Intelligent Combustion Optimisation solution for coal fired power plants. Raja De

Transcription

1 Intelligent Combustion Optimisation solution for coal fired power plants Raja De

2 STEAG Powitec GmbH Powitec Intelligent Technologies GmbH is technology leader in developing and implementing intelligent advanced control systems for power plant and other process industries. In 2012, STEAG Energy Services acquired the company Powitec Intelligent Technologies GmbH. Since then, the new subsidiary has been operating under the name STEAG Powitec GmbH. The STEAG Powitec optimization approach consists of a software-based process control in closed control loop configuration with fully automatic and selflearning process optimization features.

3 Contents 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

4 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 4

5 Customers Targets Practical problems Uncertainties 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. 5

6 Poor Combustion Effects 6

7 Approach to Closed Loop Optimization Fußzeilentext 7

8 Approach to Closed Loop Optimization Which input parameters having more influence in process and can be manipulated? Suggested variables to be manipulated: Auxiliary Air Dampers SP Burner Tilt SP Excess O2 SP Main Steam Temperature SP Reheat Steam Temperature SP Main Steam Pressure SP Mill loading SP 8

9 PiT Navigator Vibration sensors (mills) PIT Navigator Software Vibration sensors (burners) Toshiba DCS (ModbusTCP) Filtering & Preprocessing OnlineCFD Navigator core HMI Display (local) Display (remote) TOSMAP (OPC DA) Database OSI (OPC DA) 9

10 PiT-Navigator Process Interface modules by modbus and OPC server Process interface between Powitec's system server and the distributed control system (DCS) of the plant needs to be established. On one hand DCS-process data need to be transferred online from the DCS towards Powitec's server. On the other hand, calculated set-point biases of several manipulated variables need to be written back to the DCS. The PiT Navigator consists of several controller modules like mill, sadc, o2 etc. Automatic Feature Selection: Automatic feature selector operates on information based measures describing the information content by using a basic filter system and to provide maximum information content with respect to target channels with a minimum of human expertise. Automatic Process Exploration In order to get informative process response in various process conditions, PiT-Stabilizer features automatic tests like step and ramp for manipulated variables. This is being done by adding small offsets/biases in set point/ output of a control loop. 10

11 PiT-Stabilizer Process Modeling and Neural Nets Neural 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 varying process conditions.. 11

12 PiT-Stabilizer 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 monitoring Additional features: Each activation or deactivation of the optimization system is executed by a bumpless transfer. Optimization system can be started by operator personnel exclusively. Each self-deactivation of the optimization system causes an alarm in the DCS The shortest reasonable time period for data transfer is ~1s, typical are ~5s Remote access for online exploration and step testing 12

13 Implementation Vibration 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 Dr. Peter Deeskow 13

14 Typical arrangement of sensors FluxStylus in boiler elevation 1 Vibrasensor per pulv. coal pipe 1 per mill 1 per classifier 14

15 PiT Navigator 15

16 Case Study - Plant Details Unit #1 commissioned MW el 2060 t/h steam subcritical,single furnace, single reheat forced circulation, balance draft coal fired boiler 24 LowNO x Burners Rated coal flow :433.7 t/h 16

17 Boiler and Burner / Air arrangement 17

18 Plant Audit On-site inspection mills coal pipes and air path burners boiler control system available measurements available actuators process interface automatic vs. manual operation Data Mining on available process data Analysis 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. 18

19 Case study Right and left imbalance of furnace is observed. Difference in flue gas temperature between right and left at RH outlet Oxygen 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 (%) Re-Heater flue gas inlet temperature(degree C) Super Heater Spray(kg/sec) Re-Heater Spray(kg/sec)

20 Design of CO Deliverables / Guarantees Typically the PiT Navigator will control the air-/fuel-ratio with following targets Balancing of Flue Gas Temperatures reduction of average excess O 2 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. 20 These limits are to be decided with detailed discussion with plant operation team.

21 Project Execution: Process interface data transfer DCS -> Optimization system (900 variables) air flows related to the boiler (burner + mills +OFA) coal flows into the mills emissions (furnace + chimney) flue gas temperatures in the furnace metal temperatures of super heaters and reheaters steam parameters of boiler (superheaters, reheaters, boiler) burner settings mill parameters (motor amps, temperatures, pressure, speed,...) Optimization system -> DCS (100 nos. biases) coal distribution between mills secondary air distribution between 10 groups feeding three burners each over fire air classifier vane of all mills O2 set point 21

22 Project Execution: 22

23 Project Execution: 23

24 Implementation Bias Monitor 24

25 Implementation Biasing & Loop tuning 25

26 Implementation Biasing & Loop tuning 26

27 Plant specific network configuration 27

28 Implementation Process 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 28

29 Implementation Advanced 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 29

30 CFD Analysis SA_BIAS_REF BT_REF Corner % Corner % Re-heater Inlet Corner % Corner % Furnace Exit 30

31 CFD Analysis Top Elevation Middle Elevation Bottom Elevation SA Dampers SA Bias FG GH HJ +5.0% CD DE EF - 5.0% AB BC - Total SA flow changed 0.0% Re-heater Inlet Re-heater Inlet Furnace Exit 31

32 Performance Test Distribution of O2 and exit gas temp. 32

33 Performance Test Results for comparable periods Label ΔO2/% ΔT/ C CO/mg/Nm³ I -0,63-3,09 91 II -0,62-3,67 72 III -1,06-4,

34 Performance Test Overall 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 = t/a IDF/FDF savings = 2700 MWh/a 34

35 Benefits Optimization of temperature distribution over different levels and minimize imbalances. Optimize fuel consumption Reduction O 2 -excess air CO 2 -reduction CO and NO x reduction Reduction of Unburned Carbon in Ash Increased boiler efficiency Reduction of slagging and soiling at burner mouth and boiler wall. Increased availability. 35

36 Thank You... Ideas & Solutions for Tomorrow