MTA Soot Blowing
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Transcript of MTA Soot Blowing
Fuzzy Logic Based
Intelligent Soot Blowing System
Made By : Aman Singhal (102028)
Chandan Kr Nirala (1020 )Gaurav Srivastava (102005)
ET-09 (C&I),NTPC, Badarpur
OUTLINE
Basic Concepts of Fuzzy Logic Fuzzy Decision Making Process
Soot Blowing:Conventional method v/s Intelligent system Observations from Existing Systems : SWBS & PADO
Fuzzy based Intelligent Soot Blowing Parameters & Performance Data Conclusion
What is Fuzzy Logic ??
Fuzzy Logic is
• a mathematical system, that
• analyzes analog input values in terms of logical variables, that
• take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 0 and 1 (true and false).
• Fuzzy Logic is the theory of fuzzy sets, sets that calibrate vagueness and uncertainty
Motivation : Why Fuzzy ??
Fuzzy logic is conceptually easy to understand. Implementing design objectives, that are difficult to express
mathematically, in Linguistic or descriptive rules. No need for a mathematical model, based on natural
language Relatively Simple, Adaptive and Flexible Less sensitive to system fluctuations Fuzzy logic can model nonlinear functions of arbitrary
complexity Fuzzy logic can be blended with conventional control
techniques
Fuzzy Logic v/s Classical ( Crisp ) Logic
Classical Logic (Crisp Set)
Crisp set indicates whether an element belongs to it or not.
Element x belongs to set
A or it does not: μA(x) {0,1}∈
Fuzzy Logic
Fuzzy set indicates how
much (to which extent) an element belongs to it.
Element x belongs to set A with a certain degree of membership:
μA(x) [0,1]∈
Fuzzy Set A fuzzy set F in a universe of discourse U is
characterized by membership function μF, which takes values in the interval [0,1], i.e.,
μF: U→[0,1]
Structure of Fuzzy System
Fuzzy System Design
Design of a fuzzy system entails the following steps :
Defining linguistic terms for all input & output variables Defining membership functions for all fuzzy sets Specifying conditions and conclusions for all rules i.e the
rule base Choosing appropriate fuzzy operators for reasoning i.e.
the fuzzy inference method Experimenting and validating the system
Fuzzy Applications In Power Plants
Possible areas of application of fuzzy logic are:
Soot Blowing optimization Optimum fuel combustion Load frequency control Control of hydraulic drives Speed control of induction motor
Soot BlowingConventional Method
v/s
Intelligent System
Soot Blower Principle
Intended to blow off the deposits on the furnace and heater surfaces
Generally, the performance is proportional to the square of the jet nozzle diameter and jet pressure, and inversely proportional to the distance between jet nozzle and the heating surface
Remotely and sequentially operated from a separate panel in the control room.
Need of Soot blower
Clean soot off walls and heater surfaces in the boiler Improve surface heat transfer Maintain balance between radiation & convection zone
heat transfer Reduce attemperation spray rates Lower furnace exit gas temperature (FEGT) Reduce NOx emissions
Current Methodology
Traditionally been performed on a schedule rather than optimized based on actual fouling conditions in the boiler
Every alternate morning shift for water wall and evening for APH
No consideration for the amount of soot deposition No parameter observed particularly for the identification
of soot blowing regions.
Disadvantages of Conventional Soot Blowing
Certain boiler stages are blown unnecessarily leading to heat rate penalty.
Excessive soot blowing results in erosion of tube surfaces.
Improper soot blowing affect furnace exit gas temperature
Under-sootblowing results in decreased heat transfer rate
Overall reduced boiler efficiency.
Think In The Direction Of…
Establishing optimized operation of wall blowers
Achieving optimum heat absorption in the furnace
Limiting attemperation spray
“Intelligent Soot Blowing” is the solution !!
Expert system to indicate individual section cleanliness to determine correct soot blowing scheme
Estimate the cleanliness factor of the furnace followed by firing of a group of blowers where soot blowing is actually needed
Intelligent Soot Blowing
Advantages of Intelligent Soot Blowing
Improves boiler performance as furnace heat absorption can be maintained optimally
Reduces NOx emissions Minimizes disturbances caused by soot blower activation Optimal balance between furnace and convective pass
heat transfer Improve soot blower life and reduce maintenance cost Reduced attemperation spray rates Reduced tube erosion
OBSERVATIONS FROM
VARIOUS POWER PLANTS
Smart Wall Blowing System at Raichur Thermal Power Station
Heat flux study by m/s. BHEL in unit 1& 2 boilers at RTPS in April 1996
Arrangements for heat flux measurements using portable HF probes & optical pyrometer
Automated system was dedicated in Jan 2002 Operation is based on combustion regime - depends on
burner tilt, excess air, mill combination, no. Of mills in service, etc.
Brief Description of SWBS at RTPS
Based on the direct measurement of furnace heat flux at a number of selected locations within the furnace
Soot blower operation based on heat transfer need or demand
56 blowers and 32 heat flux sensors mounted near the wall blowers covering all the elevations
SH Spray water flow and Heat Flux reduction are taken as index of optimized decision
Heat absorbed per unit area is calculated by multiplying heat coefficient (obtained experimentally by thermal mapping) and the wall tube differential temperature (measured by K Type Thermocouple)
Heat Flux Sensor at RTPS
Typical SH Spray Trend -Conventional v/s SWBS
Improved Performance
Soot Blowing Optimization at NTPC, Simhadri
The relevant heating surfaces included in the soot blowing optimization are:
Furnace SH panelette SH platen RH LTSH Economizer
PADO SR4 Screenshot
u
Fuzzy based Intelligent Soot Blowing System(Based on M.Tech Report by Mr. P.S. Chowdhury, EOC)
Fuzzy rule-based Expert system to estimate the cleanliness factor (CF) of the furnace
Calculates the heat absorbed and the degree of individual stage fouling in the form of CF
No need of heat flux sensors Advises on 'When' and 'Where' to Soot Blow,
depending on a single index, CF.
Description
The mathematical methodology of finding the cleanliness factor of the following regions in the boiler:
Furnace Reheaters Low temperature Super heaters Economizers
Parameter Identification
Following input variables are identified for fuzzification :
LTSH metal temperature Total spray flow Burner Tilt Mill Combination Load Elapsed Time since last soot blow
Block Diagram
Rule Base for Estimating Furnace Dirtiness
Performance Data during Soot Blowing
Soot blowing operation at NTPC, Dadri.
•Improvement in Heat rate by 17 Kcal/Kwhr
•Boiler eff. improvement by 0.18 %
•Load is improved by around 2 MW withsame coal (fuel) input
Conclusion
With the existing instrumentations it is possible to determine the Cleanliness Factor of Furnace, Reheater, Final Superheater, LTSH and Economizer.
This gives better information on soot deposition and heat transfer in Furnace and convective zones.
Indirect method of assessing furnace condition is superior to direct method of measuring the heat flux as the heat flux meters are expensive and if failed, it requires unit shutdown for replacement.
THANK YOU
(Special Thanks to Mr. P.S. Chowdhury, EOC, NTPCfor his expert guidance on the subject)