Lipinski Workshop on Modelling and Simulation of Coal-fired Power Generation and CCS Process

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Mathematical Modelling and Simulation of

Power Plants and CO2 Capture

The practical use of selected models of power plant objects in various control systems of

pulverized coal-fired drum boilers.

Mariusz Lipiński

Institute ofPower SystemsAutomation Ltd, Wrocław, Poland

University of Warwick

20-21 March 2012

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1. Inctroduction.

2. Thermal power unit models.3. Model of a ring–ball coal mill.

4. The practical use of ring–ball coal mill model signals -detection and prevention system of fire and ignition.

4.1. Identify the loss of coal in the coal mill system.

4.2. Identify ignitions in the coal mill system.

Speech program

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Founded in 1949, the Institute of Power Systems Automation, IASE Ltd is an independent research and development

organization specializing in automated control and monitoring

systems for the electricity industry.We supply reliable system for a whole range of applications,

from single operations to providing total integrated solutions.

1. IASE Ltd. - about us.

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Our service is comprehensive and covers:- process control systems,

- monitoring systems,- power plants and

combined heat and power generating plants,- generation, transmissions and distribution,

- specialist advice and consultancy,- hardware and software,

- design, production and installation,

- staff training.

We provide flexible solutionto meet individual requirements.


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Our fields of activities:

- engineering and design of systems and equipment for power engineering and industrial plants,

- management of deliveries,

- commissioning of systems and equipment in industrialplants,

- providing training courses and maintenance services,- implementing “turn key” projects.

Contact data:

ul. Wystawowa 1

51-618 Wrocław

www.iase.wroc.pl

[email protected] (071) 348 42 21

Fax (071) 348 21 83, 348 50 34

Secretariat (071) 347 72 94


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Models of thermal power unit describe static and dynamic

characteristic of power unit and collaborate with real automatic

block system (e.g. OVATION, PROCONTROL). They facilitate:� project,

� research,� testing power unit control system.

2. Thermal power unit models.

Parameters of models formulated by IASE Ltd:� live steam and re-superheated steam pressure,� unit active power,� rotational turbine set speed,� concentration of oxygen in flue gas,� furnace chamber vacuum pressure,� drum water level,� pulverized coal temperature,� coal mill engine power.

Using models cuts down the time of project realization and reduces all the risks associated with the work on the real

object.


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Steps of making use of models on real power objects:

1. Performing model (e.g. in MATLAB/SIMULINK) basedon scientific literature and expert knowledge.

2. Model revision and optimization based on object data.

3. Model implementation and tuning on a real object indifferent power plant operating condition (until obtaining

similar time courses between model and real signal).4. Practical use of tuned model on real object.


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Models must:

� be compact, not to use up space in modules in power plant system (cost of implementation),

� have the same logic blocks as in power plant system,

� have fast time of reaction (counting process in power plant system is very important).


That’s why it is important to simplify createdmodels to meet the above criteria.

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3. Model of a ring-ball coal mill

Grinding fuel process is very important in thermal and electric power production at power units.

Essential qualities are static and dynamic characteristic of coal mill, learned through object exploitation, research and

literature.

We also get knowledge about grinding process from models research, based on data obtained from operating object.

Modeled signals can be used to identify a disturbance which has influence on this process.

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Model perform is to reprocude transient flow-heat process

during operation of power unit and to obtain (in accordance

with reality) time course as typical disturbance responce.

We can take advantage modeled signal to:� get knownledge about phisical phenomenon proceed in

process, instalation and automatic control system,� disturbance indentification in process.

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Fig. 1. Block diagram of modelled pulverized coal temperature and coal mill engine power signals.

Designations :

powm.

-primary air flow signal, powT -primary air temperature signal podV - coal feeder rotational speed signal,

rzsmN−

- coal mill engine power signal, rzmppT−

- pulverized coal temperature signal, cw -humidity of fuel,

wegT - coal temperature, mmppT−

- pulverized coal temperature model signal

msmN−

-coal mill engine power model signal


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Heat flux, putting into coal mill with primary air, is

primary air fluid flow and hot air temperature

dependent. Heat flux, putting with coal into coal mill, is coal feeder

constant temperature and rotational speed dependent.Specific heats are selected based on phisical table and

then corrected during research.

Pulverized coal temperature model signal is formed by dynamical elements signal conversion:

�heat flux loaded to coal-mill (coal flow, primary air flow,

hot air temperature),�heat flux to water evaporation in coal,

�coal and air fluid flow.

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Fig. 2. Block diagram of modelled pulverized coal temperature signals in MATLAB/SIMULINK.

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Fig.3. Results of coal mill engine power signals in MATLAB/SIMULINK (sedimentation

of mill complex at the end): pulverized coal temperature – green, pulverized coal

temperature (with idle running) – blue.

t [s]

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Fig.4. Results of coal mill engine power signals in MATLAB/SIMULINK (starting up of

mill complex at the beginnig and dig in of mill complex at the end): pulverized coal

temperature – green, pulverized coal temperature model signal – blue.

t [s]

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To compensate all the inside disturbance, imposisible

to model (e.g. homogeneous fuel fluid flow put into coalmill and fuel phisical characteristic – especially

humidity), the correction controler was inserted.

To model pulverized coal temperature the inertial

dynamical elements were used in order to reflectnatural processes and inertial behavior of meter circuit

of the pulverized coal temperature.

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Coal mill engine power model signal is created by

conversion of coal feeder rotational speed in inertialdynamical elements, which is the answer of coal mill engine

power to the coal feeder rotational speed changes.

Fig. 5. Block diagram of modelled coal mill engine power signals in MATLAB/SIMULINK.

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Fig.6. Results of coal mill engine power signals in MATLAB/SIMULINK

(sedimentation of mill complex at the end): engine power signal – green, coal mill

engine power model signal (with idle running) – blue.

t [s]

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Fig.7. Results of coal mill engine power signals in MATLAB/SIMULINK (starting up

of mill complex at the beginnig and dig in of mill complex at the end): engine power

signal – green, coal mill engine power model signal (with idle running) – blue.

t [s]

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In this case correction controler was used to adapt the

gain of the above mentioned inertial dynamical

elements.The input signals of this controller are:

�modelled coal mill engine power,�actual coal mill engine power.

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• Fire in coal mills is most undesired, and the likelihood of thisto occur is increased with biomass co-firing. The

phenomenom of biomass clustering in mill chamber occursfrequently, which may casue ignition and resulting fire inthe mill.

• During expoiltation of coal mills in systems with biomass co-

firing it can be observed that there is greater number of fuel-air mixture ignitions during mill’s operation, shutdown and

start up stages than for coal only systems.

4. Practical use of the modelled mill motor power signaland the pulverized coal temperature signal

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Taking into account before mentioned issues, IASE Ltdcreated the system to detect and prevent ignition andfire in coal mills.

The system relies on:

•Algorithm to detect loss of coal in a coal mill –preventshot air flow into the coal bunker.

•Algorithm to detect fire in a coal mill, based on the coal

mill model.


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The output signal of heat balance controller (used in the

mill model) may provide means to measure diversity offuel stream supplied to a coal mill and its varying physical

properties (mainly humidity).


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4.1. Loss of coal detection system based on the

coal-mill model.

• In the system the difference is created betweenmodelled mill motor power and the measured motor

power which is then compared with its limiting set value(in the overrun indicator with set hysteresis).

• When the limiting value is exceeded the logic signal isgenerated informing about loss of coal.

• The signal is stored and displayed, as well as

practically used in the process of disturbance

elimination.

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4.1. Loss of coal detection system based on the coal-mill model.

Fig 8.The block logic of the algorithm enabling detection loss of coal in coal mill. Designations

as in Fig.1, SP – overrun idicator with set histeresis and boundary value, SBW – logical signal

of loss of coal in coal mill.

The system was implemented on 14 power units withthe total power generating capacity of about 2800 MW.

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4.1. Loss of coal detection system based on thecoal-mill model.

Logic signals informing about loss of coal together withthe differential signal showing the dynamic

characteristic and strength of this disturbance are usedto decrease the adverse inpact of the disturbance on a

power unit control systems, e.g. in control systems ofthe load and the amount of total air.

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This information is vital, because distrubances in thefuel flow to the boiler tend to destabilize work of most

control systems, leading to creation of exagerated, harmfull deviations in technological parameters and

their nominal values.

This further leads to:

•components damage,•shortened life time of the equipment,

•overheat of the coal mill,•lowering of the work comfort,

•potential life threats to the power plant personnel

working in proximity of coal mills.

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The system is characterized by:

• very good reliability of the signal informing about thedisturbance in a wide band of the mill load,

•high sensitivity,• ease of application to the systems already being

exploited (usually equiped with power transducers).

It is important to prevent clustering of the coal in thecoal bunkers or in the coal feed pipes.

In the case such a disturbance occurs it is vital to

receive the notification.

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Fig.9. Work presentation of loss of coal in coal mill detection: correction controller of power

engine coal mill output signal (green), coal mill engine power signal (blue), coal mill engine power

inertial signal – without idle running (light green), modelled coal mill engine power inertial signal –

without idle running (light blue), logical signal of loss of coal (pink), loss of coal power (yellow),

powet of unit (red), gradient of drum steam pressure (black).

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4.2. Fire detection system based on the coal-mill model.

Fig. 10. The block logic of the algorithm enabling detection of overheat and fire in mill.

Designations as in Fig.1, SP – exceed signaling device with histeresis and boundary value,

SPT – logical signal of overheat or fire in coal mill.

The system was implemented on 14 power units with the total

power generating capacity of about 2800 MW.

The system enabling ignition and fire detection in coal-mills isbased on comparison of the pulverized coal temperature with

the modelled pulverized coal temperature.

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4. 2. Fire detection system based on the coal-mill model.

The binary signal informing about overheat of the mill iscreated when the set point (in the overrun idicator with set

hysteresis) is exceeded by the signal relating thedifference in temperatures between the pulverized coal

and its model (obtained from the heat equilibriumdeviations due to disturbances not included in the created

mill model).

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It is then successfully used in the system protecting the mill

against the propagation of a threat (fire), e.g. through

introduction of chemically neutral gas or steam.

Implementation of the above system is espessially effectivein systems with inertial temperature and the pulverized coal

measurement devices.

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Fig. 11. Results of modelled and actual pulverized coal temperature subject to natural

inputs: coal feeder rotational speed (red); corrected airflow to the coal mill (blue);

pulverized coal temperature (green); pulverized coal temperature model signal (light

blue); output of heat balance correction controller (violet); logic signal informing about

overheat or fire in the mill (yellow).

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Fig. 12. Presentation of power unit’s outputs of the fire detection system in the coal-mill based on the coal-mill model (1 tick=1 minute): pulverized coal temperature in the neighboring mill (red); pulverized coal temperature model signal in the neighboring mill (blue); logic signal informing about overheat or fire in the mill (green); fire

detection logic signal (light blue); pulverized coal temperature in the mill (violet); pulverized coal temperature in the mill model signal (orange); temperature differential signal of pulverized coal in the mill (brown), output of

heat balance correction controller (black).

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Fig. 13. Presentation of power unit’s outputs of the fire detection system in the coal-mill based on the coal-mill model (1 tick=1 minute) : pulverized coal temperature in the neighboring mill (red); pulverized coal temperaturemodel signal in the neighboring mill (blue); logic signal informing about overheat or fire in the mill (brown); fire

detection logic signal (black); pulverized coal temperature in the mill (violet); pulverized coal temperature model signal in the mill (orange); temperature differential signal of pulverized coal in the mill (green), output of heat

balance correction controller (light blue).

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Ministry of Economy Cup in 2011 for product: „The system to

detect and prevent ignition and fire in ball-ring coal mills”

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Mathematical Modelling and Simulation of

Power Plants and CO2 Capture

Thank you for attention!

Institute ofPower SystemsAutomation Ltd

Wrocław, Poland

University of Warwick

20-21 March 2012

Lipinski Workshop on Modelling and Simulation of Coal-fired Power Generation and CCS Process

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Transcript of Lipinski Workshop on Modelling and Simulation of Coal-fired Power Generation and CCS Process