loesche
DESCRIPTION
LOESCHETRANSCRIPT
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Improving Performance of
Vertical Roller Mills
Using Advanced Process Control and
Real Time OptimizationSteve McGarel
Loesche GmbH
Germany
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Operational Challenges in the Plant
• Cost efficient production despite rising energy, labor and material costs
• Increase throughput and maintain process stability to improve power efficiency
• Reduced performance due to different operator philosophies and experience
• Optimum use of personnel knowledge – capture best practices
• Highest possible availability of equipment - process and mechanical safety
• Implement projects with high Return On Investment for new capital spending
• For Roller Mills, correct product fineness, in a stable process, at high output• Optimize mill operation to lower production cost• Provide the plant with consistent feed to improve performance and reduce cost• Reduce specific energy use and carbon footprint to support sustainability efforts
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Mill Optimization Solution• is a state of the art optimization solution for Loesche Roller Mills
• Uses commercial software to benefit from advanced R&D and control technology• Loesche is a mill supplier not a software design company• Software platform fully tested and proven in industry projects
• Designed using Loesche’s unique knowledge of roller mill design and operation
• improves mill efficiency and helps solve operational problems in existing mills - or can be specified for new mill projects
• Proven on pilot site to deliver excellent results in terms of• Production rate• Energy consumption• Vibration reduction• Process stability
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• Obstacles and requirements of Vertical Roller Mill process and operation
• Advantages of mill optimization
• Solution Description
• Savings and Benefits to clients• Case Study project
Real Time Optimization of Vertical Roller Mills
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• Factors affecting stable and efficient operation are changing in real time
• Raw material characteristic – physical size, mineral composition• Affects grindability of feed
• Raw material moisture content• Affects dry mass flow of rock as well as drying and grinding capacity
• Equipment wear e.g. mill tires• Reduces grinding capacity until mechanical maintenance is performed
• Very fast process dynamics requiring constant attention and accurate control moves
• Optimal operation can only be achieved via advanced process control• Manual operation cannot be continuously effective and accurate• Multiple units to monitor and control reduces time to maximize mill performance
Obstacles to Optimal Operation
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• Increase Production in presence of potential limitations
• Mill Table Power• Mill differential Pressure - Mill fan capacity• External recirculation system - Bucket Elevator and other conveyors• Drying capacity - Mill outlet temperature
• Reduce Product Size (“Residue”) Variability• Balance throughput with stability of mill – competing objectives• Rapidly and continually adjust for changes in feed hardness for consistent grinding
• Reduce Specific Power Consumption• Reduce variability in the load parameters
• mill table power, mill delta Pressure, mill fan power• Reduce specific power - mill fan & mill table
• when operating with a fan at full speed consider table power limiting production
Requirements of Vertical Roller Mill Process and Operation
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Control Scheme Structure Using PID Controller
Set PointComparator Controller Actuator
Disturbances
PlantSensorFeedback Element
Feedback
PID Controller
Proportional - Integral - Derivative
Control Action
Sensor Output Output
Error Control Output
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Limitations of PID Loop Control Environment
• Each PID has no knowledge of the whole system or other PID loops and no process
knowledge is embedded in the control scheme
• No dynamic capability, no non-linear capability (changing process behavior)
• Reactive to the present, very limited capability to consider future effect in the process
• Poor with process delay times, noisy signals, large disturbances
• Transfers noise to the output and into the process from multiple PID loops
• PID tuning is difficult, subjective, requires time and experience, ongoing
• Compromise of multiple aspects – P,I &D; trade off regulation versus response time
• These effects add up over time
• Multiple loops acting independently are not optimal and can work against each other
• Increases variability, decreases output and quality, ultimately increases production cost
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Optimize Process Performance
Model Predictive Control
Define production versus quality versus stability
Calculate process set points
Process Control Hierarchy
Automatic Loop Performance
Ratio
Feedback – PID
Cascade
Feed forward (multivariable)
Manual Set Points
On/Off
Open loop
Optimize
Automatic
Manual
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Manual Stabilize Optimize
When stabilized, the process can be pushed to the real physical limits
Path to Process Optimization
Project Benefit
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Advantages of Mill Optimization
• Fast process dynamics controlled by high speed solution
• Multiple variables are tightly controlled over complete mill circuit
• Competing objectives of throughput, quality and energy use are handled optimally
• Continuous display of process conditions increases operator confidence
• Stable automated operation of the Vertical Roller Mills allows operators to focus on the main processes – pyro-process, alternative fuels and product quality
• Improved operation of the Vertical Roller Mills – e.g. in raw mill, clinker grinding - leads to more stable and efficient operation of the complete cement line
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Solution Description Schematic
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Model Predictive Control Variable Classes
• CV’s – Controlled Variables
• targets for the controller and process e.g. fineness
• MV’s – Manipulated Variables
• controller adjusted set points sent to the process e.g. feed rate, classifier speed, fan
• CCV’s – Constraint Variables
• upper and lower limits for variables e.g. vibration, pressure, motor load
• DV’s – Disturbance Variables
• factors that affect the process but are not in the control matrix structure e.g. feed hardness, feed size, moisture content, roller wear
DV’s
CV’s
CCV’s
MV’sModel
Predictive Controller
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MPC Step Test Response and Prediction Horizons
MV Step Change in Manipulated Variable e.g. Mill Feed Rate
Response of Controlled Variable e.g. Mill Motor Power, Delta Pressure
Gain
Rise Time
Delay Time
CV
Current Time
History Prediction Horizon
Step Move
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CV 1
Controller Matrix
CV 2 CV 3 CV 4
MV 1
MV 2
MV 3
No Model
No Model
No Model
Pairs of variables define process interactions
Process response is captured in the controller
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MV – Manipulated Variable
Total Feed (MV)
Roller Pressure (MV)
Water Flow (MV)
Classifier Speed (MV)
Fan Speed (MV)
Example of Solution Variables
CV – Controlled Variable
Fineness
(CV)
Outlet Temp (CV)
CCV – Constraint Variable
Diff. Pressure (CCV)
Vibration (CCV)
Motor Load (CCV)
DV – Disturbance Variable
Roller Wear (DV)
Feed Blend (DV)
Feed Hardness (DV)
Feed Moisture (DV)
~ 70 variables on a mill circuit
Optimizer uses a sub-set of variables for efficient control
60 sec. residue predictions
compared to
1 hour lab result
15 second controller cycle
Weather Conditions (DV)
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Implementing Control Using All Recommended Variables
• Operators normally control multiple units• raw mill, cement mill, coal mill, kiln or some combination
• Customer may not be using roller pressure or water sprays for control• too many variables to deal with on a frequent basis
• Roller mill is deliberately cut back to allow focus on full process
• increases performance level and allows hands off operation
• Operate with best possible performance all the time
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System Hardware Architecture
Client
Server
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Case Study Project In Operation
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Case Study Project Steps
• Historical data analyzed to determine baseline performance
• Determine important variables for the project mill
• Step tests of main variables on site to capture mill response
• Controller matrix constructed and connected to PLC network
• Model predictions checked in Read - Only mode
• Variables activated in Read-Write mode one by one and tuned
• Multiple variables activated in control mode and interaction tuned
• Run time accumulated in full closed loop control to fine tune
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Operational History
Process Variability
Off Line Data Analysis
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Controller Matrix Developed from Process Knowledge
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Coefficent / Rank: Determination of priorities 1. Residue 2. Mill differential pressure 3. Increase of material feed
Frustum = Soft Constraints
Constraints:Limitations for CVs and MVsCVs may violate hard constraints, but MVs may not
Controller Set Up Screen Shot
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Operator Screen
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Key Performance Indicators (KPI’s
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Applications Manager Detail
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• Development and implementation in 2-3 calendar months• Close teamwork with the customer• Operator integration into implementation process• Consultation to define improvements and improve controller• Final meeting after evaluation of results• Overall project duration approximately 4 months
Project Timeline and Major Steps
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Benefits Comparison Before and After Project
• Original Installation Contract condition (2006):• Specific power consumption 21,2 kWh/t, Product rate 480 t/h
• Optimization Project Starting condition (2011):• Specific power consumption 15,8 kWh/t , Product rate 505 t/h
• Optimization Project Evaluation condition (June 2011):• Specific power consumption 15,2 kWh/t, Product rate 528 t/h
+ 25 tph over 5 years
Mechanical and operation changes
+ 23 tph over 3 months
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Benefits Summary
• Increase throughput of 6 %
• Energy savings of 5 %
• Reduced mill vibration by 17 % (1.04 mm/s)
• Controller utilization greater than 90 %
• Improved stability of the circuit and variability of raw meal residue
• Operator freed up to better monitor kiln performance
• Reduced CO2 emissions from plant (produces own electric power)
• Return on Investment less than 12 months
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Note feed rate changes
Total Fresh Feed Before and After Project
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Power Consumption Before and After
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Mill Diff Pressure Before and After
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Maximum
Average
Maximum
Mill Vibration Before and After
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Conclusion
• is a state of the art optimization solution for Loesche Roller Mills
• improves mill efficiency and helps solve operational problems in existing mills - or can be specified for new mill projects
• Proven to deliver excellent results in terms of• Production rate• Energy consumption• Vibration reduction• Process stability
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THE END !
Thank You