apc presentation gels2004
TRANSCRIPT
Advanced Process ControlAdvanced Process ControlAPCAPC
Why ProcessWhy Process
Control ? Control ?
Why Process Control ?Why Process Control ?
Safe operationSafe operation
To meet product specsTo meet product specs
Achieve production targetsAchieve production targets
Optimization of inputs - Energy/ Power/ Raw MaterialOptimization of inputs - Energy/ Power/ Raw Material
HOW ?HOW ?
Maintain desired operating conditions, Maintain desired operating conditions, Minimize process variance, Fight Minimize process variance, Fight disturbances, Honor process constraintsdisturbances, Honor process constraints
Elements of Process Control
Feedback ControlFeedback ControlSET POINT
Controller Valve
PROCESSCONTROLLEDVARIABLE
error
MANIPULATEDVARIABLE
Types of Types of Controller ?Controller ?
Types of Controller ?Types of Controller ?
PID Controllers PID Controllers
Feed-forward Controller Feed-forward Controller
Model Predictive ControllerModel Predictive Controller
APCAPC
… … Other ControllersOther Controllers
Feed-forward controller : Disturbance rejectionFeed-forward controller : Disturbance rejection
– Require model of Process, DisturbanceRequire model of Process, Disturbance
Traditional Advanced controller : Strategy DrivenTraditional Advanced controller : Strategy Driven
– Inferential control, Ratio Control, Cascade Control, Inferential control, Ratio Control, Cascade Control, Gain Schedule controller, Gain Schedule controller,
– Model is sometimes requiredModel is sometimes required
– Programming logics can be incorporatedProgramming logics can be incorporated
PID ControlPID ControlSET POINT
PID Valve
PROCESSCONTROLLEDVARIABLE
error
MANIPULATEDVARIABLE
Limitations of PID ControllerLimitations of PID Controller
Single Input - Single Output (S I S O)Single Input - Single Output (S I S O)
Reactive strategy - cause & effectReactive strategy - cause & effect
Interactions not handledInteractions not handled
Tuning difficult for process with delaysTuning difficult for process with delays
Not predictiveNot predictive
No constraint handlingNo constraint handling
Tuning impossible for unstable processesTuning impossible for unstable processes
Multivariable ControlMultivariable Control
APC
PROCESS
G
SETPOINT(Target)
c1
c2
c3
c4
C
m1
m2
m3
M
C = G . M
APC and OptimiserAPC and Optimiser
APC is the continuous and real time APC is the continuous and real time implementation of Technological and implementation of Technological and Operation know-how through the use of Operation know-how through the use of sufficient computing power in dynamic plant sufficient computing power in dynamic plant environment in order to maximize environment in order to maximize profitabilityprofitability
The objective of Optimiser is The objective of Optimiser is
-to generate operating targets which maximize -to generate operating targets which maximize profits within all relevant constraintsprofits within all relevant constraints
Why Why A P C A P C ......
Processes are interactive and complexProcesses are interactive and complex– ……to handle the interactions to handle the interactions simultaneouslysimultaneously
Numerous operating constraints existNumerous operating constraints exist– … … to handle the constraints to handle the constraints simultaneouslysimultaneously
Frequent variations occur in the plantFrequent variations occur in the plant– … … to operate more to operate more proactivelyproactively
Optimum operating point keeps shiftingOptimum operating point keeps shifting– … … to operate at most to operate at most profitable conditionsprofitable conditions
How How A P C A P C worksworks
to handle the interactions to handle the interactions simultaneouslysimultaneously– MIMO Dynamic Model
to operate more to operate more proactivelyproactively– Dynamic model gives Dynamic model gives predictive capabilitypredictive capability
to handle the constraints to handle the constraints simultaneouslysimultaneously– Multiple Constraint ProblemMultiple Constraint Problem
to operate at most to operate at most profitable conditionsprofitable conditions– Cost Constraint ProblemCost Constraint Problem
APC RequirementsAPC Requirements
Requires a model of the processRequires a model of the process
Process variable categories:Process variable categories: Manipulated variables ( MV)Manipulated variables ( MV) Controlled variables (CV)Controlled variables (CV) Disturbance variables (DV)Disturbance variables (DV)
Model : CV response to a MV, DV changeModel : CV response to a MV, DV change Predicts future effects on CV, from MV & Predicts future effects on CV, from MV &
DVDV
MVDV
CVTypical CDU Model
Reference TrajectoryReference Trajectory
Past Future
setpoint
MV
CVPrediction horizon
Control horizon
T+1 T+2 T+3 T+4 T+5 T+6T-1T-3 T-2
APC pushes to Optimal APC pushes to Optimal ConstraintsConstraints
Speed
Temperature
Compressor
Column DP
Motor Amps
Pressure
Typical
Operating
Region
Qualities
Optimal
Constrained
Operation
APC solutionAPC solution
Operator Limits + Current Values + Operator Limits + Current Values + Models ==> best combination of MV Models ==> best combination of MV setpoint changessetpoint changes
Objective function : Minimization of sum Objective function : Minimization of sum of the square of errorsof the square of errors
Uses an LP or SQP to find the best Uses an LP or SQP to find the best solutionsolution
Benefits of APCBenefits of APC Maximize profit marginMaximize profit margin
– Throughput increaseThroughput increase– Increased yields of value added productsIncreased yields of value added products– Energy savingsEnergy savings– Improved and consistent product qualityImproved and consistent product quality
Improved Stability Improved Stability Smooth and consistent operation /Enhanced Smooth and consistent operation /Enhanced
disturbance rejection /Reduced variation in key disturbance rejection /Reduced variation in key parametersparameters
Is like the best operator controlling the process at every instantIs like the best operator controlling the process at every instant
Multivariable Control Multivariable Control BenefitsBenefits
Specification or Limit
Average
Average
Current Operation
Variations Reduced with
Advanced Control
Move Average Closer to
Specification or Limit
Suppress (minimize) process variances enabling the “pushing” of constraints
APC FeaturesAPC Features
Model-basedModel-based Multi-variable Multi-variable Predictive actionPredictive action Steady-state economic target Steady-state economic target
calculationcalculation Honours constraints while Honours constraints while
pushing limitspushing limits
APC SchemeAPC Scheme
Nodebus
CP
AW
PID PID PID
DMC+
FOXAPI
APC ImplementationAPC Implementation
APC Controller drives the setpoint of the APC Controller drives the setpoint of the DCS controllerDCS controller
Assumes that the PID controller will Assumes that the PID controller will bring the process variable to its new bring the process variable to its new setpointsetpoint
APC VendorsAPC Vendors
IDCOMIDCOM Controller from Setpoint Inc. (merged Controller from Setpoint Inc. (merged with Aspen)with Aspen)
DMCplusDMCplus Controller from DMCC (now Aspen) Controller from DMCC (now Aspen)
Star ControllerStar Controller from Dot Products from Dot Products
SMOCSMOC from Shell from Shell
ConnoisseurConnoisseur from Foxboro from Foxboro
RMPCTRMPCT from Honeywell from Honeywell
… … many moremany more
APC s in RelianceAPC s in Reliance
SMCA IDCOMSMCA IDCOM Controllers in PX and Controllers in PX and LAB, Patalganga. Will be upgraded to LAB, Patalganga. Will be upgraded to DMCplusDMCplus
Star ControllerStar Controller implemented with in- implemented with in-house APC teams in 11 plantshouse APC teams in 11 plants
DMCplusDMCplus Controller used in Naphtha Controller used in Naphtha Cracker, HaziraCracker, Hazira
APC implementations in APC implementations in RelianceReliance
Patalganga DivisionPatalganga Division– PTAPTA ( ( Star ControllerStar Controller))
– PXPX ( (SMCA ControllerSMCA Controller))
– LABLAB ( (SMCA ControllerSMCA Controller))
APC implementations in APC implementations in RelianceReliance
HaziraHazira– MEG 1,2 & 3MEG 1,2 & 3 ( (Star ControllerStar Controller))
– PVC PVC ((Star ControllerStar Controller))
– PE 1 & 2PE 1 & 2 ( (Star ControllerStar Controller))
– PTA 1 & 2PTA 1 & 2 ( (Star ControllerStar Controller))
– POY CP 4 & 5POY CP 4 & 5 ( (Star ControllerStar Controller))
– Naphtha CrackerNaphtha Cracker ( (DMCPlus ControllerDMCPlus Controller))
APC s Installed at APC s Installed at JamnagarJamnagar
CDU 1 & 2 CDU 1 & 2 FCCU FCCU CokerCoker HydrotreatersHydrotreaters AromaticsAromatics
Levels of Process Levels of Process AutomationAutomation
Planning, EconomicsPlanning, Economics
Real-Time OptimizationReal-Time Optimization
Advanced Process ControlAdvanced Process Control
Distributed Control SystemDistributed Control System
Steps in APC Steps in APC implementationimplementation
Functional Design of control strategyFunctional Design of control strategy
Detailed Engineering Detailed Engineering
Procurement & installation of new instrumentationProcurement & installation of new instrumentation
DCS configuration (graphics, tags, etc)DCS configuration (graphics, tags, etc)
Tune PID’s & modify DCS strategies (if necessary) Tune PID’s & modify DCS strategies (if necessary)
Dynamic testing & Model IdentificationDynamic testing & Model Identification
Simulation & off-line tuningSimulation & off-line tuning
Software integration, communication checkSoftware integration, communication check
Operator trainingOperator training
Controller commissioning and on-line tuningController commissioning and on-line tuning
Post-auditPost-audit
Typical APC SCREEN
APC BENEFITS
CDU APCCDU APC
Range of CDU/VDU Range of CDU/VDU BenefitsBenefits
ACU: 0.04 – $0.08/BBL of crudeACU: 0.04 – $0.08/BBL of crude Vacuum unit: $0.05 – $0.10/BBL of Vacuum unit: $0.05 – $0.10/BBL of
atmospheric residuum. atmospheric residuum. Combined: $0.06 – $0.13/BBL of crudeCombined: $0.06 – $0.13/BBL of crude
At 520,000 Barrels/Day = At 520,000 Barrels/Day =
$10.9million to $23.6million per $10.9million to $23.6million per year. ==> 50 to 100 Cr/yryear. ==> 50 to 100 Cr/yr
before and after APCbefore and after APC19
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P103.PVT101DP.PV
Post CommissioningPre- Commissioning
Mainfractionator Delta Pressure iT101DP)Regenarator Pressure(P103)
Not Actively controlled because of other constraints
P103 Average 2.467Standard Deviation 0.00482
Operator Workload (example)Operator Workload (example)
Measure ofOperator Workload
Before(#/day)
After (#/day)
Alarms 23 11
Operator interventions 155 40
DMCplus Controller DMCplus Controller TerminologyTerminology
Manipulated Variables (MV’s)Manipulated Variables (MV’s) The “handles” DMCplus moves or The “handles” DMCplus moves or
“manipulates” to control the process.“manipulates” to control the process. For example:For example:
– a distillation column reboiler steam flow set point (an a distillation column reboiler steam flow set point (an MV) is moved to control the bottoms composition (a MV) is moved to control the bottoms composition (a CV)CV)
Feedforward Variables (FF’s)Feedforward Variables (FF’s) Variables which affect the process but Variables which affect the process but
which the controller canwhich the controller cannotnot manipulate. manipulate.– For example:For example:
Ambient temperature.Ambient temperature.
DMCplus Controller DMCplus Controller TerminologyTerminology
Controlled Variables (CV’s)Controlled Variables (CV’s) Variables that change when a manipulated Variables that change when a manipulated
or feedforward variable movesor feedforward variable moves CV’s are measurements that represent CV’s are measurements that represent
important operating limits and targetsimportant operating limits and targets For example:For example:
– Flue gas OFlue gas O22
– Product composition (EP’s)Product composition (EP’s)– Valve positionsValve positions– RCSV DPRCSV DP
Also called “Dependent Variables”Also called “Dependent Variables”
Process (DCS)
DMCplus Controller
Read CV, FF & current MV values
Write new MV values
Read limits & other operator entries for all CVs and MVs
OverheadDrum Level
DP
TopComp.
BottomComp.
Dynamic Model of a Column
Feed Temp Feed ToColumn
Pressure RefluxFlow
Steam toReboiler
Linear Programming (LP)Linear Programming (LP)CW
AI
Steam
RefluxdP
Feed
Bottom product
Top product
AI
Linear Programming (LP)Linear Programming (LP)
MV1 - Steam Flowmaxmin
MV2 - RefluxFlow
max
min
steam flowlow limit
reflux low limit
high dP
minoverheadimpurity
maxoverheadimpurity
max bottomsimpurity
FeasibleRegion
Linear Programming (LP)Linear Programming (LP)
MV1 - Steam Flowmaxmin
MV2 - RefluxFlow
max
min
steam flowlow limit
reflux low limit
high dP
minoverheadimpurity
maxoverheadimpurity
max bottomsimpurity
THANK YOUTHANK YOU