data-driven hierarchical neural network modeling for high ...€¦ · thermal power plant...

Data-Driven Hierarchical Neural Network Modeling for High-Pressure Feedwater Heater Group

Authors: Jiao Yin, Mingshan You, Jinli Cao, Hua Wang,

MingJian Tang and Yong-Feng Ge

4 Feb 2020 2

Contents

Introduction

Industrial Background

Data-Driven Hierarchical Neural Network Modeling

Experiments and Results

Conclusions

4 Feb 2020 3

Introduction

Data-driven machine learning applications Image identification

Speech recognition

Natural Language Understanding

…

Machine learning in thermal power industry High-pressure feedwater heater group modeling

4 Feb 2020 4

Introduction

High-Pressure Feedwater Heater Group (HPFHG)[2]

Consists of three high-pressure feed-water heaters(HPFHs)

o Cascade structure

HPFHG Modeling requirements

o Modeling the heater group as a whole √

o Modeling each single heater at the same time √

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Introduction

HPFHG Modeling Techniques

Physical modeling techniques

o Based on the first law of heat transfer, the second law of heat transfer, the law of

conservation of mass and Newtonian cooling equation

Flaws:

o Some coefficients are dynamically changing [12,9].

o Some coefficients have no sensor to measure [1].

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Introduction

HPFHG Modeling Techniques

Data driven methods

o Traditional ‘black box’ artificial neural network (ANN) model

Flaws:


o Modeling each single heater at the same time ×

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Introduction

Our method

Data-Driven Hierarchical Neural Network Modeling Approach

o Inspired by the physical cascade structure of the heater group



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Contents

Introduction




Conclusions

4 Feb 2020 9

Industrial Background Thermal Power Plant Regenerative System

Improve thermal efficiency

Save fuel

Reduce pollutionBoiler Turbine

Turbo Generator

Steam Condenser

Condensate Pump

LPFHGHPFHG

Deaerator

Feedwater Pump

#1 #2 #3

steam

rege

nera

tive

extra

ctio

n ste

am

feed

wat

er

condensated water

discharging steam

rege

nera

tive

extra

ctio

n ste

am

feed

wat

er

G

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HPFHG modelling objective Find out the relationship between the feedwater outlet temperature and other

variables

HPFHG modelling significance: [4,5,7] Find out the best working condition

Fault detection

Improve efficiency

Reduce emissions

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Industrial Background Variables for a single High-Pressure Feedwater Heater Modeling

Relative

Available

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Industrial Background High-Pressure Feedwater Heater Group

#3 #32 2,w wt P

#3 #3,h st P

#3 #31 1, ,w w wt P G#2 #2

2 2,w wt P

#2 #2,h st P

#2st

#2L

#2odt

#2 #21 1,w wt P

#1 #1,h st P#1L

#1odt

#1 #11 1,w wt P#1 #1

2 2,w wt P#3L

#3odt#3

st

4 Feb 2020 13

Industrial Background High-Pressure Feedwater Heater Group

#3 #32 2,w wt P

#3 #3,h st P

#3 #31 1, ,w w wt P G#2 #2

2 2,w wt P

#2 #2,h st P

#2st

#2L

#2odt

#2 #21 1,w wt P

#1 #1,h st P#1L

#1odt

#1 #11 1,w wt P#1 #1

2 2,w wt P#3L

#3odt#3

st

A Shared Variable

4 Feb 2020 14


#3 #32 2,w wt P

#3 #3,h st P

#3 #31 1, ,w w wt P G#2 #2

2 2,w wt P

#2 #2,h st P

#2st

#2L

#2odt

#2 #21 1,w wt P

#1 #1,h st P#1L

#1odt

#1 #11 1,w wt P#1 #1

2 2,w wt P#3L

#3odt#3

st

High-Pressure Feedwater Heater Group

Cascade variable pairs

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#3 #32 2,w wt P

#3 #3,h st P

#3 #31 1, ,w w wt P G#2 #2

2 2,w wt P

#2 #2,h st P

#2st

#2L

#2odt

#2 #21 1,w wt P

#1 #1,h st P#1L

#1odt

#1 #11 1,w wt P#1 #1

2 2,w wt P#3L

#3odt#3

st

High-Pressure Feedwater Heater Group

Cascade variable pairs

Industrial Background Variables for Single Heater #3 / #2 / #1 Modeling

#3 #32 2,w wt P

#3 #3,h st P

#3 #31 1, ,w w wt P G#2 #2

2 2,w wt P

#2 #2,h st P

#2st

#2L

#2odt

#2 #21 1,w wt P

#1 #1,h st P#1L

#1odt

#1 #11 1,w wt P#1 #1

2 2,w wt P#3L

#3odt#3

st

4 Feb 2020 16

4 Feb 2020 17

Industrial Background Variables for HPFHG Modeling

#3 #32 2,w wt P

#3 #3,h st P

#3 #31 1, ,w w wt P G#2 #2

2 2,w wt P

#2 #2,h st P

#2st

#2L

#2odt

#2 #21 1,w wt P

#1 #1,h st P#1L

#1odt

#1 #11 1,w wt P#1 #1

2 2,w wt P#3L

#3odt#3

st

4 Feb 2020 18

Contents

Introduction




Conclusions

4 Feb 2020 19


Architecture

Consists of 3 subnets

o net #3 HPFH #3

o net #2 HPFH #2

o net #1 HPFH #1

shared input

hidden layer of #3

output of #3

hidden layer of #2

other inputs of #1

output of #2

hidden layer of #1

output of #1

other inputs of #2

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Loss function: multi-task learning

Jointly training net #3, #2 and #1o Modeling the heater group as a whole √


4 Feb 2020 21

Contents

Introduction




Conclusions

4 Feb 2020 22


Experimental Data collected from a thermal power unit whose capacity is 1000MW

collected over a month without interruption

sampling interval is 5 minutes

m=10081

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Performance Evaluation Criteria

4 Feb 2020 24


Experimental Setting

1

1

Overfitting strategy: early stop

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Contrast experiment

A “black box” ANN model with three hidden layers

1x

2x

nx

( )h xΘ

( 2)(2)n

a

(3)1a

(3)2a

(3)(3)n

a

(2)1a

(2)2a

(4)1a

(4)2a

( 4)(4)n

a

(5)1a

4 Feb 2020 26The proposed method The ‘black box’ ANN

HPFH #3

HPFH #2

HPFH #1

HPFH G

HPFH #3

HPFH #2

HPFH #1

HPFH G

Outlet temperature Percentage Error Outlet temperature Percentage Error

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Experiments and Results results comparison

4 Feb 2020 28

Contents

Introduction




Conclusions

4 Feb 2020 29

Conclusions

Contributions Defined an industrial application problem

Provided a data-driven hierarchical neural network modeling approach to model HPFHG and each single HPFH at the same time

The proposed model can be used to find out the best operating condition, detect system faults, save fuel and reduce pollution.

4 Feb 2020 30

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