Using Data Mining Methods to Optimize Boiler Performance: Successes and Lessons Learned

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Optimize combustion, OFA l Stabilize operations l Reduce emissions l Predict problems

Using Data Mining Methods to

Optimize Boiler Performance:

Successes and Lessons Learned

Thomas Hill, Ph.D.

StatSoft Power Solutions StatSoft Inc.

www.StatSoft.com

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© Copyright StatSoft, Inc., 1984-2012. StatSoft, StatSoft Power Solutions, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc. 2

Overview

■

Data mining methods have proven increasingly useful for untangling

complex relationships in high-dimensional process data (with hundreds

or even thousands of parameters)

■

These methods can produce significant performance

improvements from existing equipment and without requiring any

additional hardware or equipment modifications

■

This presentation:

■ Discusses how data mining methods and algorithms are different from

simple trending and/or the application of traditional statistical methods

■ Presents an overview of typical workflows and necessary steps from data

preparation through modeling and optimization

■ Reviews typical examples and case studies where data mining methods

have been applied successfully for combustion optimization

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Data Driven Technologies

■

Historical operational data can be used for process optimization, by

applying methods that:

■ Focus on knowledge discovery, detection of patterns, clusters, etc.

■ Apply advanced knowledge discovery (data mining) algorithms that will

result in valid models for highly complex data

■ E.g.: recursive partitioning, stochastic gradient boosting/bagging of trees,

multivariate adaptive regression splines, support vector machines, ...

■ All of these algorithms are universal approximators, which can model

(approximate) any relationships between parameters (non/linear, interactions, etc.)

■ Use optimization methods with the goal to achieve robust/stable and

optimized performance (e.g., low-variability NOx and CO, in presence of normal variability in fuel quality, load, etc.)

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Data Driven Technologies:

Comparison to Other Methods

■

Unlike CFD (Computational Fluid Dynamics) modeling

■ We analyze the actual data describing actual “observed” performance of

the power plant over the past 1 or 2 years, or more

■

Unlike DOE (Design of Experiments) methods

■ We do not perform “trial-and-error” testing to find simple relationships, but

identify complex relationships through the application of nonlinear general modeling algorithms (“data mining”)

■

To use a metaphor:

■ The power plant or boiler is “talking” to the operators or control

system through the language of numbers, recorded into a data historian such as OSI Pi

■ The data mining methods and algorithms described on the previous

slide “decipher” this language, and leverage the extracted information for process optimization

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Data Mining and Statistical Modeling

Knowledge Discovery vs. Statistical Analysis

■

Statistical Analysis

■ Focuses on “hypothesis testing” and “parameter estimation”

■ Fits “parsimonious statistical models” with the goal to “explain”

■ Examples: Regression, traditional generalized linear models (GLM)

■

Data Mining and General Predictive Analytics

■ The data are your model!

■ Focuses on the detection of repeatable patterns in historical data, with the

goal to make predictions about future events

■ Focuses on knowledge discovery, detection of patterns, clusters, and so

on; we only have data and no (or few) expectations and hypotheses

■ Applies “pattern recognition algorithms” or “general approximators”, with

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Data Driven Technologies:

Summary

■

In short, based on these methods, StatSoft developed and validated a

workflow that:

■ Uses all historical data

that are already routinely collected (and have already been collected for some time)

■ Identifies in those data the

specific operational

parameters that are critical for optimal boiler performance

■ Builds data mining models describing how exactly the important

parameters affect the performance of a furnace

■ Uses those models for robust optimization (optimization for robust,

low-variability operations/performance)

■ Identifies optimized parameter ranges and relationships for critical

operational parameters that can be implemented into the existing control system

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Concrete Examples:

Cyclone Furnaces

■

See also: EPRI/StatSoft Project 44771: Statistical Use of Existing DCS

Data for Process Optimization (2008)

■

Numerous projects have applied data mining technologies to cyclone

furnaces of various designs and sizes

■ Goal typically is to stabilize flame temperatures within a desirable range

(e.g., measured flame intensities, door, slag temperatures..)

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Concrete examples:

Cyclone Furnaces (cont.)

■

Typical results are often immediate

■ Significant sustained improvements to LOI and boiler efficiency

(>20%) without negative side-effects have also been demonstrated

■ Quote: “….. after the data mined settings were put into the control system (unit 3)

with solid success (≈ $1.4M savings in oil), however the results were harder to

quantify in unit 4, as 4 never needed or used as much oil as unit 3. (Oil in unit 3 was more of a crutch than on unit 4.) However, the settings (unit 4) that you folks did

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Concrete Examples:

Cyclone Furnaces (cont.)

■

After implementing results into

the DCS system as new

defaults, flame temperatures are

consistently higher

■

Conclusion: The new settings

• Nox and CO emissions are

significantly lower

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EPRI Perspective

■

See:

http://my.epri.com/portal/server.pt?Abstract_id=000000000001016494

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Results

■ “The results show that the data analysis and optimization methods

effectively identified specific ranges for a relatively small subset of operational parameters. Significantly improved and stable operations resulted for all cyclones.”

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Application, Value and Use

■ “These methods provide a way to achieve cost-effective and ‘realistic’

(virtually immediately obtainable) boiler optimization given the existing

data and control systems—without the need for further boiler modifications or hardware and/or software purchases—requiring only modifications to the parameters and “equations” guiding existing control system software.”

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Concrete Examples:

Wall Fired

■

Several projects have focused on lowering NOx and CO emissions

(or SCR inlet NOx) over the entire load range.

■

For example, optimization of a 400 MW Coal-Fired DRB-4Z Burner

for consistent/robust low-NOx operations under low load (50-175 MW)

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Concrete Examples:

Wall Fired (cont.)

■

Typical Results:

■ Lower NOx during testing and more robust performance (lower variability in

NOx measurements, with fewer/no spikes)

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Concrete Examples:

T-Fired

■

To date StatSoft has completed only one project with a twin T-fired

boiler, with a complex (multi-zone) OFA system

■

Goal was to stabilize and reduce CO while maintaining or lowering Nox

■

Simple before-after tests

showed the effectiveness

of specific combinations of

settings for various parameters

(involving Windbox/Furnace

Differential Pressure, and

OFA parameters)

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Lessons Learned (1)

■

The methods described here are generally applicable to any type of

coal furnace

■ And likely other types of furnaces and complex equipment as well

■

Only basic requirement is a process historian (database, such as OSI

Pi) that collects and stores operational parameter data

■

The methods described here can be used to optimize boiler

performance using the existing control systems and methods

■

Lessons learned (organizational):

■ Commitment by operators and performance engineers to project is critical

for success

■ Identify at the start how the results are to be implemented (e.g., into the

control system); this decision will guide the modeling efforts and ensure that results are actionable

■ Determine the dollar value of the performance improvements; this is critical

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Lessons Learned (2)

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Lessons learned (data and methods):

■ Data quality must be ascertained before performing any analyses; in

particular: Make sure that data gathering devices, sensors, scaling, or computations did not change

■ Establish through testing that the variability of key inputs (e.g., secondary

air flows) are controllable within the required bounds; in particular, if the unit and air flows “swing” (e.g., because of unreliable O2

probes/measurements), then recommended settings may not be achievable!

■ Sometimes, changes to the logic of the control system are necessary

■ Examine causes of variability in inputs (e.g., air flows), to determine that

operators do experiment and “drive the unit”; sometimes, all variability is produced through default operation of the control system

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References

■

Nisbet, R., Elder, J., & Miner, G. (2009). Handbook of Statistical

Analysis and Data Mining Applications. Academic Press

■

StatSoft Electronic Text Book:

■ www.statsoft.com/textbook/stathome.html

■ Also available from Amazon: Hill, T. & Lewicki, P. (2005). Statistics:

Methods and Applications.

■

EPRI/StatSoft Project 44771. Statistical Use of Existing DCS Data for

http://my.epri.com/portal/server.pt?Abstract_id=000000000001016494

www.statsoft.com/textbook/stathome.html