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Plant Operation by Dr AA, 2008 Page 2.3 -#

APC and RTO

Prof. Dr. Arshad Ahmad

Process Control and Safety Group,

Universiti Teknologi Malaysia

Strategies to increaseStrategies to increasePerformance andPerformance and

ProfitabilityProfitability

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Page (3)

Recall: Process Control OverviewRecall: Process Control Overview

Planning andScheduling

RegulatoryControl

AdvancedProcess Control

Real-TimeOptimisation

Process

Process Computer

DCS

Plantwide Computer

Page (4)

SURVEY OF SOME AVAILABLESURVEY OF SOME AVAILABLETECHNOLOGIESTECHNOLOGIES

Qin and Badgewell (2003). A survey ofindustrial model predictive control. ControlEngng Practice, 11, pp733-764.

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History of MPCHistory of MPC

Reproduced from Qin and Baldgewell (2003)

Page (6)

First GenerationFirst Generation

Linear Quadratic Gaussian (LQG) Kalman and co-workers (1960s) Linear state-space model

Identification and Command (IDCOM) Richalet (1976 conf), Richalet (1978,automatica) Impulse response model

Dynamic Matrix Control (DMC) Cutler & Ramaker,1979; work at shell in 1970s linear step response model

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Weakness of First Generation MPCWeakness of First Generation MPC

IDCOM and DMC algorithms represent had an enormous impact on

industrial process control

served to define the industrial MPC paradigm.

Excellent control of unconstrained multivraiableprocess

Main Weakness Constraints handling on ad-hoc basis

Page (8)

Second Generation, QDMCSecond Generation, QDMC

QDMC Cutler, Morshedi, & Haydel (1983), Garcia and

Morshedi (1986)

Key Features linear step response model for the plant; quadratic performance objective over a finite

prediction horizon; future plant output behaviour specified by trying to

follow the setpoint as closely as possible subject toa move suppression term;

optimal inputs computed as the solution to aquadratic program.

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Weakness of Second Generation MPCWeakness of Second Generation MPC

Growing control requirements More constraints

Fault tolerant

Main weakness No clear way to handle infeasible solution

Page (10)

Third Generation MPCThird Generation MPC Setpoint

Improvement of IDCOM called IDCOM-M (1988)

Introduced Setpoint multivariable controlarchitecture (SMCA) Combination of identification, simulation, configuration and

control products.

Shell SMOC

Bridge between state space and MPC algorithm

Adersa Hierarchical constraint control (HIECON)

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33rdrd Generation MPCGeneration MPC

Key Features linear impulse response model of plant; controllability supervisor to screen out ill-

conditioned plant subsets; multi-objective function formulation; quadratic

output objective followed by a quadratic inputobjective;

controls a subset of future points in time for each

output, called the coincidence points, chosen from areference trajectory; a single move is computed for each input; constraints can be hard or soft, with hard

constraints ranked in order of priority.

Page (12)

Fourth GenerationFourth Generation

RMPCT Combination of RMP (honeywell) and CT (Profimatics)

DMC PLUS Combination of DMC and SMCA

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Linear MPCLinear MPCCompanyCompany Product NameProduct Name DescriptionDescription

Adersa HIECONPFCGLIDE

Hierarchical constraints controlPredictive function controlIdentification Package

Aspen Tech DMC-PlusDMC-Plus Model

Dynamic Matrix Control PackageIdentification Package

Honeywell Hi-

Spec

RMPCT Robust Model Predictive Control

Technology

Shell GlobalSolution

SMOC-II Shell Multivariable Optimising Control

Invensys Connoisseur Control and Identification Package

Page (14)

Linear MPC TechnologyLinear MPC TechnologyProductProduct TestTest ModelModel EstimationEstimation

MethodMethod

DMC-Plus Step, PRBS VFIR, LSS MLS

RMPCT PRBS, Step FIR, ARX, BJ LS, GN, PEM

AIDA PRBS, Step LSS, FIR, TF,MM

PEM-LS, GN

Glide Non-PRBS TF GD, GN, GM

Connoisseur PRBS, Step FIR, ARX, MM RLS, PEM

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Linear MPCLinear MPC

Model Type FIR (finite impulse response), VFIR (Velocity FIR), TF (Laplace Transfer Function), LSS (linear State Space), ARX (Autoregressive with exogeneous input), BJ (Box-

Jenkin), MM (Multi-modal)

Estimation Method Least Square (LS), Modified LS (MLS), Recursive LS (RLS), subspace ID (SMI), Gauss-Newton (GN), prediction error method (PEM) Gradient descent (GD), global method (GM)

Commercial name for RMPCT is profit-controller AIDA: advanced identification data analysis

Page (16)

Nonlinear MPCNonlinear MPCCompany Product Name Description

Adersa PFC Predictive Functional Control

Aspen Tech Aspen Target Nonlinear MPC Package

Continental Control MVC Multivariable Control

Dot Product NOVA-NLC NOVA Nonlinear Controller

Pavilion Technologies Process Perfecter Nonlinear Control

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Critical Success Factor in Implementing APCCritical Success Factor in Implementing APC

Good Instrumentation /Quality MeasurementInstrumentation

Expert team For install + maintenance.

Management commitment

Operator training

Performance monitoring

Maintenance

MPC AlgorithmMPC Algorithm

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Objectives of MPCObjectives of MPC

1. prevent violation of input and outputconstraints;

2. drive the CVs to their steady-stateoptimal values (dynamic outputoptimization);

3. drive the MVs to their steady-stateoptimal values using remaining degreesof freedom (dynamic input

optimization); 4. prevent excessive movement of MVs;

5. when signals and actuators fail,control as much of the plant aspossible.

Plantwide Optimisation

Local Optimiser

MPC

DCS

Plant

Page (20)

Main IdeaMain Idea

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MPC CalculationMPC Calculation

Read MV, DV, CV Values from process

Output Feedback (state estimation)

Determine controller process subset

Remove Ill-conditioning

Local steady state optimisation

Dynamic optimisation

Output MVs to process

Page (22)

Output FeedbackOutput Feedback

Use available measurements to estimate the dynamicstate of the system However, most commercial MPC dont use the concept of

process state

Rely on ad-hoc biasing scheme

Implications (for using ad-hoc biasing) SMOC and aspen Target use EKF DMC-Plus use rotation factor

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Determining the controlled sub-processDetermining the controlled sub-process

Which MV and CV to be used If measurement status of a CV is good, the it

should be controlled.

The MV must also be in good status Low level control for MV,e.g valve position

Sensor faults

Only complete failure detected and not used RMPCT and DMC Plus

Critical and non-critical sensor failures.

For non-critical, control is still implemented

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Removal of Ill-ConditionRemoval of Ill-Condition

High condition number in the process gainmatrix means that small changes in controllererror will lead to large MV moves

RMPCT uses SVT (singular value thresholding)

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Local Steady State OptimisationLocal Steady State Optimisation

Optimal target may change due to disturbanceany time steps. So, local optimisation isrequired.

Methods used LP: COonnoisseur

QP: RMPCT,PFC,Aspen target, MVC

DMC Plus uses a sequence od LP and/or QP

Page (26)

Dynamic OptimisationDynamic Optimisation

MPC computes a set of MV adjustment thatwill drive the process to the desiredoperation.

Common Objective Function

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Four conflicting contributionFour conflicting contribution

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Page (28)

The objective function is subject toThe objective function is subject toconstraintsconstraints

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Page (29)

Numerical SolutionNumerical Solution

Linear MPC Technology Quadratic Programming

Nonlinear MPC Technology Uses more complex methods

E.g. Aspen Target uses Newton type algorithm, NOVA-PLC usesNOVA optimisation package

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Advanced Process ControlAdvanced Process Control

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Page (32)

Process Control OverviewProcess Control Overview

Planning andScheduling

RegulatoryControl

AdvancedProcess Control


Process

Process Computer

DCS

Plantwide Computer

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Page (33)

The needs for optimisationThe needs for optimisation

ProductionSpecification

Competition

NewRegulations

EquipmentWear & Tear

To maintain/increase profitability process plantmust go beyond standard practices

Variation inFeedstock

Interruptions ofUtilities

Page (34)

When to apply optimisationWhen to apply optimisation

Sales limited by production. Here, throughput should be increased.

Sales limited by market. Efficiency at the current production rate must be

improved.

Large throughputs. Small savings in production costs per unit throughput are

greatly magnified.

High raw material or energy consumption.

Losses of valuable or hazardous components throughwaste streams.

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Needs for RTO (Example)Needs for RTO (Example)

Problem Profits in Petroleum Refining is Marginal, varying

feedstock, large throughput Optimisation Problems

Unit-based: Crude Distillation Tower Plant-wide : All areas of a refinery

Multiple Objectives Quality Raw Materials Services: energy, water, steam ... Waste Minimization

Page (36)

RTO Block DiagramRTO Block Diagram

NumericalOptimizationAlgorithm

ProcessModel

EconomicParameters

EconomicFunctionEvaluation

Optimization

Variables

EconomicFunctionValue

ModelResults

Initial Estimateof Optimization

Variables

OptimumOperatingConditions

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Plant Optimization HierarchyPlant Optimization Hierarchy

Real-Time Optimisation- Rigorous steady state model- on-line tuning

- Targets automatically implemented

Planning (LP/NLP)Plant Information

System

APCController

APCController

APCController

Operating Conditionsand Constraints

OptimalTargets

Plant economicsstrategic ConstraintsInventory constraints

Future ModelUpdates

Operating conditionsOn-line analysers

Lab Data

Page (38)

Formulation & Solution of OptimizationFormulation & Solution of OptimizationProblemsProblems

Identify the process variables

Select performance criteria and develop amathematical expression for the objective function.

Develop the models for the process and the constraints.

Simplify the model and objective function.

Simulate and validate process model

Data reconciliation Compute the optimum.

Perform sensitivity analysis

Ready for Implementation

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Page (39)

Example: Temperature controlExample: Temperature control

Feed

Steam

Product

FT

TT

TemperatureController

Flow Setpoint

OptimizerTemperature setpoint

FC

Page (40)

Successful RTO Requires AdvancedSuccessful RTO Requires AdvancedProcess ControlProcess Control

Optimization benefits are achieved byconsistently pushing the process to the mostprofitable constraints

Traditional PID-type constraint-selectorcontrollers give poor performance against

multiple constraints Feedback-only controls cycle at constraints

Pairing of constraints and manipulated variables isfixed in controller design

Retuning needed as constraints change

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Successful RTO RequiresSuccessful RTO RequiresAdvanced Process ControlAdvanced Process Control

Optimal operating conditions often located nearconstraints

Predictive multivariable constraint controllersare designed to run at multiple constraints Predictive nature allows constraints to be corrected

before they are violated

Multivariable controls handle allconstraint/manipulated variable permutationsmathematically

Page (42)

RTO Interface to APCRTO Interface to APC

Outputs to APC controllers For selected optimization variables

Sets MV and CV targets Or pinches MV and CV limits

RTO limits are the same as APC limits RTO includes the same constraints as APC Operator interface via regulatory control

computer RTO specific graphics APC graphics to show RTO targets

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RTO Interface to APCRTO Interface to APC

Two basic APC configurations Minimum movement

Configures all MVs to move only in response to meet RTOtargets or for CV limit violations

Will not move process to take advantage of opportunitiesbetween optimizations

Limited optimization Leaves some MVs for controller to maximize/minimize (no

RTO targets) as conditions permit between optimizations

Controller pricing set up to push constraints

RTO targets usually configured as max or min instead of asetpoint

Page (44)

APC Interface toAPC Interface toRegulatory Control SystemRegulatory Control System

Outputs to regulatory system Usually setpoints of PID controller

Sometimes moves the valve directly

Includes nonlinear transformations

Valve positions Ratios

Column pressure drop

Operator interface via the regulatorycontrol computer

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Real-Time Optimization BenefitsReal-Time Optimization Benefits

Page (46)

APC and RTO Benefits versus Project CostAPC and RTO Benefits versus Project Cost

20 40 60 80 100

Investment%

Potential %

AdvancedRegulatory

Control

DCS

AdvancedProcessControl


20

40

60

80

1

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Page (47)

Real-Time OptimizationReal-Time Optimization

Determines optimal targets for a single unit

More degrees of freedom than planningmodel

Based on fundamental non-linear models of asingle unit (CDU, FCC, etc.)

Optimization cycles 4-12 executions per day

Model calibrated to plant each run

Uses current economics and constraints

Page (48)

Real-Time Optimization BenefitsReal-Time Optimization Benefits

Optimization and Control projects are veryprofitable Payout times are often measured in months

Implementation costs continue to decrease

Maintenance costs continue to decrease

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Plant Operation by Dr AA 2008 Page 2 3 -#

Page (49)

Real-Time Optimization SystemReal-Time Optimization SystemComponentsComponents

Process model

Large-scale optimizer to optimize the processmodel

Real-time implementation system Steady-state detection

External data interfaces to read/write plant data andparameters

Data validation and conditioning

Interfaces Engineer

Operator

Page (50)

The Principle of OptimalityThe Principle of Optimality Rutherford Aris, 1964Rutherford Aris, 1964

If you dont do the best you canwith what you happen to have,youll never do the best you mighthave done with what you shouldhave had.

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