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)