Mathematical Modelling and Simulation of Power Plants and CO2 Capture

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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

1 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. University of Warwick March 2012 Mariusz Lipiński Institute of Power Systems Automation Ltd, Wrocław, Poland 1

2 Speech program 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 Identify the loss of coal in the coal mill system Identify ignitions in the coal mill system. 2

3 1. IASE Ltd. - about us. 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. 3

4 1. IASE Ltd. - about us. 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 solution to meet individual requirements. 4

5 Our fields of activities: 1. IASE Ltd. - about us. - engineering and design of systems and equipment for power engineering and industrial plants, - management of deliveries, - commissioning of systems and equipment in industrial plants, - providing training courses and maintenance services, - implementing turn key projects. Contact data: ul. Wystawowa Wrocław sekretariat@iase.wroc.pl Phone(071) Fax(071) , Secretariat(071)

6 2. Thermal power unit models. 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. 6

7 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.

8 2. Thermal power unit models. Steps of making use of models on real power objects: 1. Performing model (e.g. in MATLAB/SIMULINK) based on scientific literature and expert knowledge. 2. Model revision and optimization based on object data. 3. Model implementation and tuning on a real object in different power plant operating condition (until obtaining similar time courses between model and real signal). 4. Practical use of tuned model on real object. 8

9 2. Thermal power unit models. 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 created models to meet the above criteria. 9

10 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. 10

11 3. Model of a ring-ball coal mill 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. 11

12 3. Model of a ring-ball coal mill Fig. 1. Block diagram of modelled pulverized coal temperature and coal mill engine power signals. Designations : ṁ pow -primary air flow signal, Tpow-primary air temperature signal Vpod -coalfeederrotationalspeedsignal, N sm rz-coalmillenginepowersignal, Tmpp rz-pulverizedcoal temperaturesignal, wc -humidityoffuel, Tweg- coal temperature, Tmpp m-pulverizedcoal temperaturemodel signal 12 N -coalmillenginepowermodel signal sm m

13 3. Model of a ring-ball coal mill 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. 13

14 3. Model of a ring-ball coal mill Fig. 2. Block diagram of modelled pulverized coal temperature signals in MATLAB/SIMULINK. 14

15 3. Model of a ring-ball coal mill 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. 15 t [s]

16 3. Model of a ring-ball coal mill 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] 16

17 3. Model of a ring-ball coal mill To compensate all the inside disturbance, imposisible to model (e.g. homogeneous fuel fluid flow put into coal mill 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 reflect natural processes and inertial behavior of meter circuit of the pulverized coal temperature. 17

18 3. Model of a ring-ball coal mill Fig. 5. Block diagram of modelled coal mill engine power signals in MATLAB/SIMULINK. Coal mill engine power model signal is created by conversion of coal feeder rotational speed in inertial dynamical elements, which is the answer of coal mill engine power to the coal feeder rotational speed changes. 18

19 3. Model of a ring-ball coal mill 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. 19 t [s]

20 3. Model of a ring-ball coal mill 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. 20 t [s]

21 3. Model of a ring-ball coal mill 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. 21

22 4. Practical use of the modelled mill motor power signal and the pulverized coal temperature signal Fire in coal mills is most undesired, and the likelihood of this to occur is increased with biomass co-firing. The phenomenom of biomass clustering in mill chamber occurs frequently, which may casue ignition and resulting fire in the mill. During expoiltation of coal mills in systems with biomass cofiring it can be observed that there is greater number of fuelair mixture ignitions during mill s operation, shutdown and start up stages than for coal only systems. 22

23 4. Practical use of the modelled mill motor power signal and the pulverized coal temperature signal Taking into account before mentioned issues, IASE Ltd created the system to detect and prevent ignition and fire in coal mills. The system relies on: Algorithm to detect loss of coal in a coal mill prevents hot air flow into the coal bunker. Algorithm to detect fire in a coal mill, based on the coal mill model. 23

24 4. Practical use of the modelled mill motor power signal and the pulverized coal temperature signal The output signal of heat balance controller (used in the mill model) may provide means to measure diversity of fuel stream supplied to a coal mill and its varying physical properties (mainly humidity). 24

25 4.1. Loss of coal detection system based on the coal-mill model. In the system the difference is created between modelled 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 is generated informing about loss of coal. The signal is stored and displayed, as well as practically used in the process of disturbance elimination. 25

26 4.1. Loss of coal detection system based on the coalmill 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 with the total power generating capacity of about 2800 MW. 26

27 4.1. Loss of coal detection system based on the coal-mill model. Logic signals informing about loss of coal together with the differential signal showing the dynamic characteristic and strength of this disturbance are used to decrease the adverse inpact of the disturbance on a power unit control systems, e.g. in control systems of the load and the amount of total air. 27

28 4.1. Loss of coal detection system based on the coalmill model. This information is vital, because distrubances in the fuel 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. 28

29 4.1. Loss of coal detection system based on the coalmill model. It is important to prevent clustering of the coal in the coal bunkers or in the coal feed pipes. In the case such a disturbance occurs it is vital to receive the notification. The system is characterized by: very good reliability of the signal informing about the disturbance in a wide band of the mill load, high sensitivity, ease of application to the systems already being exploited (usually equiped with power transducers). 29

30 4.1. Loss of coal detection system based on the coalmill model. 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). 30

31 4.2. Fire detection system based on the coal-mill model. The system enabling ignition and fire detection in coal-mills is based on comparison of the pulverized coal temperature with the modelled pulverized coal temperature. 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. Thesystem was implementedon 14powerunitswiththetotal power generating capacity of about 2800 MW. 31

32 4. 2. Fire detection system based on the coal-mill model. The binary signal informing about overheat of the mill is created when the set point (in the overrun idicator with set hysteresis) is exceeded by the signal relating the difference in temperatures between the pulverized coal and its model (obtained from the heat equilibrium deviations due to disturbances not included in the created mill model). 32

33 4.2. Fire detection system based on the coal-mill model. 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 effective in systems with inertial temperature and the pulverized coal measurement devices. 33

34 4.2. Fire detection system based on the coal-mill model. 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). 34

35 4.2. Fire detection system based on the coal-mill model. 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). 35

36 4.2. Fire detection system based on the coal-mill model. 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 temperature model 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). 36

37 Ministry of Economy Cup in 2011 for product: The system to detect and prevent ignition and fire in ball-ring coal mills 37

38 Mathematical Modelling and Simulation of Power Plants and CO2 Capture Thank you for attention! University of Warwick March 2012 Institute of Power Systems Automation Ltd Wrocław, Poland 38

CHAPTER 2 STUDY OF 210 MW BOILER SYSTEM 2.1 DESCRIPTION OF 210 MW BOILER

8 CHAPTER 2 STUDY OF 210 MW BOILER SYSTEM 2.1 DESCRIPTION OF 210 MW BOILER Detailed study has been carried out on a 210 MW boiler system with regard to model development, control and optimization. Figure