Improving Performance of

Vertical Roller Mills

Using Advanced Process Control and

Real Time OptimizationSteve McGarel

Loesche GmbH

Germany

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

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

• 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

• 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

• 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

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

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

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

Manual Stabilize Optimize

When stabilized, the process can be pushed to the real physical limits

Path to Process Optimization

Project Benefit

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

Solution Description Schematic

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

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

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

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)

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

System Hardware Architecture

Client

Server

Case Study Project In Operation

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

Operational History

Process Variability

Off Line Data Analysis

Controller Matrix Developed from Process Knowledge

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

Operator Screen

Key Performance Indicators (KPI’s

Applications Manager Detail

Slide 28/21

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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

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

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

Note feed rate changes

Total Fresh Feed Before and After Project

Power Consumption Before and After

Mill Diff Pressure Before and After

Maximum

Average

Maximum

Mill Vibration Before and After

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

THE END !

Thank You