a value service - operational efficiency advanced...

Protection notice / Copyright notice© Siemens AG 2008. All rights reserved.

A Value Service - Operational Efficiency

Advanced Process Control

Dave Kelly & David Lovett

Region / Sector / Division / DepartmentProtection notice / Copyright notice

Value Propositions / Value Services Business Portfolio Overview

Value Propositions / Value Services

Carbon Mgmt & Emissions

Trading

Energy & Process

Optimization

Services to optimise plant energy efficiency and Sustainability

Energy & Environmental

(EES)

Energy / Water Management

Steve Barker

Plant Maintenance

Services

Maintenance Operation

Plant Maintenance Consulting

long-term partnershipperformance-based contracts

Integral Plant Maintenance

(IPM)

Graeme Coyne

Plant Reliability

Engineering

Plant IT Security

Services to optimise plant energy efficiency and Sustainability

Condition Monitoring &

Reliability (CMR)

* Condition Monitoring

Paul Hingley *

MES

Automation Solutions

Applying Siemens products in industrial environments

Projects D-F(RP)

Electrification Solutions

Mike Houghton

Product training for

customers

Siemens Certification

Program

Training equipmentConsulting

Training for Automation and Industrial SolutionsSITRAIN

TrainingServices

(TR)

David Wheeler

Verification

Project safety engineering

Services to ensure machine and process safety

Safety

Risk Assessment

Paul Hingley

Plant assessment

Plant simulation/ MES

Lean

Service & solutions to improve plant performance

Operational Efficiency

David Kelly

Spare Parts & Components

OEM components

Repair

Online & Offline Maintenance

Metallurgical Services

(MT)

Mark Evans

Full service provider for the entire metallurgical plant


RAPF

1st PhaseProjects

Operational Efficiency – A Value Service

PhaseIterationVDA/Lean/

SRM/APC

2nd PhasePlant

Simulation

3rd Phase

MES

4th Phase

A Systematic Approach

I1I2I3

Ô1Ô2

Neural Network


Linking Technology to Production Gains

Prod

uctivity In

crease

Lean Activities

Simulation

Further Productivity Gains

Traditional Lean Productivity Gains - PlateauData Management

APC/SPC

Automation

Protection notice / Copyright notice© Siemens AG 2008. All rights reserved.

Advanced Process Controlfrom petrol and pills to powder and poo

David Lovett


Content

• Introduction to Siemens Operational Efficiency

Overview of Advanced Process ControlAPC Tool & Techniques within PCS 7

When to use APCOptimal OperationMake everything as simple as possible, but not simpler!

Summary of non COG APC applications in regular use

Case studiesPharmaceuticalsNutritional Foods (Infant milk and chocolate!)Waste Water Treatment

Summary and Conclusions

2012-07-05 Author:Lovett/Kelly


APC Tools Techniques

high

APC Method

low APC Complexity

GainScheduling

Integrated / interfaced3rd party tools

Embeddedin PCS 7

OverrideControl PID

Tuning

SmithPredictor

OperatorTraining

Simulator

FuzzyControl

Soft Sensor

lean large

MPC

ControlPerformanceMonitoring

Lead-LagFeed-Forward

Control

APC Strategies & Tools



Model Predictive Control with PCS 7

Application areas2x2:

Two components distillation column(> 6 References)Steam generator (2 References)Drying roomPaper production (2 References)Chemical ReactorTwo-variable pressure control Two tank system

3x7 +2DV‘s:Heavy crude fractioning column [4.]

3x2:Steel bleaching process

5x6:Industrial oven

3x3:Loop reactor [2.]Steam generator [4.]Distillation column [1.]

7x7:Multi component distillation

3x4:Cement kiln

6x9:Column system of a gas decompositionplant [2.]

4x4:Three components distillation columnLiquid gas steam generatorOven with four burners

10x7:Glass oven feeder [1.]

lean

large, or combination of several

lean MPC blocks

APC Strategies & Tools



When to use APC Techniques


• Think of a process (unit or multiple units).• Is this a suitable process to apply Advanced Process Control?

• Draw all the Inputs and Outputs• Which Inputs and Outputs are correlated? i.e. affect each other.• What are the approximate time delays (pure dead time and response times) for each

input to each output?• What constraints exist for each process Output?• What constraints exist for each process Input?• What are the economic costs associated with running the process? (unit)• Control Objective: Trajectory following or process disturbance rejection?

• Question: Is you process technically suitable for Advanced Process Control?


Optimal Operation

In practice, the operating points are determined to satisfy economic goals.The control system normally operates conservatively to avoid constraint

violations. Whilst offering a degree of comfort, the operating point is frequently lower on the profit-loss curve.

Optimal Operation shifts the operating point(s) higher on the Profit Loss Curve.



Make everything as simple as possible, but not simpler …..One of the biggest challenges.



Control & Optimisation Experience applied to non Chemical, Oil & Gas systems

Granulation, Milling, BlendingDrying: Spray, Fluidised Bed, Rotary, RingFermentationMicro ReactorsEvaporation, Distillation, PurificationCrystallisationBio RefiningActivated Sludge processes, Anaerobic DigestionCombustors & IncineratorsPaper Machine optimisation Optimising the Process

2012-07-05 Author:Lovett/Kelly12

Examples of Non COG Applications


Case Studies

1.Pharmaceutical – Optimisation of Granulation2.Powders – APC applied to Nutritional Powder Production plant3.Casein Production4.Waste Water Treatment Optimisation

2012-07-05 Author:Lovett/Kelly13


Drug Product Granulation Control Control System Overview

Source: Example from Published Industrial Applications: Pfizer’s HSWGReal-time Control System (IFPAC 09 – Mojgan Moshgbar).



Results

Source: Example from Published Industrial Applications: Pfizer’s HSWGReal-time Control System (IFPAC 09 – Mojgan Moshgbar).



Casein Production How is operation is improved using APC?

Outlet Air Temperature SP

Moisture – Dryer ExitCasein DryerPrimary Air

Temperature SP

Secondary Air Temperature SP

Feed Density

Mass Flow (Curd) SP

Ambient Humidity ofcooling air

Bulk Density

Dec 2b hydro pressure


Dynamic Tests for Modelling

Moisture

Bulk Density

Inlet Air Temperature

Feed Rate



Dynamic Process Model

Dead-Time Lag Time



Model (Soft Sensor) Validation

Moisture MeasurementMoisture Prediction

Evaluate the Dynamic ProcessModel by running it in real-timewith the process



Audited Benefits

• 45% Reduction in Variability –• “within” process sigma increased from

1.8 to 3.5• 0.6% moisture uplift in bag• 96% MPC Service Factor• 10 hours per week of extra on spec

product• Reduction in dryer blockages• Downtime reduction attributable to

Process Monitor & MPC currently being audited


•Combined benefits >£300,000 per year •Payback period <4 months (excluding any savings in downtime reduction)

Note: There is a separate presentation available discussing the advantages of combining multivariate statistical models with the time series dynamic models used for monitoring multivariable control systems!


Model Predictive Control – Nutritional Powders

Inlet air temp SP

Inlet air flow SP

Chamber pressure

Chamber outlet temp

Exhaust air fan speed

Inlet air humidity

Feed temperature

Feed pressure

Concentrate solids

Outlet air RH calc block

Outlet temp SP constraint

Hom

ogenizer pum

p speed

Sticky point curve

EFB air flow

EFB air tempInlet air humidity

Concentrate solidsInferred powder moisture

Chamber outlet temp SP

Powder MoistureSP

IFB air temp SP

IFB air flow SP

MODEL PREDICTIVE CONTROLLER 2

MODEL PREDICTIVE CONTROLLER 1

Control scheme manages moisture by co- ordinated adjustments to the fluidised bed temperatures.

A software estimate of moisture would be supported by hourly updates from the Lab



Achieved Variability Reductions

75% reduction in impact of batch change

Discharge solids SP error on a batch change

70% - 90% of time Almost the only time APC doesn’t push to a limiting process constraint is on a finished product tank change

Amount of time APC drives throughput up to a process constraint

80% reduction in variabilityBuffer tank level swings in between finished product tank changes

35% reduction in variabilityDryer outlet temperature

65% reduction in variabilityEvaporator discharge solids

CommentsImprovementProcess Variable / Event

90% reduction in impact of batch change

Outlet temp SP error on a finished product tank change



Results Infant Formula

Evaporator / Dryer 1Nominal 3 TPH spray dryer, integrated Fluidised BedOperated well under manual control for many yearsHigh degree of operator experienceAPC System delivers a throughput increase of 7 - 9%4% specific steam saving~3% Increase in Yield

Evaporator / Dryer 23 years oldNominal 4 TPH spray dryer / integrated Fluidised BedHigh degree of operator experienceCorporate KPI to increase production capacity WITHOUT capital investmentAPC system delivers a throughput increase of 7.7 %6% specific steam saving2.2% increase in Yield



Solution Evaporator Start-up

9 start-ups

PLC

Multiples of 32s (~1 hour span)

Set Point

Stable operation

~ one hour

11 start-ups

APC

Multiples of 16s (~1 hour span)

Set Point

Stable operation

~ 20-30 minutes



Solution Dryer Humidity Constraint Control

Ambient %RH doubles over the time range shown

A

B

While exhaust air humidity is climbing but not yet at the constraint limit, APC pushes throughput

APC increases outlet air temp and reduces feed rate to prevent violation of exhaust air humidity constraint



1 2

3

4

5

1

2,4 5

1. APC switched on.

2. APC lowers outlet temperature to optimize yield and capacity. Sticky curve reached

3. APC responds to variation in ambient humidity by keeping on the sticky curve for optimal yield and capacity

STICKY

SAFE

Solution Powder Sticky Point Control



Solution Process Monitor (Preventative Maintenance)

Process “Healthy”

Using all the process measurements around the production plant to monitor the “state” of each process unit

Abnormal Operation

e.g. detection of SFB temperatures widening



Understand what is Good or ‘Normal’operation for the process.

Develop statistical operating envelopeRelate measured process variables to final quality parametersMultiple ‘normal’ zones dependent on plant operation, e.g. diurnal, seasonal.Examples

Waste Water Treatment PlantCoal Fired BoilerMultiple products Spray DryerFurnaces

Solution Process Condition Monitoring



Water

Water UtilitiesWaste water treatment – high energy useClean water treatment - chemical costs, sludge disposal, water lossesWWTP and WTW operate in ‘safe’ zone, not ‘optimal’ zone

Perceptive WaterMVPredictive control of ASP – optimal operation based on loadPredictive control of filter washing – optimal cleaning based on bed condition

ResultsProcess units pushed closer to constraintmuch higher asset utilisation, higher efficiency, reduced losses, reduced energy

Return on Investment Energy reduction on ASP, asset utilisation far higher – payback <18 monthsReduced sludge / water loss on RGF – payback ~12 months



MPC Active

P2 DOAverage

P4 DOAverage

P5 DOAverage

Total Power(kWh)

Sensor Saturation

Setpoint Tracking

Water Advanced Control

MV MPCSupervisory Control

Average DO above upper limit

At or below limit

Operating within limits

Operating within limits

Reduced power use



Water Advanced Control

Supervisory ControlMPC Control

MPC Normal

Aerator Power kWh 90.8 152.2

Energy reduction 35-40%

“Comfortable” Operating ZoneNOT Optimal


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Summary & Conclusions

1. APC applications increasing, significantly outside COG industries

2. Learn from other people’s mistakesBenchmark and audit performance before embarking on APC project

3. Ensure the APC design remains as simple as it can be but as complex as it needs to be.

4. Final word to author of the original Warren Report:Professor Mike Brisk stated in a recent article:Process Control: Potential Benefits and Wasted Opportunities“We now have ample indisputable evidence, collected over very many years in all parts of the world, that improving process control yields real, predictable economic benefits. It is also well-established that advanced control can deliver major economic gains. However, the latter is only true if the underlying basic regulatory control is maintained at peak performance.”

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