TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
aixergee - Process Optimization for the Cement Industry
1. Alfonsstr. 44
52070 Aachen
Tel. +49 241 4134492-50
info@aixergee.de
www.aixergee.de
Process Optimization for the Cement Industry
2. Process Optimization for the Cement Industry
What is process optimization ?
Getting better results without big investment
Who needs process Optimization?
Equipment suppliers, Cement producers, corporations,
associations Everybody !
How does it work?
Identification of limitations
Understanding of root causes
Provision of solutions to overcome limitation/shortcoming
(C)aixergeeGmbH,Germany2014
3. Permanent Need For Process Optimization
• Cost pressure and ever changing requirements force cement plants to
modify & optimize their production process continuously
• The process inside the vessel is different from what it looks like from
the outside!
• Equipment as delivered by OEM‘s needs to be adapted:
• “as much as necessary – as little as possible”
• Supplier-independent optimization is necessary for:
• Process
• Equipment
• operation
(C)aixergeeGmbH,Germany2014
4. The aixergee Approach
Understanding the process & optimize it:
Process optimization needs
• a knowledgeable understanding of the real plant and the transfer
to the model
• Careful check of the model and its computational results
• Solutions from experts as a synthesis from their know-how and the
models results
Modeling
• gas-flows
• meal flows
• combustion
• calcination
• mineralization
• emission
• clinker quality
• …
transfer
(C)aixergeeGmbH,Germany2014
5. The aixergee Approach
(C)aixergeeGmbH,Germany2014
• Site visits
• Measurements
• Control system
• Operator interviews
Analysis
Data collection from control system Operator interviewsOn-site visits including measurements
Generate a deep
understanding of
pneumatic, physical
and chemical
phenomena. Detect
root causes and
eliminate those
Proposal
Develop a reasonable
and materializable
solution
Data assessment
6. The aixergee Approach
(C)aixergeeGmbH,Germany2014
CFD modeling Flowsheet modeling
Preheaterexhaust gas Stack
Temperature 360 °C Temperature 110 °C
False air ingress tower 20000 Nm³/h False air ingress conditioning tower & ESP 0 Nm³/h
Flow rate 203352 Nm³/h Flow rate 203352 Nm³/h
Flow rate 471508 m³/h (@360°C) Flow rate 285288 m³/h (@110°C)
O2-content n.a. vol-% O2-content 6,5 vol-%
SO3-content n.a. vol-% SO3-content n.a. vol-%
NOx-content n.a. ppm NOx-content 1000 ppm
Cyclone 1
Exit temperature gas 360 °C Cyclone 2
Meal temperature n.a. °C Exit temperature gas 550 °C
Pressure -48 mbar Meal temperature n.a. °C
Number of cyclones 1 Pressure -37 mbar
Number of cyclones 1
Cyclone 3
Exit temperature gas 670 °C
Meal temperature n.a. °C Cyclone 4
Pressure -29 mbar Exit temperature gas 800 °C
Number of cyclones 1 Meal temperature 810 °C
Pressure -21 mbar
Number of cyclones 1
Cyclone 5
Exit temperature gas 890 °C
Meal temperature 865 °C Bypass
Pressure -15 mbar Objective No bypass
Number of cyclones 1 Temperature after mixing chamber °C
Total flow rate Bypass ID fan m³/h
Kiln inlet Flow rate cooling fan m³/h
Temperature 1000 °C Dust load mg/m³
pressure -2 mbar LOI bypass dust
O2-content 0,4 vol-%
SO3-content n.a vol-%
NOx-content n.a vol-% Flow rates are calculated on the basis of oxygen content at stack
False air ingress assumed based on typical values
Energy, species and mass balancing
• Site visits
• Measurements
• Control system
• Operator interviews
Data assessment Analysis Proposal
Develop a reasonable
and materializable
solution
• Conventional
• Mass & Energy
Balancing
• Combustion
• Process models
• CFD
• CPFD
• Thermochemical
models
7. The aixergee Approach
(C)aixergeeGmbH,Germany2014
Modification/Retrofit Detail EngineeringBasic Engineering
• Site visits
• Measurements
• Control system
• Operator interviews
Data assessment Analysis Proposal
• Conventional
• Mass & Energy
Balancing
• Combustion
• Process models
• CFD
• CPFD
• Thermochemical
models
• Process settings
• Control concepts
• Modification/Retrofit
• Equipment selection
• Basic Engineering
• Detail Engineering
8. DEM:
Discrete
Elements
CFD:
Euler/Lagrange
Euler/Euler
The aixergee Approach – Modeling Options
(C)aixergeeGmbH,Germany2014
low (e.g.: behind filter) high (e.g.: silo discharge)
Influence of particle on the multi-phase flow
Levelofmodeling
Processlevelphysical
Flowsheet Simulation:
Mass- & energy balances – properties of process
equipment
Granular
flow
modeling
CPFD:
MP-PIC
(multi phase –
particle in cell)
Conventional -
Spreadsheet,
Table
Conventional -
Spreadsheet,
Table
9. Modeling of a cyclone preheater
(C)aixergeeGmbH,Germany2014
Preheater with shaft-stage: high temperatures and
unstable operation
• Twin-string preheater with
common shaft-stage
• Meal from stage 1 introduced
above the shaft-stage
• Meal from stage 2 introduced
into shaft-stage
• Meal from stage 2 also partially
bypassed around the shaft-stage
Where does the meal go?
Does it take this path
continuously?
Degree of calcination?
Stable kiln operation?
What is the optimum for lowest
exhaust gas temperatures?
10. Modeling of a cyclone preheater
(C)aixergeeGmbH,Germany2014
Preheater with shaft-stage: where does the meal go?
Meal from stage 2 into riser
duct:
Enters the shaftstage in
suspension from the riser duct
Splits into:
1 stream upwards
2 stream downwards
Meal from stage 2 into shaft-
stage (left side)
falls down
Meal from stage 2 into shaft-
stage (left side)
falls down
Meal from stage 2 into shaft-
stage (right side)
Splits into:
1 stream upwards
2 stream downwards
11. Modeling of a cyclone preheater
(C)aixergeeGmbH,Germany2014
Preheater with shaft-stage: where does the meal go?
Meal flow and gas
temperatures:
• Meal particles flow
uncontrolledly
• Huge temperature
differences within the
shaft-stage
• Calcination very
inhomogenuous
• Kiln operation disturbed
by unstable
precalcination
• Preheater exhaustgas
temperatures high
12. Modeling of a cyclone preheater
(C)aixergeeGmbH,Germany2014
Distribution of the calcination degree:
• Either uncalcined (20 % of quantity) or fully calcined (35 % of
quantity) material enters the preheater cyclones
• Rather no partly decarbonized material delivered to the cyclones
• While fine particles can be of both types, coarse particles are likely
uncalcined
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
0-10 10-20, 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100
Fraction[%]
Degree of Calcination (%)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 50 100 150 200
DegreeofCalcination
Particle Diameter (microns)
13. Modeling of a cyclone preheater
• Counter-current flow with internal recycles
requires model based mass and energy
balancing
• Combination of flow sheets and CFD
• Dynamic flow-sheets based on unit
operations
• Customized models for specific process
units featuring
• miscellaneous material/phase properties
• solid flows including particle size distribution
Which split-rates for the meal produce the
lowest exhaust gas temperature?
(C)aixergeeGmbH,Germany2014
Transfer of the CPFD-model into a dynamic flowsheet-
model
14. Plant design operation
Current plant operation
Optimum plant operation
Optimization of the cyclone preheater
Parameter study shows:
• Optimum operation point can be found generating a shaft exit
temperature of 715 °C
• Todays operation generates 750 °C (at worse calcination!)
• PH exit Temperature can be lowered by 30 °C
• Heat consumption of kiln can be lowered by approx. 100 kJ/kg Cli
(C)aixergeeGmbH,Germany2014
15. CFD Modeling
Kiln burner:
• Energy loading of sintering zone
• Material quality of product
• Mineralogy
• Burn-out
• Ash drop-out
Calciner:
• Lower particle loading
• Complex chemistry
• Dynamic / transient simulation
• Numerical evaluation:
• Particle classes
• Residence times
• Calcination degrees
• Fuel burn out rates
• Histograms of particles
• Scenario studies
• Sensitivity analyses
• Forward simulation of modifications
• “Virtual plant”
(C)aixergeeGmbH,Germany2014
16. Conclusion
Process Optimization achieves:
• Improvement of plant performance, e.g.:
• Reduction of exhaust gas temperatures
• Reduction of pressure drops
• Stabilization of plant operation
• Increase of secondary fuel utilization
• Increase of product quality
• Support of decision making through virtual plant simulation:
• Comparative rating of different optimization options
• Feasibility checks
• Investment safeguarding
• Speed up of optimization projects:
• No trial & error but target directed action
(C)aixergeeGmbH,Germany2014