Regenerator design study combining numerical simulations and statistical tools

14th Int. Seminar on Furnace Design - Operation & Process Simulation

June 20-21 2017

Z. Habibi, F. BioulAGC Glass Europe – Technovation Center

Gosselies, Belgium www.agc-glass.eu

1

Introduction

AGC and float glass… We use heat from combustion for glass production:

To melt raw materials and to refine the glass melt

Combustion air is preheated by means of regenerators

Total energy Input = 40-50 MWRaw materials

Molten glass to tin bath (float)(1100°C)

2

Design optimization

What is the best design?

In term of what?

Is there only one?

Could we reach it?

How to reach it?

3

Regenerator optimization

Key challenges

Build a simple realistic model estimating regenerator efficiency

Include in the model information related to clogging/ageing

Objective

Build a simplified tool in order to optimize regenerator design under constraints

4

Outline

Methodology

Numerical model, description and validation

Statistical analysis and optimization

Conclusions

5

Height Width… Steam Insulation

Design of experiment & validation

Methodology: General overview in 5 steps

Numerical validation Industrial measurements

Parameters identification

Model setup and validation

DOE & Numerical simulations

Statistical analysis

Optimization Best possible

designs

6

Model setup and validation

7

Study description – Simulations

Study based on a complete model

Separate/Unique/Twin designs considered

7 burners

Including combustion space

8

Assumptions and model inputs

Steady simulation

Glass No glass model (To reduce simulation cost)

Fixed glass and batch T°

Incl. batch gases

Combustion Global power is a tuning parameter

Fire curve is fixed: gas flow rate distribution is fixed

Lambda or air/gas ratio is fixed

Air flow distribution

Fixed for separated chambers

Calculated for unique chambers

Fumes distribution calculated

9

Fixed boundary condition

Assumptions and model inputs

Regenerators No detailed checkers geometry:

approximated by porous wall model

Reversal process modelling: Quasi-steady simulation - Transient regenerator simulation is time consuming

i.e. Checkers T° is time averaged between firing and exhaust sides

10

Porous wall T°

Fluid T°

Model validation

Generally good agreement between model and measurements for

11

,

,1

inFumes

outAir

T

TEff

outAirT ,

inFumesT ,

12

Identification of Parameters & Evaluation Criteria

Parameters included in the study

Study description - Parameters

13

Regenerator ConfigurationUnique / Separated / Twin

Flow distributionFront / Back / Front & Back / Side

Fumes flow rate

Regenerator Insulation

Selected parameters during the study to generate the optimisation tool

Other parameters have not been included because either less impacting or with lower priority

Study description - Evaluation criteria

Selected evaluation criteria

Regenerator efficiency

Combustion efficiency

Clogging indexcf. bellow.

14

70%)~max (usual, )(

)(

)%100 reach (can

,

,

,,

,,2

,

,1

inFumesFumesFumes

outAirAirAir

inFumesinFumes

outAiroutAir

inFumes

outAir

TqCp

TqCp

TH

THEff

T

TEff

combustion

Glasscomb

Q

QEff

outAir

outAir

H

T

,

,

inFumes

inFumes

H

T

,

,

combustionQ

GlassQ

Study description - Clogging index Proposed sulfate clogging index

Based on literature (Beerkens), a simplified index which can estimate deposition rate of sodium sulfate to the checkers has been defined

An approximation of clogging function is implemented in GFM

Thanks to the « field manipulation » tool of GFM

Validation To confirm correlation of the index & reality, alkali concentration level difference

between top and bottom level of the regenerator have been measured and compared with estimated index.

15

dc

Expectation: dc should be relatively larger due to more alkali loss by creating sodium sulfate deposition to the checkers

Expectation: dc should be relatively smaller

c:Alkali concentration level

16

Design of ExperimentNumerical simulation

Methodology: Design Of Experiment

Step by step approach

17

Step Goal Information Model

ScreeningSelection keyparameters

- General trend- Design well centered- KO for optimization

ModelingFirst model(with most relevant key parameters)

- Full model- Interaction- OK for optimization

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Sequential methodology to build model step by step by keeping previous trials

Set of experiments

Case # Height Width …

1

2

3

…

N

Case #Effi-

ciencyHeat to glass

Clog

1

2

3

…

N

Evaluation Criteria

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Methodology: DOE & Numerical simulation

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Numerical simulations

Based on complete furnacemodel (incl. Combustion)

Set of experiments

Case # Height Width …

1

2

3

…

N

Case #Effi-

ciencyHeat to glass

Clog

1

2

3

…

N

Evaluation Criteria

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Simulation cases

Study description – Simulations

1) Selection of most impacting parameters

2) Generation of the simplified model

Remark: Range of combustion efficiency 10% Significant impact

Evaluation criteria: • Regenerator efficiency

(Temperature, Enthalpy)

• Combustion efficiency

• Sulfate clogging index

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20

Statistical analysis

Statistical Analysis

21

Statistical tools

Set of experiments

Numerical simulations

Case # Height Width …

1

2

3

…

N

Case #Effi-

ciencyHeat to glass

Clog

1

2

3

…

N

Evaluation Criteria

Based on complete furnacemodel (incl. Combustion)

Used to define the set of experiments and generate the simplified model

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Statistical analysis

Interactions

Effect of one parameterdepends on the level of otherparameters

Final approximation

22

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Physical understanding

Conclusions are also derived from physical interpretation of the results

Example:

Fumes distribution between different chambers configuration

23

24

Best possibledesigns

Optimization

25

Statistical tools

Set of experiments

Numerical simulations

Case # Height Width …

1

2

3

…

N

Case #Effi-

ciencyHeat to glass

Clog

1

2

3

…

N

Evaluation Criteria

Based on complete furnacemodel (incl. Combustion)

Used to define the set of experiments and generate the simplified model

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Optimization underconstraints

Efficiency Ageing

Optimization

Optimum is a balance between efficiency and ageing

Results show that there is not only one absolute optimum

Optimization depends on constraints

Dimensions

(cold repair, building size…)

Civil work cost

Refractories cost

Optimization is constantly evolving

in term of geo-economical factors,

Investment strategies, know-how…

26

Con

stra

int1

Constraint 2

Low efficiency Higher

efficiency

Conclusions

Thanks to numerical simulation (CFD) we were able to study the impact of 12 parameters simultaneously on regenerator efficiency and clogging index (Impossible to realize without simu).

GFM simulation tool has a good ratio Accuracy x CPU-cost x Model setup

Design of experiments allows us to optimize the number of simulation runs

Statistical analysis shows the impact of the parameters and their interactions on regenerators

efficiency.

allows the building of a simplified function of efficiency in term of regenerator parameters

Physical interpretation of the results improves our understanding of regenerators

Optimization of the efficiency depends on constraints: civil works, refractories cost…etc. no unique optimal design

27

Thank you for your attentionQuestions?

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