regenerator design study - glass service · regenerator design study ... to confirm correlation of...
TRANSCRIPT
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
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
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Porous wall T°
Fluid T°
Model validation
Generally good agreement between model and measurements for
11
,
,1
inFumes
outAir
T
TEff
outAirT ,
inFumesT ,
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.
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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.
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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
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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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
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
� = � + � ���� + � � ��� ��������� Y
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…
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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
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