Effective Implementation of Electric Boosting In

Glass Melting Furnace Mahdie Moaveni

2017 ICG Annual meeting & 32nd

sisecam symposium, Istanbul

SIMULLEX GmbH is a consulting and engineering company based in Germany,

specialized in glass melting furnaces.

Our competence is mainly the conduction and evaluation of studies of the melting

concept using the CFD simulation of melting and conditioning processes of all

types of glass.

Introduction 2

The challenges that are posed to today’s competitive glass industry are

environmental emission regulations, higher performance and energy saving.

Precise solutions for each furnace design are required to meet glass

production’s requirements and overcome these challenges.

Advanced computer modeling and an analysis of glass melting furnaces are

reliable tools, which allow you to forecast and to visualize effect of any changes

in geometrical and operational parameters prior to implementation.

Side Wall Boosting

Melting Bottom Boosting

Barrier Boosting

The electric boosting, plays an important role in increasing output capacity in a great number of fossil

fired glass melting furnaces. The benefits of boosting can only be realised with well-designed and

correctly positioned boosters.

Electric Boosting

Increasing output capacity

Fine-tuning of glass quality

Fast stabilizing the bottom temperature

Flexibility of furnace operation

With Pull rate & Color Variation

Emission reduction

Electric Boost Implementation in Fossil Fired Melting Furnaces 3

Electric Boost Implementation in Fossil Fired Melting Furnaces 4

Well-designed boosting system

Improvement of furnace performance efficiency; Higher productivity

Optimum convection flow in melting area

Proper bottom and riser temperature

Glass quality improvement

Crucial parameters of suitable boosting system design

Type of product / glass composition

Furnace geometry; Splitting concept of energy (Electricity & Fossil)

Presence of other elements instance of bubbling, preheating system, etc.

Experience of furnace designer and furnace operator

Investment & Operation costs e.g. No. of transformers, No. of electrodes, electrode cooling, etc.

Difficulties during maintenance

Mathematical analysis; CFD Simulation

Modelling Case Study

Model Case Study

Case I

119 sqm, 370 tpd, green glass

Case I_V 01

Side Wall Boosting

Case I_V 02

Melting Bottom Boosting

Case I_V 03

Side Wall & Barrier Boosting

Case II

170 sqm, 450 tpd, green glass

Case II_V 01

Side Wall & Barrier Boosting

Case II_V 03

Side-Bottom & Barrier Boosting

Modelling case study:

1. 119 sqm end fired furnace; 370 tpd

2. 170 sqm end fired furnace; 450 tpd

5

Case I; General geometry Description

14000

8500

Pull Rate tpd 370

Product Container Glass

Color green

Cullet % 60

Length mm 14000

Width mm 8500

Melting Surface sqm 119

Melting Depth mm 1200

Ref. Depth mm 2200

Specific Melting

surface t/sqm 3.11

Doghouse 2 Side wall

Burners 3 (each Port)

Natural Gas Nm³/h 1150

Electric Boosting kW 1600

Energy Consumption Kcal/Kg 753

Energy Consumption KJ/Kg 3153

6

Case I

Case I; Boosting Configuration

Case I_V 01

Total Electric power: 1600 KW

12 Side Electrodes

Case I_V 02

Total Electric power: 1600 KW

12 Melting Bottom Electrodes

Case I_V 03

Total Electric power: 1600 KW

6 Side Electrodes (60% El. Power)

10 Barrier Electrodes (40 % El.

Power)

7

Evaluation of Case Study

Temperature distribution in glass batch and superstructure

• Desired riser temperature

• Proper bottom temperature

• Crown temperature over the wall

Convection flow in melting area

Glass quality factors

Evaluation 8

Temperature (°C) Glass Tank Model

Temperature (°C) Combustion Model

Case I; Temperature Profile

Case I_V 02

Melting Bottom Boosting

Highest bottom temperature

Lowest riser temperature

Case I_V 01

Side Boosting

Lowest bottom temperature

Highest riser temperature

Case I_V 03

Side & Barrier Boosting

About 20-25 °C higher bottom

temperature in compare to Case I_V 01

Lower crown temperature over the wall

9

Case I; Convection Flow; Glass Streamlines; Temperature Profile

Case I_V 01

Side Boosting

Case I_V 02

Melting Bottom

Boosting

Case I_V 03

Side & Barrier

Boosting

10

Case I; Bottom Temperature

Average Temperature of a slice 50 mm from the bottom

along the furnace length

Bottom Temperature (°C) (Refractory)

Case I_V 01

Side Boosting

Case I_V 02

Melting Bottom

Boosting

Case I_V 03

Side & Barrier

Boosting

11

Case I; Temperature profile

Case I_V 01

Side Boosting

Case I_V 02

Melting Bottom

Boosting

Case I_V 03

Side & Barrier

Boosting

12

Glass quality parameters are derived from particle tracking in the glass model.

A large number (200 000) of massless particles are released from the batch chargers and their

trajectories are calculated through the furnace until the outputs at the model end.

Calculation of the residence time for each particle in the glass model

Storage of temperature, viscosity and velocity along each trajectory calculation of the melting, fining

and mixing index

The melting index: indication of the melting quality along the trajectory: a high value means that the

particle resides for a long time in regions with high temperature and low viscosity. ∫Trajectory(T/μ)dt,

The fining index: indication of the fining quality along the trajectory: a high value means that the

particle resides for a long time in regions with temperature above a typical fining temperature and low

viscosity. ∫trajectory((T − A)b /μc)dt

The mixing index: indication of the mixing quality or the inhomogeneity dilution along the trajectory:

the value denotes how many times a 1 cm thick cord can be diluted along the trajectory.

∫trajectory(4 ||grad v|| 2/3 Dr 1/3 )/ (3(0.012/3))dt

Quality Analysis 13

Case i; Quality Analysis

Residence time is the time which the particle spent on its way between the starting point (batch chargers) and the target planes. The Case I_V 03 (Side & Barrier Boosting) shows the highest min. residence time of particles. Subsequently Case I_V 02 (Bottom Boosting) and the lowest value is related to Case I_V 01.

14

The path of fastest particles with minimum residence time

Case i; Quality Analysis

Melting index is a function of local temperature on the trajectory, of dynamic viscosity of glass and of

time. The higher the value of melting index of each trajectory, the longer time has the particle spent in

regions of high temperature and of low viscosity. Thus the melting index informs us about the quality of

melting along the trajectory. The absolute value of melting index depends on the type of glass.

The Case I_V 03 (Side & Barrier Boosting) shows the highest Melting Index

of critical particles. Subsequently Case I_V 02 (Bottom Boosting) and the lowest

Value is related to Case I_V 01 (Side Boosting).

Mixing index is an indicator of inhomogeneity dilution capabilities. It is function of local velocity gradient

tensor and diffusivity of typical cord compound (Al2O3, SiO2, and ZrO2). The results tell us how many

times a 1 cm thick cord has been diluted while it follows the massless particle trajectory.

The Case I_V 03 (Side & Barrier Boosting) shows the highest Mixing Index of critical particles.

Subsequently Case I_V 01 (Side Boosting) and the lowest value is related to Case I_V 02 (Bottom Boosting).

15

Case i; Quality Analysis

Sand dissolution index is a computation for each massless particle's trajectory which corresponds to

sand grain tracing. Statistics are performed on the resulting values. For each trajectory, the theoretical

largeness of sand grains that would be able to be dissolved is calculated. Consequently, as the sand

grain travels through the glass melt, it gradually dissolves at the rate given by the grain growth function

that depends on temperature.

Bubble growth index is a refining indicator which depicts the speed of growth and dissolution of the

bubbles in the glass melt.

The Case I_V 03 (Side & Barrier Boosting) shows the highest values in both indexes.

Subsequently Case I_V 02 (Bottom Boosting) and the lowest value is related to

Case I_V 01 (Side Boosting).

16

Case I; Conclusion

Conclusions Case I:

The Case Study shows that the 119 sqm furnace with Side Wall & Barrier Boosting results higher furnace

efficiency in comparison to the case with Side Wall Boosting and the case with Melting Bottom Boosting

regarding:

Temperature profile in melting depth

Convection flow in melting area

Glass quality

Bottom temperature

17

Is this solution still valid for the furnace with larger

dimension?

This issue has been investigated in Case II of case study.

Pull Rate tpd 450

Product

Container

Glass

Color green

Cullet % 30

Length mm 17000

Width mm 10000

Melting Surface sqm 170

Melting Depth mm 1300

Ref. Depth mm 2500

Specific Melting

Surface t/sqm 2.65

Doghouse 2 Side wall

Burners 3 (each Port)

Natural Gas Nm³/h 1600

Electric Boosting kW 1600

Energy Consumption Kcal/Kg 833

Energy Consumption KJ/Kg 3486

Case II; General Description 18

• High ratio of Fe2 /(Fe2 + Fe3)

• Low thermal conductivity

• Higher temperature gradient in glass depth

• Wide furnace

• High distance of crown in the middle to glass surface

• Weak radiation heat transfer from crown to glass surface

• More distance of side electrodes

• Deeper melting bath

Deviations from Case I: Case II

17000 10000

Case II; Boosting Configuration

Case II_V 01

Total Electric power: 1600 KW

Circle 1: 12 Side Electrodes

Circle 2: 12 Barrier Electrodes

19

Case II_V 02 Total Electric power: 1600 KW

Circle1: 8 Side Electrodes & 4 Bottom

Electrodes

Circle 2: 12 Barrier Electrodes

Temperature (°C) Glass Tank Model

Temperature (°C) Combustion Model

Case I; Temperature Profile

Case II_V 02

Side-Bottom & Barrier

Boosting

Higher bottom

temperature

higher riser temperature

Case II_V 01

Side Boosting &

Barrier Boosting

20

Case I; Convection Flow; Glass Streamlines; Temperature Profile

Case II_V 01

Side Boosting &

Barrier Boosting

Case II_V 02

Side-Bottom &

Barrier Boosting

21

Case Ii; Bottom Temperature

Average temperature of a slice 50 mm from the bottom

along the furnace length

Bottom Temperature (°C) (Refractory)

Case II_V 01

Side Boosting &

Barrier Boosting

Case II_V 02

Side-Bottom &

Barrier Boosting

22

Higher bottom temperature in the melting area in the case II_V 02 is observed.

Case II; Quality Analysis 23

Residence Time (hr)

The path of fastest particles with minimum residence time

Residence time

It’s obvious that the paths of critical particles are more or less similar and that consequently no mentionable advantage through the

relocation of 4 side electrodes to the bottom is achievable.

Case II; Quality Analysis 24

1,00

1,10

1,20

1,30

1,40

1,50

1,60

1,70

1,80

1,90

2,00

Min.Mixingindex

CaseII_V01

CaseII_V02

Regarding the melting index and the sand dissolution index, a considerable improvement can be observed in the Case with the

boosting configuration of Side-Bottom Boosting & Barrier Boosting, whereas the mixing index stays unchanged in both cases.

Melting Index Mixing Index

The results of the massless particle tracing depict that the relocation of 4 of the 12 side electrodes to the bottom has no significant

influence on the residence time, the bubble growth index as well as the fining index of critical particles.

Bubble Growth Index Sand Dissolution Index

Case ii; Conclusion

Conclusions Case II:

The Case Study shows that the 170 sqm furnace with Side-Bottom & Barrier Boosting results higher

furnace efficiency in comparison to the case with Side Wall & Barrier Boosting regarding:

Temperature profile in melting depth

Melting and sand dissolution indicators of glass quality

Bottom temperature

25

Case ii; Conclusion

Conclusions

The Case Study of two furnaces with different dimensions shows that the boosting configuration has an

important influence on the furnace performance regarding glass quality, temperature distribution and

glass current in the furnace. Hence, with the right positioning of the electrode in the Fossil-Electric

furnaces, even while keeping the same amount of energy, we are able to improve the factors referred to

above.

This study has been performed for the mentioned design parameters. Any changes in the furnace

concept regarding furnace geometry, type of glass or the usage of other elements like bubbling and

preheating system can lead to different outcomes.

The significance of individual solution designs for different furnace categories in terms of the furnace

geometry is demonstrated by the evaluation of the results of both case studies. Thus, an optimum

furnace performance can be accomplished through an effective electric boosting configuration.

26

Thanks for your attention!

Dipl.-Ing. Mahdie Moaveni SIMULLEX GmbH

Thomastrasse 7,

92665 Altenstadt

Germany

Phone: +49 9602 6175448

Fax: +49 9602 6175449

Mobile: +49 151 1213 0275

Email: moaveni@simullex.com

Internet: www.simullex.com