Intelligent Combustion Optimisation solution for coal fired power plants

Raja De

STEAG Powitec GmbHPowitec Intelligent Technologies GmbH is technology leader in developing andimplementing intelligent advanced control systems for power plant and otherprocess industries.

In 2012, STEAG Energy Services acquired the company Powitec IntelligentTechnologies GmbH. Since then, the new subsidiary has been operating under thename STEAG Powitec GmbH.

The STEAG Powitec optimization approach consists of a software-based processcontrol in closed control loop configuration with fully automatic and self-learning process optimization features.

• Customer targets• Combustion effects• Approach for close loop optimization• CO system „PIT Navigator“• Case study• Targets• Design of CO Solution• Project execution• Performance Test• Results

Contents

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Customers Targets

Generation companies has the major thrust of improving

Boiler efficiency and heat rate. Maintaining global and local CO and NOx,SOx level below certain limits

for meeting the legal emission thresholds. Reduction of average excess O2 Reduction of average flue gas exit temperature, metal temperatures

and maintaining higher temperature homogeneity in the furnace Reduction of SH and RH spray• Decrease operating costs• Higher availability and service life of the plants• Higher profitability of power plants

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Practical problemsUncertainties and poor combustion process happens due to variations occur with respect to:* Fuel quality (calorific value, ash content, volatiles,

degrees of milling fineness, blending)* Fuel flow (non-uniform distribution of the pulverized coal flow)* Air flow disturbances (distribution of combustion air on

the individual burner and across boiler)* Inadequate DCS close control loop tuning.

But plant operators keep most boiler control loop settings constant and depends on DCS control loops to make action (delayed) to stabilize the process.

Customers Targets

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Poor Combustion Effects

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Fußzeilentext

Approach to Closed Loop Optimization

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Approach to Closed Loop Optimization

Suggested variables to be manipulated:

Auxiliary Air Dampers SPBurner Tilt SPExcess O2 SPMain Steam Temperature SPReheat Steam Temperature SPMain Steam Pressure SPMill loading SP

Which input parameters having more influence in process and can be manipulated?

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PiT Navigator

PIT Navigator SoftwareVibrationsensors (mills)

Toshiba DCS(ModbusTCP)

TOSMAP(OPC DA)

OSI(OPC DA)

OnlineCFD

HMI

Navigator core

Database

Filtering &Preprocessing

Display(local)

Display(remote)

Vibrationsensors (burners)

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PiT-Navigator

Process Interface modules by modbus and OPC serverProcess interface between Powitec's system server and the distributed controlsystem (DCS) of the plant needs to be established. On one hand DCS-process dataneed to be transferred online from the DCS towards Powitec's server. On the otherhand, calculated set-point biases of several manipulated variables need to be writtenback to the DCS.The PiT Navigator consists of several controller modules like mill, sadc, o2etc.Automatic Feature Selection:Automatic feature selector operates on information based measuresdescribing the information content by using a basic filter system and toprovide maximum information content with respect to target channels with aminimum of human expertise.Automatic Process ExplorationIn order to get informative process response in various process conditions,PiT-Stabilizer features automatic tests like step and ramp for manipulatedvariables . This is being done by adding small offsets/biases in set point/output of a control loop.

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Process Modeling and Neural NetsNeural networks are used for creating process models.Neural networks/ models are trained based on historical informative dataset stored in navigator and finds potential areas for optimization and to get solution strategies..Adaptivity of Neural Networks:PiT navigator is adaptive in its control strategy with respect to time varyingprocess conditions..

PiT-Stabilizer

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Powitec Data Base• storing historical process data• training of Neural Networks based on historical data• creation of reports describing the performance of PiT-Stabilzer• system monitoringAdditional features:Each activation or deactivation of the optimization system is executed by a bump-less transfer.Optimization system can be started by operator personnel exclusively.Each self-deactivation of the optimization system causes an alarm in the DCSThe shortest reasonable time period for data transfer is ~1s, typical are ~5sRemote access for online exploration and step testing

PiT-Stabilizer

13Dr. Peter Deeskow

ImplementationVibration sensors (here: burners)

Heat flow analysis from 5 different directions

Fast sampling of vibration (better noise) sensors

Vibration spectrum (frequency domain) instead of intensity used

Result: Distribution of coal feed at each single burner at one mill

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Typical arrangement of sensors

FluxStylus in boiler elevation

1 Vibrasensor per pulv. coal pipe1 per mill1 per classifier

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PiT Navigator

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Case Study - Plant Details

Unit #1commissioned 2010

600 MWel 2060 t/h steam subcritical ,single furnace, single

reheat forced circulation, balance draft coal fired boiler

24 LowNOx Burners Rated coal flow :433.7 t/h

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Boiler and Burner / Air arrangement

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On-site inspectionmillscoal pipes and air pathburnersboilercontrol system

available measurementsavailable actuatorsprocess interfaceautomatic vs. manual operation

Data Mining on available process dataAnalysis of historical DCS/PCS data towards optimization potential with the aid of statistical software and neural nets estimation of controllability of combustion identification of existing „weak“ control loops.

Plant Audit

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Case study

Right and left imbalance of furnace is observed.

Difference in flue gas temperature between right and left at RH outletOxygen difference between right and left at economizer outlet.Main steam and re-heater spray difference between right and left.

Si.No Parameter Right Hand Side Left Hand Side Difference

min max min max min max

1. Oxygen content (%) 2.234 5.829 4.81 7.85 0.178 3.1

2. Re-Heater flue gas inlet temperature(Degree C)

650.87 740.51 624.8 795.05 -35.64 107.85

3. Super Heater Spray(kg/sec) 0 20.13 12.01 24.26 -7.737 21.206

4. Re-Heater Spray(kg/sec) 17.34 26.228 6.84 26.228 -19.33 5.37

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Design of CODeliverables / GuaranteesTypically the PiT Navigator will control the air-/fuel-ratio with following

targets Balancing of Flue Gas Temperatures reduction of average excess O2 Balancing air and fuel flow in furnace. Maintaining certain metal temperatures below given limit

Online CFD required: visualisation of the impact of different coal types, coal flow, secondary air, burner tilt

on combustion visualisation of combustion unbalances

Interfaces required: process interfaces to DCS and Pit Navigator. necessary changes in DCS for set-point biases setup of remote maintenance

Hardware installation: mounting and connecting necessary computer hardware mounting and connecting vibration sensors and cabinets for mills and burners

Operation Range For all manipulated variables to be biased by PiT-Stabilizer there are absolute limits.

These limits are to be decided with detailed discussion with plant operation team.

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DCS -> Optimization system (900 variables)air flows related to the boiler (burner + mills +OFA)coal flows into the millsemissions (furnace + chimney)flue gas temperatures in the furnacemetal temperatures of super heaters and reheaterssteam parameters of boiler (superheaters, reheaters, boiler)burner settingsmill parameters (motor amps, temperatures, pressure, speed, ...)

Optimization system -> DCS (100 nos. biases)coal distribution between millssecondary air distribution between 10 groups feeding three burners eachover fire airclassifier vane of all millsO2 set point

Process interface data transfer

Project Execution:

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Project Execution:

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Project Execution:

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ImplementationBias Monitor

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ImplementationBiasing & Loop tuning

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ImplementationBiasing & Loop tuning

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Plant specific network configuration

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ImplementationProcess exploration

Optimization system should contain a module to carry out full automatic process exploration to aquire process data apart from normal operation following characteristic

curves does not disturb “normal” operation by maintaining production, limits, ... base of process data is expanded by potentially favourable working points

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ImplementationAdvanced Process Control (APC)

A multi dimensional, model based controller is required

The model is not “hand made“, but “teached” from process data, e.g. existing process data (temperatures, volume & mass flows, pressures, ...) coal distribution (see: vibration sensors at burners) coal properties (vibration sensors at mills, ...) targets (reduction of excess O2, reduction of flue gas temperature, ...)

A ready to use model can be used for simulation and exploration What will happen if I would change a single set-point or a group of set-points?

Optimiser uses model to find best possible action plan with regards to targets

Safety layer passes or blocks action plan to be carried out online

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CFD Analysis

SA_BIAS_REF BT_REF

Corner -1 0.0% -21.0

Corner -2 0.0% -21.0

Corner -3 0.0% -21.0

Corner -4 0.0% -21.0

Re-heater Inlet

Furnace Exit

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CFD Analysis

SA Dampers SA BiasTop Elevation FG GH HJ +5.0%

Middle Elevation CD DE EF - 5.0%

Bottom Elevation AB BC -

Total SA flow changed 0.0%

Re-heater Inlet

Furnace Exit

Re-heater Inlet

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Performance TestDistribution of O2 and exit gas temp.

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Performance TestResults for comparable periods

Label ΔO2/% ΔT/°C CO/mg/Nm³I -0,63 -3,09 91II -0,62 -3,67 72III -1,06 -4,28 90

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Performance TestOverall Results

O2 reduced by 0.6% to 0.8% abs.

Flue gas temperature reduced by up to 4°C

Coal Flow decreased by 0.5 t/h/mill = 15.600t/a

IDF/FDF savings = 2700 MWh/a

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• Optimization of temperature distribution over different levels and minimize imbalances.

• Optimize fuel consumption• Reduction O2-excess air• CO2-reduction• CO and NOx reduction • Reduction of Unburned Carbon in Ash• Increased boiler efficiency• Reduction of slagging and soiling at burner mouth and boiler wall.• Increased availability.

Benefits

Thank You

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