how the batch preheating system can affect the glass ...€¦ · how the batch preheating system...

How the Batch Preheating System Can Affect theGlass Current Flow in the Melting Furnace

Mahdie Moaveni

14th

International seminar on furnace design operation & Process simulation 1

SIMULLEX GmbH provides technical consulting to glass manufacturing companies, furnace suppliers, as engineering and project management companies involved in Glass Industries worldwide.Our competence is mainly computer based – CFD simulation of all glass melting and conditioning processes.

Introduction 2

Goal of the Modeling study is to get

knowledge regarding the temperature

profile, the currents in the glass bath and the

combustion space of the glass melting

furnace. 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.

It is useful in the elementary stage of new

furnace concept developments as well as for

the performance optimization of existing

furnaces.

Modeling study 3

Application of Batch preheating system

Application of the Batch and Cullet Preheating System as a reasonable energy recovery from waste gas since several decades in the glass industry

4


Batch &

Cullet

Preheating

System

Significant amount of

energy saving by keeping

constant pull rate

Increase of pull rate

by keeping same

amount of energy

consumption

Increase of furnace life time by

keeping constant pull rate

-Replacement of some pre-

reactions of the batch into the

preheater (e.g. water

evaporation) lower

temperatures in the furnace

dust carry-over

-batch pre-treatment

-surface glazing, closed dog

house

-regenerator cleaning

Emission reduction

(NOx, SOx) due to

fossil energy input

decrease

5


Sufficient operational data reported by different Batch Preheating System (BPS) suppliers depict 12-14% of energy saving in case of batch preheating up to 250-300°C

Data from Zippe Industrieanlagen GmbH

Specific Energy: appr. 3100 kJ/kg

Cullet: appr. 80%

Proportion Boost: appr. 7 %

6


• Most of the experimental information are based on

✓ Energy consumption saving

✓ (more or less) dust carry over

• BUT: Aggressive process on account of very high temperatures allows less experimental

investigations about the effect of BPS on the glass current flow in the furnace; The process

parameters are virtually impossible to measure

• Even the particle trace test can merely give some information about the residence time but not

about the path of particles and other quality factors

Evaluation of processes with BPS

7

Role of the CFD-Simulation for the evaluation

•Realization of the

impact of BPS on

glass current,

temperature

distribution, glass

quality

Investigation

of process

parameters

•Consideration of

overall impact of

furnace features

•Also in

combination with

other elements in

furnace like

boosting and

bubbling

Impact of

BPS

•Optimum furnace

design

•Efficient

implementing of

BPS

Aim of the

investigation

Modeling Study 8

Modeling Study

Model Study

End Fired Furnace Green

Glass

Case I

119 sqm; 370 tpd

Case I_V 01

Side Wall Boosting

Case I_V 08

Side Wall Boosting

+ Batch Preheating

Case I_V 02

Melting Bottom

Boosting

Case I_V 07

Melting Bottom

Boosting

+ Batch Preheating

Case I_V 03

Side Wall & Barrier

Boosting

Case I_V 06

Side Wall & Barrier

Boosting

+ Batch Preheating

Case I_V 04

Side Wall Boosting &

Bubbling row

Case I_V 05

Side Wall Boosting &

Bubbling row

+Batch Preheating

Case II

170 sqm; 450 tpd

Case II_V 01

Side Wall & Barrier

Boosting

Case II_V 03

Side-Bottom & Barrier

Boosting

9

Case I_V 01 (Cold Batch)

&

Case I_V 08 (Preheated

Batch)

12 Side Electrodes

Case I_V 02 (Cold Batch)

&


Batch)

12 Melting Bottom

Electrodes

Case I_V 03 (Cold

Batch) &


Batch)

6 Side Electrodes &

10 Barrier Electrodes

Case I_V 04 (Cold

Batch) &

Case I_V 05

(Preheated Batch)

6 Side Electrodes

&

10 Bubbling

Nozzles

Case study 10

General 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 surfacet/sqm 3.11

Doghouse 2 Side wall

Burners 3 (each Port)

General geometry description of all cases

11

Case study

Nm³/h

KW

KJ/Kg

% 8

% 72

% 12,34

12

Comparing the temperature distribution in melting tank and combustion space of the furnace withpreheated batch/ cold batch, considering reduced energy consumption with focus on:

Evaluation of the temperature distribution

✓ Desired riser temperature

✓ Proper bottom temperature

✓ Crown temperature over the wall

Temperature Distribution 13

Temperature Distribution

Boosting Configuration: 12 Side Electrodes

Temperature (°C); Glass melting tank model

Temperature (°C); Combustion model

Cold Batch

PreheatedBatch

14

Average glass Temperature of a slice 50 mm from the bottom along the furnace length

Boosting Configuration: 12 Side Electrodes

Melting areaRiser Temperature

Crown Temperature

Batch Preheating up to 250 °C in case ‘’Side Electrodes configuration’’:➢ Energy consumption reduction up to 12.5%➢ Achieving same riser temperature➢ Lower bottom temperature about 5 to 10 °C➢ Increase of crown temperature over the wall up to 4 °C

Temperature Distribution 15

Temperature distribution

Boosting Configuration: 12 Bottom Electrodes



Cold Batch

PreheatedBatch

16


Boosting Configuration: 12 Bottom Electrodes

Melting area

Riser Temperature

Crown Temperature

Batch Preheating up to 250 °C in case ‘’Bottom Electrodes configuration’’:

➢ Energy consumption reduction up to 12.5%

➢ Higher riser temperature up to 8 °C

➢ Lower bottom temperature about +3 to -7 °C

➢ Increase of crown temperature over the wall up to 4 °C

Temperature distribution 17

Temperature distribution

Boosting Configuration: 6 Side Electrodes & 10 Barrier Electrodes



Cold Batch

PreheatedBatch

18




Crown Temperature

Batch Preheating up to 250 °C in case ‘’Side & Barrier Electrodes configuration’’:



➢ Nearly same range of temperature +/- 2 °C



Temperature distriution




Cold Batch

PreheatedBatch

20


Boosting Configuration: 6 Side Electrodes & 10 Bubbling Nozzles


Crown Temperature

Batch Preheating up to 250 °C in case ‘’Side & Barrier Electrodes configuration’’:



➢ Nearly same range of temperature +/- 2 °C



Convection Flow; Glass Streamlines; Temperature Profile

Side Boosting Side & Barrier Boosting

Side Boosting & Bubbling

Bottom Boosting

22

Glass Quality Analysis

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 Dr1/3 )/ (3(0.012/3))dt

Quality Analysis 23

Quality Analysis

Residence time is simply the time which the particle spent on its way between the starting point (batch chargers) and the target planes.

The case with Side & Barrier Boosting shows the highest min. residence time of critical particles with and without preheating system. Furthermore the effect of BPS on Residence time in this case is higher than all other cases.

24

Quality Analysis

Electrode Side Boosting; The path of 10 random fastest particles with minimum residence time.

Temperature (°C); Path of trace particles Temperature (°C); Path of trace particles

25

Quality Analysis

Bottom Boosting; The path of 10 random fastest particles with minimum residence time.


26

Quality Analysis

Side & Barrier Electrodes; The path of 10 random fastest particles with minimum residence time.


27

Quality Analysis

Side Electrodes & Bubbling ; The path of 10 random fastest particles with minimum residence time.


28

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 dependson the type of glass.

The Case with Side & Barrier Boosting shows the highest Melting Index of critical particles. Moreover this electric configuration in combination with BPS has the most positive effect on quality of melting.

29

Quality Analysis

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

30

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.

The case with bottom boosting shows the highest values of Sand Dissolution index. Subsequently Case with side & barrier boosting. and the lowest value is related to the Case with Side Boosting.

31

Quality Analysis

Bubble growth index is a refining indicator which depicts the speed of growth and dissolution of the bubbles in the glass melt.

The case with bottom boosting shows the highest values of Sand Dissolution index. Subsequently Case with side & barrier boosting. and the lowest value is related to the Case with Side Boosting.

32

Conclusions:

• Revelation of the Case Study: by preheating up to 250 °C, a specific energy reduction up to appr. 12.5% isachievable

• Generally: improvement of glass quality indicators with BPS implementation observable

• Integrating the BPS + Side & Barrier Boosting

➢ highest value of residence time of critical particles & a more reasonable path of particles achievable

➢ Other indicators (e.g. mixing/melting index & bubble growth) have in the case of Barrier Boosting also thehighest value

➢ But: The Sand Dissolution Index has the highest value in the case with Bottom Electrodes

• Temperature Distribution in the cases of Side Electrodes & Bottom Electrodes: inspite of high riser & crowntemperature → slight decrease of bottom temperature

• In Case of Barrier Boosting or Bubbling implementation still suffiecient bottom temperature can be achieved

→ on account of Convection Current in melting area

• Consequently, it is recommended to apply a vertical element (preferably Barrier Boosting) in front of the wall if a BPS implementation is desired

Conclusions 33

Conclusions:

• Nonetheless, these results are related to the case study with the mentioned boundary conditions, which meansthat for an optimum solution, individual parameters should be taken into account

Conclusions 34

Thanks for your attention!

Dipl.-Ing. Mahdie Moaveni

SIMULLEX GmbHThomastrasse 7,92665 AltenstadtGermanyPhone: +49 9602 6175448Fax: +49 9602 6175449Mobile: +49 151 1213 0275Email: moaveni @simullex.comInternet: www.simullex.com

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