-11-22
Powitec Intelligent Technologies – Germany Page 1
Powitec
ChallengesChallenges
Solution
References
Discussion
Powitec Intelligent Technologies – Germany Page 1
Presentation Powitec
Challenges
Powitec‘s solution
References
Discussion
Advanced Combustion Advanced Combustion
OptimisationOptimisationin Large in Large Scaled ThermalScaled Thermal Power PlantsPower Plants
process optimized. value delivered.
Powitec Intelligent Technologies – Germany Page 2
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� Founded in 2001
� Powitec offers optimisation solutions for:- Cement and Lime Industry (rotary kilns)- Fossil fired Power Plants- Waste-to-Energy Combustion Plants
� 45 employees experienced in complex industrial combustion processes
� Expertise in Neural Network & Digital Image Processing
� Partnership with Technical University Ilmenau, Germany FACULTY OF COMPUTER SCIENCE AND AUTOMATION: Neuroinformatics and Cognitive Robotics Lab
� Honoured in 2010 by Federal Minister of Environment with German Innovation Award Climate and Environment for Outstanding and Sustainable Technology
� More than 100 references: E.ON, Vattenfall, Lafarge, BuzziUnicem, Holcim, HeidelbergCement, Cemex, Dyckerhoff, CRH, Carmeuse, Cimpor, Titan, Lhoist in Europe, Africa, Asia and North America
Powitec Intelligent Technologies GmbHProcess Optimisation with Intelligent Technologies
Essen
Ilmenau
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Discussion
Dr. Röttgen: German Environment Minister, F. Wintrich: Technical Director Powitec, B. Beyer: Commercial Director Powitec
Dr. Schnappauf: CEO BDI, Prof. Töpfer: Former German Environment Minister and Director UN Environmental Program
German Innovation Award German Innovation Award
Climate and Environment Climate and Environment
for Outstanding and Sustainable Technologyfor Outstanding and Sustainable Technology
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Online-flame- and boiler wall charac-
teristics by using different sensors+ automatic mutual information generation
PowitecPowitec‘‘s modular solutions modular solution
Flame and Boiler AnalysisFlame and Boiler AnalysisOnline- mill- and coal- characteristics
by frequency analysis+ automatic mutual information generation
Coal System AnalysisCoal System Analysis
Adaptive validation and analysis with innovative neural nets of all sensors
and process data describing the process.
Multi-Correlation (MiMo, Cross…)
Online-CFD
Open-Loop-Analysis and -Indication
AutoAuto--Analyse Sensor and Process DataAnalyse Sensor and Process Data
Self learning adaptive boiler optimisation with innovative neural nets
=> active learning to permanently optimize the air-/fuel-ratio
Closed-Loop-Control
AutoAuto--Optimise the ProcessOptimise the Process
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Do you need ClosedDo you need Closed--Loop boiler control?Loop boiler control?
• Does every operator/every shift run the mill-burner-boiler
–system the same way?
• Are experienced, intuitive operators readily available these days?
• Do your operators try to find the best available settings
and trims every 1-5 minutes?
• Is the mill-burner-boiler –system capacity permanently
and completely utilised at its technical optimum?
• When things are fixed or adjusted in your plant do they
stay fixed and adjusted?
If you answered YES to all, you don`t need closed-loop boiler control...
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ClosedClosed--Loop ControlLoop Control --> PiT Navigator> PiT Navigator
• Continuous on-line data (signals) from the process
via DCS,
• the possibility to influence the process via automated
actuators,
• a complex multi-dimensional process with significant
reaction times,
and controls this process
to a new optimum.
Auto Optimizer “PiT Navigator” is based upon
PiT Navigator is applicable for the opti-misation of stand-alone single aggre-
gates as well as for an overall, integrated process optimisation approach!
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ClosedClosed--Loop ControlLoop Control --> PiT Navigator> PiT Navigator
• extensively uses existing process data,
• automatically selects and extracts features by relevance ranking,
• automatically generates process models (process description)
• uses the process models for optimising,
• and sends set-point corrections to the existing control system.
Auto-Optimization Solution PiT Navigator
=> Intelligent software for a continuous, optimized control
Thus PiT Navigator is• a Multi dimensional optimizer (like a group of experts),
• generically learning (like a human),
• and adapting automatically to changing process situations (like a human).
This benefits in
• stabilized and optimized process operation,
• increased production at decreased specific energy,
• fully automated operation without the need for human interventions.
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N: Non-Linear
M: Model Computer models describing the process
P: PredictiveDecisive process results (NOx, CO, FCaO…) predicted
C: Control Permanent (24/7) Closed Loop Control
NMPC NMPC –– NonNon--Linear Model Predictive ControlLinear Model Predictive Control
Linear Non-Linearvs.
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PiT Navigator: PiT Navigator: Advantages (1)Advantages (1)
• SELF-LEARNING: Mathematic statistical models do not rely
on expert knowledge; they learn from existing process data automatically
• ADAPTIVE: PiT Navigator process models learn
continuously by themselves to extend the knowledge from new process situations
• PERMANENT: Models are self-optimising 24 hours 7
days a week, even at already good, but still improvable process situations
• FLEXIBLE: Easy changes in optimisation targets
without reprogramming orre-parameterisation n of software
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PiT Navigator: Advantages (2)
• FAST: Commissioning of NMPC software within 2-3
weeks on-site and with 5-10 man days customer involvement only
• COMPLETE: Vibration information and optical information
are analysed as well as non-linear correlations between all process data
• STRAIGHTFORWARD: Total cost of ownership is low, as no
permanent manual adaptation and re-programming is required: Self-training!
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Powitec Patents:Powitec Patents:
80 Intern
ational P
atents
15 patents in applic
ation phase
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Data acquisition from Process Control SystemData acquisition from Process Control System
Fuel amount, lab values
Feederamount, speed, height
Flue gas recirculation (mills)amount, pressure, temp.
mills (1 to n)
momentum, temperatures, amount
etc.
Mill airsamount, pressure, temp.
Burners (1 to n)
flap pos., flame detectors
De-Asher (boiler-/fly-/filter-ashes)
Amount, speed, height
AshAmount, lab values
Steam/fresh wateramount, pressure, temp.
Flue gasafter combustion chamber before/after heating
surfaces, amount, pressure, temp.,
CO, O2, NOx, SO2
Induced drought fanamount, pressure, temp., Delta p, Pel
Flue gas recirculation (boiler)amount, pressure, temp., Delta p, Pel
Heating surfacesBoiler wall
CO, O2, NOx,
Burn out airsamount, pressure, temp., flap pos.
Burner airsamount, pressure, temp., flap pos.
Forced draft fanamount, pressure, temp.,
delta p, Pel, flap pos.
Airamount, temperature, moisture
If data is available !!!
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Process Optimisation systems: StatusProcess Optimisation systems: Status
Expert Systems Operator
Process
Interlocking
Actuating variablesMeasurements
Characteristics curves
Controller
PIDControl
System
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Advanced Combustion ControlAdvanced Combustion Control
� Intensive usage of existing process data
�Automatic feature selection and extraction (relevance ranking)
�Automatic model generation (regression, neuronal networks, probabilistic nets, Gray-Box-Models)
�Use of the process models for optimising
�Set point integration into the DCS/PCS as correction values
(old system stays unchanged and ready for operation)
Expert System/ Database
Operator
Process
Interlocking
ActuatorsMeasure-ments
Characteristics curves
Controller
PID –controller
Relevance ranking
Process models +controller models
Optimiser
Data
Correctio
n
Management
Targ
ets
Control System
Data
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1Proleit
2Oracle 9i
1Oracle 10g
1XTC
2Teleperm XP4RK 512 TCP
5Teleperm M7RK 512
1Symphony/Maestro19Profibus DP
3S5 Redundant1PMSX
1S51
OPC DA
Redundant
1Procontrol P40OPC DA
2Procontrol1Emerson14Modbus RTU
26PCS 741Siemens0Modbus +
1Infi 901KH-Automation6etmix
1Delta V5FLS1Analog
1AC 870P17ABB11Adlink
DCS TypeDCS MakerProcess
Connection
Type & amount of realised Process Connections
(as of June 2010)
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Realised process connectionsRealised process connections
Procontrol P
Teleperm XP
Teleperm M
800xA
Symphony Maestro
FLS
PCS 7
Profibus DPMaster
CIF10 / CP341 Modbus Master
87TP01 87TS01Modbus Master
CM104
CP 441RK512
Intellution Inc. OPC Data Access 2.0
Server for iFix
AC870P
S7
S5
Contronic S
PowitecSystem Server
Analog
PMSX
HilscherCIF50DPS
Modbus RTU/TCP
Client
RK512 3964rRK512 TCP
Analog
OPC DA 2.05aClient
PMSX Client
PMSX pro
Ethernet
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Integration of the set-point correction into the DCS (example)
Communication Interface between DCS
and PiT Navigator
Detailed process information (instrument
signals and DCS channels) with high quality
and in time.
The following interface options could be
provided by Powitec:
Direct link via RK512/3964R protocol
The process signals from the DCS, SCADA
or plant control system are connected via a
communication processor (installed by the
customer) to the PiT Navigator system. No
additional cost.
File exchange / Profibus / Modbus RTU
The process signals from the DCS, SCADA
or plant control system are cyclically written
into a file that contains finally the valid
information at a defined point in time. This
e.g. is possible with a OPC server with
scripting function. No additional cost.
OPC client
The PiT Navigator will be added by an
interface that is linked as an OPC client to an
existing OPC server. Additional costs 2.000
EUR (OPC client software licence).
DCS
combustioncontrolsystem
+
PiT System-Server
X
Profibus DP Master(e.g. Siemens S7 416DP)
Profibus Client(Hilscher CIF 50 DPS)
PID
) (
F
security layere.g. min/max airflow
air pipe
PiT Navigator
input:- setpoint airflow- actual airflow
output:- delta setpoint airflow
delta airflow
Profibus DP
setpoint airflow
actual airflow
t
1Powitec online
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Tools for Process OptimizingTools for Process Optimizing
Relevancy / Sensitivity analysis:
� Which variables interact?Approach: � Data driven, no expert knowledge necessary
� Calculation of information content between variables� Maximisation of information content of extracted features:
feature
extractor
target
feature
DCS
mutual
information
Sensors
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Tools for Process OptimizingTools for Process Optimizing
Automatic feature selection:
� Data driven, no expert knowledge necessary
� Calculation of the information content between target and measured variables
� Calculation of the redundancy between measured variables
� Elimination of redundant or not informative variables
x x'
Selection
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Tools for Process OptimizingTools for Process Optimizing
Process modelling:
� Usage of existing process data + new sensor data
� statistic data driven process description
Approach:
� Neuronal Networks
� Probabilistic Models
� Gray-Box-Models
Application:
� Model represents the quantitative relations between all relevant values variable x
variable yvar
iable
p
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Tools for Process OptimizingTools for Process Optimizing
Probabilistic Models
� description with probability distribution
� explicit modelling of fuzziness
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Working on 3 different time scales
• Short term scale
– Control of short termed process changes like CO, NOx
– Rule based & PID …
• Middle term scale
– Predictive control of process values with high dead times like kiln torque, free lime etc.
– Neural Net Based
– Controlling on basis of current and predicted values
• Long term scale
– Following the long term targets like production increase, energy efficiency
– According to scoring
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Unique features
� Distribution of the reward to the single agents in a multi-agent system
� Including expert knowledge in a learning system without constraining the learning process
� Applicability of each learning system on different applications
� Correct behaviour in critic states of a plant
+
Reward
If case x and control y
� bad result
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Overview of current controller used by Powitec:
PID„classic“ Controllers:� PID- Controller� Rule-Based-Controller
Self learning Controller:� neuronal Controller:
� Pnav� CoSyNE� ADHDP ( Action Dependent Heuristic Dynamic Programming )
� Bayes System� Bayes-Controller
� Reinforcement learning� Bayesian Fitted Q-Iteration
� Mixed Controller� Neural Fitted Q-Iteration
Finite Elements Model
R?
+
+
Reward
Reward
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Tools for Process OptimizingTools for Process Optimizing
Optimisation:
� Process model reflect quantitative relationships between
all process targets and parameters
� „inquiring“ the process model about the optimal setting
Possible optimisation potentials:
� Temperature balance
� Emissions (CIA, NOx, CO)
� Efficiency
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Generic Modelling
• Self learning
• Transferable: Not depending on a specific process
• Structural designed with different decision and knowledge levels (like companies)
• Emergence: New properties or structures following the interplay of its elements
• Integration of Linear + Fuzzy + PiD + AI
• Feature generation instead of single data storage
…to achieve that the
• PLS receives a “human-like” brain
• same programmed structure is usable in a large variety of plants and processes
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Strong changes in coal quality?
• Powitec recommends installation of
PiT VibraSensors
• Vibration Sensors at mill, classifier and PF-pipes
gather additional information
• From automatic feature extraction information
about the current coal cluster
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PiT Vibrasensor
• 1-2 sensors per mill, 1 per classifier, 1 per pipe
• Piezo-ceramic sensor for
industrial use following military specification
• Positioning decided following trials,
fixed by magnets at locations <160°C
• Sampling with 10kHz, 16bit
• Data processing by analysis of spectral
characteristics (concentrations), statistic
verification and analysis of correlations
(Mutual Information Optimisation)
• Single server for acoustic analysis
Online vibration analysisOnline vibration analysis
Millcasing
Sensor position
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Online Prediction of Coal Quality
Volatiles
Water
Ash
Heating Value
COAL MODEL
ADAPTIVITY
Process Data
Lab. Data (offline) via:- PHUMI- or DB-query
Model Retrainingand Testing
OnlinePrediction
PROCESSNAVIGATOR
Adaptive Coal Modelsare used as “Soft-Sensors”to provide additional online information for the PiT Navigator
Sensor Data
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Online Coalmodel WATER (Hard Coal, Fenne)red=Prediction, green=Train Data, blue=Test Data. (all in %)
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Online Coal Model VOLATILES
(Hard Coal, MKV Fenne)
red=Prediction, green=Train Data, blue=Test Data. (all in %)
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PiT Navigator: Typical Project Steps
1. Analysis of mills, PC pipes, burner, boiler – system2. Analysis of PLC status and connection possibilities3. Common definition of interface, targets, actuating variables ramps,
borders, alarming4. Data:
– Reading connection to PLC
– Data validation and pre-processing
– Data analysis towards correlations, relevancy analysis, informationcontent, feature extraction
– PID-Controller and PLC controllers and characteristic curves analysis
– Online-validation of existing measurements
– PLC-Programming for set-point correction acceptance (3rd party)
– Writing connection to PLC
5. Common definition of PiT Navigator surfaces and reports6. Software training, fine tuning and monitoring7. Common analysis of suggested changes8. Activation – analysis of results (may be fine-tuning)9. Performance test – result evaluation10. PiT Navigator in continuous optimising mode
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Some ReferencesSome References
Power plant Burner installation
burners/mills
MWel Results with PiT Navigator
KomipoSeocheon TPP
Slag tap fired boilers
202 x 215
Boiler 1 and 2Unburned Carbon in Ash -0,8% abs. Unburned Carbon in Ash -1,2% abs.
EvonikKW Fenne
Shifted boxer 8 / 4 195
Boiler 1η steam generator: + 0,4% abs
λ: 1,25 to 1,18
Auxiliary cons.: -2000MWh/yUnburned C in Ash -0,2% abs.+ 180.000 €/y
VattenfallKW Tiefstack
Front wall 6 / 32 x 252
Boiler 1 and 2η steam generator: +0,3% absλ: 1,22 to 1,15CO -12%NOx -29mg/Nm³Unburned C in Ash -0,5% abs.+ 230.000 €/y each boiler
E.OnKW Scholven
Shifted front wall
16 / 44 x 400
Boiler CResidual C up to -1% abs.+ 590.000 €/y
20 power boilers equipped
with Powitec
12 power boilers in closed
loop optimization
46 closed loop
applications in total
approx. 210+ optical
sensors in the field
approx. 250+ acoustical
sensors in the field
80+ international patents
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PiT Navigator at Evonik power plant FennePiT Navigator at Evonik power plant Fenne
MKV Fenne, Völklingen,
(Evonik New Energies)210 MWel
Shifted boxer firing with 8 burners on 2 sides
in 2 levels each. PiT Navigator controls the
secondary air distribution per burner and
the amount of sludge, since May 2005.
A Performance Contracting was agreed
between Saarenergie (Evonik) and Powitec!
- All savings of 7 years to be shared!
- Powitec operates the PiT Navigator
(installation, maintenance etc.)
8 PiT Multisensors
8 x secondary air, controlled
4 x mill, controlled
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Burners-Air System
• Fuels: ballast coal, heavy fuel oil, mine gas, coke oven gas
• 8 low-NOx burners in a staggered, opposed arrangementat 4 levels
• air staging is conducted by secondary air 1 and 2, shellair at the boiler walls and over fire air as burn out air
• to achieve a reduced NOx level, a lambda value of 0.8 isset at the burners and overall air index is increased with
shell air and over fire air to 1.25 (full load)
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High speed serverHigh speed server
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Air/Fuel Ratio
• It was found, that the coal dust distribution in the
2 coal ducts to the burners of one level ist
uneven.
• The navigator corrects this inbalance by
increasing the combustion air to the burner with
the greater fuel flow and reducing the air to the
other one
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Actuations on the
secondary air at
the 8 burners.
Parallel, the
amount of sludge
(feed into coal
mills) is controlled.
Control of the burner secondary air during 24 hControl of the burner secondary air during 24 h
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Control of the burner secondary air during 1hControl of the burner secondary air during 1h
Time scale changed to one
hour
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Fenne power plant:Fenne power plant:
PiT Navigator in normal operationPiT Navigator in normal operation
boiler operation = 8760 h
Bo
ile
r lo
ad
[%
]
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O2O2--set point depending from boiler loadset point depending from boiler load
‚Old‘ set
point
Set point with
PiT Navigator
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O2O2--set point depending from boiler loadset point depending from boiler load
with/without PiT Navigatorwith/without PiT Navigator
Steam
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O2 reduction = wall corrosion ?
• A great danger in changing the air/fuel-ratio is the oxygencontent reduction at the boiler walls.
• The reduction can lead to chlorine included high-temperature corrosion.
• The MKV combustion chamber is equipped with 120 measuring nozzles.
• O2 and CO are measured in regular intervals, all 3 month.
• Dirk Kiehn, Production Manager MKV Fenne:
„With the PiT Navigator, the boiler wall atmosphereimproved significantly.“
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O2O2--measurement results on boiler walls measurement results on boiler walls without PiT Navigator (without PiT Navigator (λλ=1,25=1,25))
Linke Seitenwand Vorderwand Rechte Seitenwand Rückwand
Measurement of reducing atmosphere20.01.2004
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O2O2--measurement results on boiler walls measurement results on boiler walls with PiT Navigator (parallel to a reduction of O2 from with PiT Navigator (parallel to a reduction of O2 from λλ=1,25 to =1,25 to λλ=1,18=1,18))
Measurement of reducing atmosphere20.01.2004
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Results 2010 at MKV Fenne:
• Improved boiler efficiency through air reduction 1,022 t Coal/year = 2,794 t CO2 /year
• Reduced auxiliary consumption at FD and ID fans2,268 MWh/year = 775 t Coal/year = 2,220 t CO2/year
• Reduced Unburned Carbon in Ash: 372 t Coal/year = 1.018 t CO2/year
• Reduced limestone consumption: 99 t/year
• Savings 2010 in total: 2.169 t Coal and 5.936 t CO2 ≈ 300.000 €
• Increased plant net efficiency of 0,18 %
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Further results in 2006 to 2010 at MKV FenneFurther results in 2006 to 2010 at MKV Fenne
• Boiler efficiency + 0,4%
• O2 amount - 24%
(from 4,2 to 3,2 % respectively λ 1,25 to 1,18)
• Unburned Carbon in ash - 0,15% (from 4,0 % to 3,85%)
• No increase of CO and NOx
• “Significantly improved boiler
wall atmosphere”
• “No slagging at the burners”
• “Increased availability”
Savings of < 180.000 € per year + additional benefits
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ReferencesReferences
2010: German Innovation
Award
Climate and Environment
for Outstanding and
Sustainable Technology
Extensive experience in
Power Plants
20 Power Plants
equipped with Powitec
12 Power Plants in
Powitec’s closed loop
operation
Sizes range from
195 MWel to 700 MWel
46 closed loop
optimisers world wide