Improved Boiler Performance

using Targeted Sootblower

Activation

2015 EPRI Heat Rate Improvement Conference

February 3-5, 2015

Steve Piche, Director Research & Development, NeuCo, Inc.

piche@neuco.net

Outline

Introduction

Modeling the Cleanliness Factor

Optimizing Blower Activation

Results

Conclusions

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Traditional Sequence Based Blowing

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• Blowers are grouped in zones based upon locations

• Operator activates a cleaning sequence for the zones.

• Once a zone is selected, the blowers are activated in a predetermined order.

Intelligent Sootblowing

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• Blowers are grouped in zones based upon locations

• Software, often rules based, selects a zone for cleaning.

• Once a zone is selected, the blowers are activated in a predetermined order or

based upon simple logic.

Targeted Intelligent Sootblowing

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• Blowers are grouped in zones based upon locations

• Software, often rules based, selects a zone for cleaning.

• Once a zone is selected, a single blower with the greatest impact is activated.

Targeted Intelligent Sootblowing Technologies

A rules based expert system is used to select a

zone for cleaning.

Performance calculations are used to compute the

cleanliness factors and heat duties of boiler heat

transfer surfaces.

Neural network based models are used to represent

the relationship between blower activation and

cleanliness of the boiler sections.

In selecting a blower, optimization techniques are

used to select a blower for activation based upon

maximizing a goal (greatest impact) while observing

constraints (typical minimum and maximum idle

times).

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Neural Network Modeling of

the Cleanliness Factors

Definition of Cleanliness Factor

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Heat Transfer Coefficient times Area:

U x Ar = Q

LMTD

Cleanliness:

C = Uactual x Ar x 100

UAVE x Ar

= Uactual x 100

UAVE

The Pyramid of Calculations for Cleanliness Factors

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Steam Flow Calculations

Boiler Heat Duty Calculation

Boiler Efficiency

Gas Flow

Gas Temps

Cleanliness

Neural Network Model

Neural network modeling is a curve fitting approach.

Neural network models are trained using historical

data. (In this case, we used 3 months of blower

activation and heat transfer coefficient data.)

Neural network models are convenient when you

have lots of data and not very good understanding of

the first principles involved.

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Neural

Network

Model

Blower 1 Time Since Last Blow

Blower 2 Time Since Last Blow

Blower 3 Time Since Last Blow

Heat Transfer

Coefficient or

Cleanliness

Factor

Division Panel Heat Transfer Coefficient (1 Day)

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• Red Line: Actual heat transfer coefficient

• Navy Blue Line: Neural prediction of heat transfer coefficient

• Yellow, Light Blue and Orange Lines: Time since blower activation

Division Panel Heat Transfer Coefficient (1 week)

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• Red Line: Actual heat transfer coefficient

• Navy Blue Line: Neural prediction of heat transfer coefficient

• Yellow, Light Blue and Orange Lines: Time since blower activation

Optimizing Blower

Selection

Scenario Evaluation

(Blower 1 with time since = 0)

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Cleanliness

Neural

Network

Model

Blower_1_TS(t+1) = 0

Blower_2_TS(t)

Blower_3_TS(t)

Delta_Cleanliness_1_(t+1)

Cleanliness

Neural

Network

Model

+-

+

Blower_1_TS(t)

Blower_2_TS(t)

Blower_3_TS(t)

The change in cleanliness due to blower activation can be predicted by

modifying the blower time since activation from the current value to a value

of zero.

Optimization Example

If any time since last activation for a blower in the zone is

greater than the maximum idle time, then activate the

blower with the longest idle time otherwise

select the blower in a zone that maximizes the following

function:

max_i(Delta_Cleanliness_i(t+1))

subject to the constraint that the time since last blow for

blower i is greater than a minimum idle time.

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Optimization Formulation

The previous example gives a simple optimization

formulation, more complex optimization formulations

can be allowed such as

Zone Specific Optimization Function: The optimization

formulation can be tailored for a specific zone, ie. not all

formulations for each zone need be the same.

Multiple Neural Models per Zone: Blowers in some zones

affect two heat transfer surfaces, thus, two neural models

may be needed in the formulation.

Advantage of Average Change in Heat Duty: If using two

models, it may be preferable to model the change in average

heat duty rather than average change in cleanliness. This

biases blowers to cleaning larger heating surfaces.

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Results

Experimental Data

Experiment is conducted at large T-fired unit (550 MW).

Neural blower selector experiment was conducted from

March 10-23, 2014 (2 weeks)

For comparison, a time based selector (the baseline case)

was used from Dec 28 – Jan 10, 2014 (2 weeks).

During the experiment, the neural models were retrained

every night.

During the experiment and comparison period, SootOpt is

on 100% of the time.

No outages occurred during either the experiment and

comparison period.

Load profiles are comparable, however, the unit was base

loaded more during the experiment.

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Blower Impacts West during Baseline

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Blower Impacts West during Neural Selection

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Sootblowing Counts

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Zone Baseline Neural Selector Percent Change

SH Platen 93 96 3.2

Reheat 1 257 163 -36.6

Reheat 2 423 251 -40.7

Low Temp SH 225 151 -32.9

Economizer 393 448 14.0

Total 1391 1109 -20.3

20% reduction in blowing

Superheat and Reheat Temperatures

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Baseline Scatter Plot (Temp vs MW) Neural Selector Scatter Plot (Temp vs MW)

Average Temp Baseline Neural Selector Change

Superheat East 992 997 5

Superheat West 999 998 -1

Reheat East 965 980 15

Reheat West 968 976 8

Average 7

Increases in steam temperatures results in improved heat rate.

Conclusions

Targeted, intelligent sootblowing is achieved using a

combination of performance calculations, neural network

modeling, expert systems and optimization techniques.

High quality neural network models of the effects of blower

activations on the cleanliness factor and heat duty can be

developed.

Using these high quality models, optimization techniques can

be used to select a blower in a zone with the “greatest impact”.

The application engineer developing the targeted, intelligent

sootblowing system has great flexibility in defining what it

means for a blower to have the “greatest impact”.

At demonstration site, targeted, intelligent sootblowing resulted

in fewer sootblower activations and increased steam

temperature which correspondingly improved heat rate.

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