modelling of fireside corrosion of superheaters and ... · fireside corrosion of superheaters and...

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Modelling of Fireside Corrosion of Superheaters and Reheaters Following Coal and Biomass Combustion

12th ECCRIA ConferenceCardiff University, Cardiff, UK

5th – 7th September 2018

B. Ekpe, J. Sumner, N.J. Simms

© Cranfield University2

OVERVIEW

Data Collection and Gathering:

• Collate corrosion dataset

• UK Coals

• World Traded Coals

• Include more information (Alloy composition, fuel composition)

• Corrosion Dataset

Data Analysis / Model Development:

• Principal Component Analysis / Principal Component Regression

• Partial Least Squares Regression

Model Validation

Introduction

• Fireside corrosion

• Review of existing corrosion models

Corrosion Dataset

• Fuel: UK coals / biomass

• Fuel: World traded coals (US, EU)

• Materials: Austenitic, ferritic, nickel-based alloys, coatings and claddings

Data Analysis / Model Development


• Partial Least Squares

• Model suite


PULVERISED COMBUSTION BOILER

www.tbwes.com/products/subcritical-boilers/


FIRESIDE CORROSION:Deposition on Superheater / Reheater Tubing

Vapour species

Condensation onto solid particles &

aerosolsSolid particles &

aerosols

Water / steam

Corrosion

Heat transfer

SOx HCl

00

Thermophoresisof fine particles

Condensation into deposit Vapours, SOx & HCl

diffuse into porous deposits

Coarse particles stick

Reference: Simms et al., 2007

© Cranfield University5 © Cranfield University5

FIRESIDE CORROSION:High Temperature Corrosion Mechanism

Temperature

Corrosion

damageStable

deposit

Deposit

unstable

Oxidation damage

Temperature

Corrosion

damageStable

deposit

Deposit

unstable

Oxidation damage

CORROSIVE DEPOSITS:

• Sulphate deposits (pyrosulphates, alkali

iron trisulphates, mixed sulphates)

• Chloride deposits

• Sulphates-Chlorides-Carbonates (mixed)

DEPOSIT INSTABILITY:• Vapour condensation dewpoints

• Insufficient SO3 available to stabilise

some phases

• Other phases more stable with

temperature change

Reference: Simms et al., 2007


1. Borio Index: The index value was derived from a nomograph defining factors including

acid-soluble K2O, Na2O, CaO and MgO, and Fe2O3. The coal corrosivity index derived was

of the range 0 - 22 with increasing index denoting increased corrosion.

2. Raask Index: i.e. < 0.5 wt % (Na + K) denotes low coal corrosivity; and > 1.0 wt % (Na +

K) denotes high coal corrosivity.

3. PE-8 model: 𝐶𝑜𝑟𝑟𝑜𝑠𝑖𝑜𝑛 𝑟𝑎𝑡𝑒𝑛𝑚

ℎ𝑟= 𝐴𝐵( 𝑇𝑔

𝐺)𝑚[

𝑇𝑚−𝐶

𝑀]𝑛 %𝐶𝑙 − 𝐷

4. Modified PE-8 model:

𝐶𝑜𝑟𝑟𝑜𝑠𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 𝑛𝑚 ℎ𝑟 = 𝑒𝑥𝑝[( 𝑟 𝑇𝑚) + 𝑠 %𝐶𝑟𝑎𝑙𝑙𝑜𝑦 + 𝑡 𝑇𝑔 − 𝑇𝑚 + 𝑢 + 𝑣 %𝐶𝑙𝑓𝑢𝑒𝑙 ]

5. Laboratory Test Equation:

𝐶𝑜𝑟𝑟𝑜𝑠𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 𝑛𝑚 ℎ𝑟

= exp[( 𝑎 𝑇𝑚) + 𝑏 + 𝑐 𝐻𝐶𝑙 + 𝑑 𝑆𝑂𝑥 + 𝑒(𝑁𝑎 + 𝐾)𝑑𝑒𝑝%+ 𝑓 %𝑆𝑑𝑒𝑝 + 𝑔 %𝐶𝑙𝑑𝑒𝑝 ]

EXISTING CORROSION MODELS

References: Borio et al., 1968[1]; Clapp, 1991[2]; James et al., 2007[3,4, 5]; Lant et al., 2010[3,4]; Wright and

Shingledecker, 2015[1,2,3]


OVERVIEW



• UK Coals







Model Validation

Introduction



Corrosion Dataset







• Model suite

8

ALLOYS Austenitic, Ferritic, Coatings/Claddings

FUEL Coal (UK, US, EU); Biomass

OPERATING CONDITIONS Metal Temperature, Gas Temperature

TUBE POSITION Leading (facing gas flow), Non-leading

CORROSION DATASET Input Parameters from Plant Data


OVERVIEW



• UK Coals







Model Validation

Introduction



Corrosion Dataset







• Model suite


MODEL DEVELOPMENT: Principal Component Analysis / Partial Least Squares

Reference: PCA illustration - Transformation of

input data using PCA (University of Illinois, 2000)

\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\PCA is employed to

reduce the

dimensionality of a

set of variables while

retaining maximum

variance

PLS extracts linear functions

of the predictor dataset that

has maximum covariance with

the dependent variable

(corrosion rate)


MODEL STRUCTURE

Arrhenius Equation:

Taking the natural logarithm on both sides gives: 𝑰𝒏 𝑲 = 𝑰𝒏 𝑨 −𝑬𝒂

𝑹𝑻

Kelvin Temperature

Pre-exponential Factor

Activation Energy

Rate Constant

Gas Constant

𝐾 = 𝐴𝑒−𝐸𝑎𝑅𝑇

𝑰𝒏 𝒚 = 𝑰𝒏( 𝜷𝒐 ) + 𝜷𝟏𝒙𝟏𝒊 + 𝜷𝟐𝒙𝟐𝒊… .……+ 𝜷𝒌𝒙𝒌𝒊… .+ 𝜺𝒊

1000/Metal temperature , Tm

(1000/K)

Alloy composition (wt %)

Fuel composition (wt %)Constant

Corrosion rate (µm.(khr)-1)

Residual error


PRINCIPAL COMPONENT ANALYSIS

Input dataset mean centered and standardised

Variance (eigenvalue) and weight vector

(eigenvector) computed from

correlation matrix

Principal components with significant

eigenvalues selected via a selection

criterion

Original variables mapped onto the new

coordinated space

Principal component regression using

selected PCs

Variables with high loadings on selected

PCs added to the regression model

Standardised regression coefficients

transformed to original

(unstandardized) form

Prediction model


PARTIAL LEAST SQUARES

PLS components selected via cross

validation

PLS weights extracted to build

the model and used later for

variable selection

PLSR model is fitted to the data

Variable selection is carried out by

introducing a threshold

(VIP Scores)

Prediction model generated

Reference (VIP scores): Mehmood et al (2012); Farres et al (2015)


Model Materials Tube

Position

Fuel

Model 1Austenitic Fe based

Leading Midland and North Eastern

Region UK Coals

Model 2 Non-leading

Model 3Ferritic

Leading Midland and North Eastern

Region UK CoalsModel 4 Non-leading

Model 5 Austenitic Fe based

Position not

specified

Biomass - Clean wood

pellets, waste wood and

forestry waste

Model 6 Ni based alloys

Model 7 Ferritic

Model 8 Coatings and claddings

Model 9 Austenitic Fe based

US Coal – Eastern and

Western Bituminous coalModel 10 Ni based alloys

Model 11 Coatings and claddings

Model 12 TP316, HR3C10-20% Wood/CCP, Daw

Mill Coal

MODEL SUITE


PLS – VARIABLE SELECTION FOR MODEL BUILDING

(MODEL 1, UK COAL DATA)

0.0

0.5

1.0

1.5

2.0

10

00

/Met

al T

em

p (

100

0 /

K)

(Tg

-Tm

) (°

C)

Car

bo

n (

%)

Man

gan

ese

(%)

Ph

osp

ho

rus

(%)

Sulp

hu

r (%

)

Silic

on

(%

)

Ch

rom

ium

(%

)

Nic

kel (

%)

Mo

lyb

den

um

(%

)

Tita

niu

m (

%)

Van

adiu

m (

%)

Na

(%)

K (

%)

Mg

(%)

Ca

(%)

Al (

%)

Fe (

%)

Si (

%)

Cl (

%)

S (%

)

Ti (

%)

VA

RIA

BLE

IMP

OR

TAN

CE

IN P

RO

JEC

TIO

N



(MODEL 5, UK BIOMASS DATA)

0.0

0.5

1.0

1.5

2.01

00

0/M

etal

Te

mp

(1

000

/ K

)

(Tg-

Tm)

°C

Car

bo

n (

%)

Man

gan

ese

(%)

Ph

osp

ho

rus

(%)

Sulp

hu

r (%

)

Silic

on

(%

)

Ch

rom

ium

(%

)

Nic

kel (

%)

Mo

lyb

den

um

(%

)

Nio

biu

m (

%)

Tita

niu

m (

%)

Van

adiu

m (

%)

Nit

roge

n (

%)

Co

pp

er (

%)

Tun

gste

n (

%)

Bo

ron

(%

)

Alu

min

ium

(%

)

Co

bal

t (%

)

Na

(%)

K (

%)

Mg

(%)

Ca

(%)

Al (

%)

Fe (

%)

Si (

%)

Cl (

%)

S (%

)

Ti (

%)

P (

%)

Ba

(%)

Mn

(w

t%)

VA

RIA

BLE

IMP

OR

TAN

CE

IN P

RO

JEC

TIO

N



(MODEL 9, US COAL DATA)

-0.4

0.1

0.6

1.1

1.6

2.1

2.61

00

0 /

Met

al T

em

p (

10

00

/ K

)

(Tg-

Tm)

(°C

)

Car

bo

n (

%)

Man

gan

ese

(%)

Ph

osp

ho

rus

(%)

Sulp

hu

r (%

)

Silic

on

(%

)

Ch

rom

ium

(%

)

Nic

kel (

%)

Mo

lyb

den

um

(%

)

Nio

biu

m (

%)

Tita

niu

m (

%)

Co

pp

er (

%)

Van

adiu

m (

%)

Alu

min

ium

(%

)

Nit

roge

n (

%)

Cer

ium

(%

)

Tun

gste

n (

%)

Bo

ron

(%

)

Tan

talu

m (

%)

Na

(%)

K (

%)

Mg

(%)

Ca

(%)

Al (

%)

Fe (

%)

Si (

%)

Cl (

%)

S (%

)

Ti (

%)

P (

%)

Mn

(%

)

VA

RIA

BLE

IMP

OR

TAN

CE

IN P

RO

JEC

TIO

N



(MODEL 12, UK CO-FIRING DATA)

0.0

0.5

1.0

1.51

00

0/M

etal

Te

mp

(1

000

/(K

))

Car

bo

n (

wt%

)

Silic

on

(w

t%)

Ch

rom

ium

(w

t%)

Nic

kel (

wt%

)

Nio

biu

m (

wt%

)

Nit

roge

n (

wt%

)

Mo

lyb

den

um

(w

t%)

Na

(%)

K (

%)

Al (

%)

Si (

%)

P (

%)

Mg

(%)

Ca

(%)

Ti (

%)

Mn

(%

)

Fe (

%)

Ba

(%)

Cl (

%)

S (%

)

VA

RIA

BLE

IMP

OR

TAN

CE

IN P

RO

JEC

TIO

N


COMPARISON OF PREDICTED AND MEASURED CORROSION RATES USING PLS

Austenitic (Coal_UK)

Austenitic (Biomass_UK)Austenitic (Biomass)

Austenitic (Biomass_UK)

Austenitic (Coal_UK) Austenitic (Biomass_UK)

R² = 0.85

0

1

2

3

4

5

6

0 1 2 3 4 5 6Pre

dic

ted

rat

es (

µm

/kh

r)

Measured rates (µm/khr)

Model 1 (UK Data)

R² = 0.73

0

1

2

3

4

5

6

0 1 2 3 4 5 6Pre

dic

ted

rat

es (

µm

/kh

r)


Model 2 (Biomass Data)

R² = 0.90

1

2

3

4

5

6

1 2 3 4 5 6 7 8Pre

dic

ted

rat

es (

µm

/kh

r)


Model 3 (Co-firing Data)

R² = 0.75

1

2

3

4

5

6

7

1 2 3 4 5 6 7Pre

dic

ted

rat

eS (

µm

/kh

r)


Model 4 (US Data)

Model 1 Model 5

Model 12Model 9


CONCLUSIONS• The analysis suggests a sulphur dominated fireside corrosion dependent on the fuel Cl and

Na levels in UK data; for biomass data (comprising mainly wood pellets), alkali sulphate

interaction with fireside corrosion dependent mainly on K, Ca and Ti; a fireside corrosion

model dependent on Cl, K, Mg, S and P in co-firing data, and the corrosion model from US

data is dependent mainly on the fuel’s sulphur content.

• Variable selection for fireside corrosion models is in line with conclusions from previous

investigations – for instance; chloride influence in UK coals.

• PLS on average produced better predictive models than PCA, and with fewer latent

variables compared to PCA. On PCA, the more latent variables (or PCs) selected, the

better the model performance; this is not the case for PLS so there is less chance of

overfitting the data.

• PCA also includes more predictor variables with negligible effects on the corrosion rate

compared to PLS


• Biomass and Fossil Fuel Research Alliance (BF2RA)

• Niger Delta Development Commission (NDDC)

• Unpublished data from Doosan, E-ON/Uniper, EPRI

• Supervisors – Professor Nigel Simms

– Dr. Joy Sumner

ACKNOWLEDGEMENTS


[email protected]

[email protected]

[email protected]

modelling of fireside corrosion of superheaters and ... · fireside corrosion of superheaters and...

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