The document discusses process optimization for the cement industry. It describes how process optimization can help cement producers get better results without major investment by identifying limitations, understanding root causes, and providing solutions. It provides an overview of aixergee's approach to process optimization, which includes data collection, analysis using modeling and simulations, developing proposals for improvements, and engineering support. It also includes several examples of process optimizations performed for cement plants.

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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
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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
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5. The aixergee Approach
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• 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
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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
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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
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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?
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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
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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
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17. Some References
CEMEX WestZement, Beckum
CEMEX OstZement, Rüdersdorf
CEMEX UK, Rugby, UK
China United Cement Company, Wulanchabu, CHN
ECOMB AB, Södertälje, SWE
Greco, Gödersdorf, AUT
HeidelbergCement, Harmignies, BEL
Holcim Czesko, Prachovice, CZ
KHD Humboldt Wedag, Köln
Lagan Cement, Kinnegard, IRL
LHOIST, Flandersbach
Loesche GmbH, Düsseldorf
Praxair Deutschland, Düsseldorf
CEMENTAREN Ladce, Ladce, SVK
Thyssen Krupp Polysius, Neubeckum
ECRA CCS-Research Partner
(C)aixergeeGmbH,Germany2014