TEST 4

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ENQUIRY NO. 876

28 industrial automation asia | Oct/Nov 2008

control point

O lefins plants have evolved into highly integrated and flexible processing systems that can

profitably adjust to ever-changing landscapes of raw material availability and market demand for high purity Olefins products.

Advanced Process Control technologies such as Model Predictive Control (MPC) are commonplace in Olefins plants and have greatly improved the consistency of product quality and constraint protection and have typically resulted in quick payback on investment. Another advanced technology, on-line optimisation promises even greater benefits.

Application of traditional on-line optimisation technology however remains a challenging task primarily because of long process dead times, mixed process dynamics and frequent disturbances. The steady state models

used in traditional optimisation are inadequate representation of the on-line behaviour of most commercial Olefins plants.

A unique approach to on-line optimisation of dynamic processes is presented in this paper. In this approach plant steady state is not a requirement for on-line optimisation. This approach fully utilises the MPC dynamic models for optimisation.

Appl icat ion of th is on- l ine o p t i m i s a t i o n t e c h n o l o g y h a s resulted in continuous savings for some very dynamic Olefins plants. This paper briefly describes this dynamic optimisation technology and summarises experiences from several completed and in-progress Olefins plant optimisation projects.

Ethylene PlantsEthylene is the largest volume

industrially produced organic material. Current worldwide production is projected to increase at a rapid pace that exceeds the rate of GDP increase for the foreseeable future.

A typical modern plant produces in excess of a billion lbs per year. Feedstock to Ethylene plants ranges from light Ethane/Propane mix (E/P) to heavy naphtha and vacuum gas oils. Most plants are designed with raw material flexibility in mind to make use of opportunities that arise from time to time. Majority of ethylene produced is used in the production of polymers and ethylene derivatives such as Ethylene Oxide and Glycol.

Depending on the plant location, the ethylene produced could be supplied to a network of ethylene product pipeline or to dedicated derivatives unit. Plants that produce for dedicated downstream units are

A unique approach to on-line optimisation technology has resulted in continuous savings

for some very dynamic Olefins plants. By Paul Yap (L) and Leong Hon Mun (R),

advanced solutions business, Honeywell Process Solutions Southeast Asia

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Oct/Nov 2008 | industrial automation asia 29

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more challenging to operate as they have to continuously adjust to the demand swings of the downstream facilities.

Ethylene plants in general have some unique characteristics that have a strong bearing on the optimiser design. These are:• Semi-continuous operation of

furnaces.• Frequentfurnacefeedswitching.• Frequentfeedqualityvariations.• Signif icant product demand

variation.

MPC TechnologyModel Predictive Control (MPC) refers to a class of control algorithms in which dynamic process models are used to predict and control a process. MPC is well suited for high performance control of constrained multivariable processes because explicit pairing of Controlled Variables (CV) and Manipulated Variables (MV) is not required and constraints are directly imbedded in the problem formulation.

One drawback in conventional

MPC technology is that it requires specification of CV reference trajectories. Reference trajectories are typically set equal to the steady state targets and are not dynamically optimum and are often dynamically infeasible and could result in excessive MV movement without additional move suppression.

Honeywell’s Profit Controller r e m o v e s t h i s r e q u i r e m e n t . It employs a funnel design in its problem formulation that enables a simultaneous determination of both the MV moves and the CV reference trajectories. Solution procedureembedded in the controller is called the Range Control Algorithm (RCA).

For practical applications, there usually are an infinite number of solutions to a MPC control problem. In Profit Controller, a minimum effort solution is sought which means minimum movement to the process. The minimum effort solution brings a significant robustness to the control solution. This treatment of robustness differs from the standard robust control design in that the control robustness is gained from the freedom provided

by CV high/low bounds and not by specification of model uncertainty.

In Profit Controller, the complete unit dynamic optimisation is solved together using the Range Control Algorithm. It not only solves for the steady-state optimum, but also determines the trajectory of ‘how to get there’ while handling the control tasks currently at hand. This trajectory is a combination of control response and economic optimisation path.

Note that economic ‘setpoints’ could instead come from a plant-wide optimisation solution, in which case Profit Controller serves as a dynamic enforcer of optimisation setpoints. This is how it integrates with Profit Optimiser.

Approaches To RTOFunctionally, Real Time Optimisation (RTO) is a supervisory control layer. RTO continuously strives to maintain the plant operating conditions at the most profitable point. In order to achieve this functionality, an RTO system must have the following three capabilities:

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1. to determine the current state (StateEstimation),

2. to determine the optimum state (OptimumState),

3. to determine a path from the current to the optimum (Dynamic Path).

State Estimation refers to thedetermination of the current state of the system. The plant is a dynamic entity with a wide mix of process dynamic characteristics (fast, slow, inverse and delayed responses) and all manners of process disturbances. This means that a ‘snapshot’ of process data gives an incomplete and sometimes incorrect view. A complete assessment of current state requires analysis of process history.Stateestimationdefinesbothwhere the plant is right now as well as where it can be expected to be in the future.

Optimum State refers to thesteady state optimum point. For a system described by a set of algebraic equations, there are useful mathematical tools such as linear algebra and optimality theorems . The task then is to describe the reality of the plant operation into a set of algebraic equations. Having defined the equations, finding the optimum is just a mathematical exercise.

Dynamic Path refers to the path taken in going from the current state to the optimum state. In general a multitude of paths exist and the most direct path is not necessarily the best path and may not even be feasible for a process with dynamic interactions.

Dynamic OptimisationProfit Optimiser is a paradigm shift in RTO. Instead of a steady state optimisation model this optimiser has a dynamic model of the process. The dynamic optimisation model is not developed from scratch. Instead the starting point for the optimisation model is the MPC control model of the process. The MPC model forms the base optimisation model. Limited amount of additional modeling effort is required to tie various controller models using dynamic bridge models,

source-clone models and combined constraint models .

SinceMPC is a prerequisite foroptimisation, use of MPC models for base optimisation model represents a significant savings of engineering effort both during the initial development of the optimiser, and more importantly in the long term maintenance of the optimiser.

Profit optimiser is based on a dynamic model of the process, therefore, steady state operation of the plant is not a prerequisite for optimisation. At any given time the optimiser is aware of the various transients in the process and has a prediction for their consequences, that is, it has a prediction of steady state values for all variables.

In this framework, the steady state optimisation problem is formulated as a semi positive definite Quadratic Programming (QP) problem for which solution is fast and guaranteed. QP formulation along with frequent gain updatinginessenceamountstoaSQPtype optimisation.

The optimiser typically executes at the frequency of MPC controllers, usually once every minute. At each execution of the optimiser, the optimiser gets process feedback. This frequent process feedback compensates for model mismatch, this forms the basis for data reconciliation.

For process areas where non-linearities are significant such as the reaction system, frequent gain updating is performed using cracking yield models.

In this approach, there is a tight integration between optimisation and

controls. Not only does the optimiser pass down the optimum desired values to the controllers, it also passes down the optimisation speed factor to each of the controllers that takes into account the dynamic characteristics of each of the controllers.

This dynamic optimiser moves the process towards the optimum along a minimum energy path that eliminates optimiser induced dynamic violations of Controlled Variables. In addition, the dynamic optimiser also helps the process better cope with disturbances by applying corrective action to multiple controllers as and when needed.

Real Time SolutionIn summary, Profit Optimiser is an effective technology for optimisation of dynamic processes such as the ethylene process.Sincethisoptimiserisbasedon dynamic process models, steady state operation for the process is not a requirement for optimisation.

The optimiser formulates a semi positive definite QP that can be solved efficiently with guaranteed optimum, typically at the same frequency as the controllers. The optimiser allows for on-the-fly gain updating, which in essencemakesitaSQPoptimiser.

Since the controller modelsprovide bulk of the optimisation model, initial development efforts as well as long-term maintenance efforts for the optimiser are greatly reduced. On the whole, Profit Optimiser is an optimisation solution that can be effectively used to optimise real world dynamic problems in real time.

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control point

ue to the lack of control c o o rd i n a t i o n , i n d u s t r i a l power plants are typically not

fully optimised. The purpose of a coordinated control and optimisation system is to improve plant stability and responsiveness to demand or supply disturbances, and to minimise overall fuel costs. The specific objectives include, but are not limited to the following:

1. Operational objectives a) Reduce process variability in: • Steamtemperatures • Steampressures • Boilerload

b) Maintain process variables within constraints:• Boilersteamflows,pressures,

and temperatures• FD-and ID-fans, feedwater

pumps• Turbine-generator power

output• Tie-lineload

2. Economic Objectives a) Improve power generation

efficiency:• Increaseturbinesteaminlet

temperatures• Minimiseturbineextraction

andexhaustpressures• Allocate load to most

efficient turbines• Maximise backpressure

power generation

b) Improve steam generationefficiency and reduce fuel costs:• Minimise high cost fuels,

maximiselowcostfuels• Reduceloadvariationsfor

better combustion control• Allocate load to most

efficient boilers

c)Optimisein-housegenerationvspower purchases or sales:• UsePRV’s to reduce own

generation when power cost is low

• Maximise backpressurepower generation

Coordination

The

Power A coordinated control and optimisation system can improve the stability and responsiveness of a power plant. By Pekka Immonen (L), manager of advanced control and optimisation, Ted Matsko (M), principal consultant, optimisation services, Marc Antoine (R), programme manager, plant optimisation, ABB Power Systems



• Condensing power fromlow-costfuelsvs.purchasedpower

• Blow-off power from low-cost fuel vs purchased power

Coordinated Control & OptimisationThe coordinated contro l and optimisation solution adjusts the load demand of each turbine, duct burner, boiler, and other plant components based on the steam and power side control requirements and economic optimisation.

Because of the complexity ofthe steam and power systems, it is difficult to develop coordinated control strategies using the base controls only, and optimisation at that level is practically impossible. Therefore, the solution involves a combination of base level control changes and advanced, supervisory control strategies.

The coordinated control and optimisation solution is based on a multi-levelapproach(Fig1).AtthetoplevelaRealTimeOptimisation(RTO)programme monitors the current plant situation and uses efficiency data to optimise the load scheduling of individual plant equipment. The programme then dispatches these orders to an Advanced ProcessControl (APC) layer,whichprovidescoordinated, decoupled control of pressures, flows, and power generation. The base control level modulates the actuators affecting the plant to maintain the process quantities at the setpoints determined bytheAPClayer.

In advanced controlmode thecoordinated control and optimisation application essentially determines the loading of the individual plant components. In order to improveplant stability, the solution uses all process components to respond quickly to large disturbances, allowing these components to temporarily deviate from their economic optimum targets. Once the process upset or disturbance is over, the process is automatically brought back to its

optimum conditions. No operator intervention is required to achieve this.

The various plant components can be taken in and out of optimisation by operatordiscretion.Allchangestakeplace bumplessly, and the optimisation algorithm recognises the available degrees of freedom for any given combination of component modes.

Real-Time OptimisationThe optimisation layer (RTO) usesLinear Programming (LP) orMILP(MixedIntegerLP)technology.LPisa common optimisation methodology used for planning and scheduling applications in many industries, and it is particularly well suited for cogeneration optimisation.

The LPminimises an objectivefunction(egtotalcostoffuel),whileadhering tomany constraints. Forexample,LPdetermineshowtobestallocate load to the turbines, duct burners and boilers and the APCsoftware responds to these commands whilecontinuouslymaintainingthetie-line load and monitoring all process constraintsinreal-time.

Someof theconstraintsarepartof the plant model, representing

mass and energy balances. Other constraints represent capacity limits (maximumfuelflowrates,generatorand transformer KVA limits, etc).Tie-line load to the localpowergridis another constraint which can be included in the optimisation constraints.

TheMILP capability is utilisedwhen discrete decisions need to be made.Forexample, lowcostmarketprice energy may not be available to theplantbeforethemoreexpensivebase energy has been used up. Alinear or nonlinear solver without MILPcapabilitywouldusethelowercost energy first, which would not be possible contractually.

Advanced Process ControlTheAPClayerusesModelPredictiveControl(MPC).MPCisamultivariablecontrol algorithm and like LP,MPCuses a model, but this model is a dynamicmodel (time-dependent).MPCrunsatamuchhigherfrequencythan the LP, typically less than 10seconds compared to several minutes fortheLP.

The MPC solut ions for theapplications described in this paper use a process model to predict

Fig 1: Typical architecture for coordinated control optimisation.

Real-Time Optimization(RTO)

Base Regulatory Control(Integrated DCS)

TargeTs

seTPOINTs

aCTUaTOrsseNsOrs

PrOCess valUes sTaTes CONTrOl mOdes Advanced Process Control

(APC)

POwer PlaNT


cONtrOl pOiNt

the effect of moving multiple base control setpoints and to estimate the level of disturbances acting on the process. This results in a greatly simplified control structure withmucheasiertuning.Inaddition,MPC achieves smoother transitionfrom one constraint to another and shows superior performance during load changes and process disturbances.

ThebenefitsofusingMPCforsuchprocesses are: • Reducingvariance,thusobtaining

better control performance.• Abilitytooperateclosertooptimal

constraint(design)limitsbecauseof lower variance.

• Operatingclosertolimitsreducesoperating costs or increases production.

Duetothemanyconstraintsandcomplex interactions in a powerplant, multivariable MPC is wellsuited for coordinated control of the plant since theMPC algorithm‘knows’ the dynamic behaviourand the interactions between the process quantities, and controls all the variables at the same time, in a coordinated fashion.

This is different from traditional control (eg PID control), whereeach controller has one input and one output. The knowledge of the process behaviour is contained in the dynamicmodelsoftheMPCcontrollerand they predict the model output response to model input changes. The inherent knowledge of the process constraints also eliminates the need for complex over-ride schemes,simplifying the design of the control system.

The advantage of this solution is theabilitytousestate-spacemodelsandmulti-objective optimisation with prioritised control objectives, which is particularly useful for power plant optimisation. The benefits include much improved observer -based est imat ion ofprocess disturbances, and more robust handling of multiple levels of constraint priorities, as compared to otherMPCalgorithms.

Case In PointThe industrial power plant shown in Fig 2 generates steam in twoprocess boilers and one power boiler and delivers it to the various users at different pressures and temperatures. On average, the plant generates approximately 90percentof the electrical power needed by the manufacturing process with onedouble extraction-backpressureturbine and one straight - f lowcondensing turbine. The balance is purchased from the local power utility.Asthepurchasedpowerpriceis much higher than the incremental cost of its own condensing power, the amount of power imported from the grid should be minimised. This is not an easy task for the base controls and the operators, due to the following operational challenges:• There are large fluctuations in

process s team and power demand

• The amount of steam fromthe process boilers cannot be controlled, and is also subject to large variations

• Thepowerboilerisalsousedforwaste incineration. This combined with the large variability in the boiler load makes it difficult to maintain adequate control of the boiler steam pressure and temperature

• Becauseofthevariationstheboiler

is frequently overloaded, causing serious maintenance problems

The energy demand variations arealsoreflectedinthetie-lineload.There are frequent situations where power is exported to the grid.Thisis not economical since the power company does not provide any credit fortheexportedpower.

In order to improve the plantope ra t i ons , a bo i l e r - t u rb inecoordinated strategy was developed, usingMPCtechnology.Theobjectivefor this plant was to reduce pressure and temperature variability, and to maximise itsownpower generationwithout exporting power or over-loading the boiler. The various prioritised constraints of theMPCalgorithm were employed to achieve the following functionality:• In a normal situation the plant

is running at one of its load constraints, iemaximum steamflow for the power boiler and minimum or maximum powerfor the condensing turbine, or minimum (zero) purchasedpower.

• Theminimumpressureconstrainthas a higher priority than the boiler steam flow constraint. Incase of high steam demand the power boiler load is increased to itsmaximum limit first. Ifmoresteam is needed, the condensing

Fig 2: Example of optimised Boiler: Turbine coordination.


ENQUIRY NO. 7302

turbineloadisreducednextuntilitsminimum is reached. If thedemandstill exceeds supply theboiler pressure starts dropping. At that point the controllerwillincrease the boiler steam flow beyond the maximum limit ifneeded to keep the steam pressure above its minimum setting.

• Themaximumpressureconstrainthas a higher priority than the minimum power purchase limit. In case of low steam demand,the power boiler steam flow is minimised first, and afterwards the condensing turbine will absorb the excesssteamevenifpowerneedstobeexported.

ByimplementingtheAPCsolution,the achieved improvements are:• Reducedstandarddeviationby80

percent on steam temperature and pressure,and60percentonpower

boiler steamflow.The trends in Fig 3 show the improvementrealised throughAPC on steamcontrol (temperature, pressureandflow).

• Becauseofthereducedvariabilitythe steam temperature setpoint could be increased by 10 degC, and the averageboiler steamflowcouldbeincreasedby29t/h (6 percent). As a result, morepower could be generated in-house, reducing importedenergy. This adds up to total yearly energy savings of several percent.

• In addition, the improved steampressure contro l not on ly reduces the number of boiler trips due to pressure or drum level f luctuations, but also reduces lifetime consumption of cri t ical thick-wal led boi lercomponents.

Fig 3: Improved steam pressure, Temperature and flow control.

Animportantcontributiontothesuccess was the early participation of the plant’s engineering andoperations staff. Operator acceptance was bolstered by the fact that the optimisation system user interface was entirely implemented using the commonhuman-machineinterfaceoftheplant’sdistributeddigitalcontrolsystem (DCS). An example of theoptimisation system displays is shown inFig4.

A Multi-level ApproachBecauseof the complexityofmanyindustrial power plants, it is generally difficult to develop good coordinated control strategies using the base controls only, and true optimisation at thatlevelispracticallyimpossible.Byusingamulti-levelapproach,thenewsolutions presented in this paper are able to control all process variables in a coordinated, decoupled manner.

These solut ions have been implemented in different projects and resulted in significant improvements such as reduced process variability; better response to process upsets, decreased equipment stress, and improved plant stabil i ty. This translated to substantial economic benefits such as increased power or steam generation efficiency, optimised in-house generation vs purchasedenergy, and reduced down times.

Yearly savings in operating costs of several percent and payback times of lessthanoneyeararenoexceptions.This improvement solution can also be implemented with no down time on the stem generation which is critical for most of the plant.

Fig 4: Boiler-Turbine optimisation user interface.


software & Networks

he requirements for production automation are continually rising. It takes flexible production lines,

detailed product tracking or close cooperation between man and machine to open up the last meters to the sensors and actuators for continuous communicat ion. The f ie ldbus-independent communication interface

IO-Link now offers a uniform standard that applies to all manufacturers.

Many sensors and actuators now have microprocessors that control displays and parameterise and store configuration data. To date, manufacturing automation has offered only proprietary solutions that have enabled sensor and

actuator intelligence to be used for communication purposes as well. Customer acceptance has been correspondingly low.

That’s why 14 manufacturers of sensors, actuators and automation systems began an initiative with the goal of overcoming the binary standard interface shortage and making additional functionalities of modern sensors and actuators for the automation system centrally accessible.

The result of this cooperation is IO-Link, a fieldbus-independent communications interface for sensors and actuators. With the IO-Link, a standard has been created that makes it possible for the actuators and sensors to communicate with the respective device, regardless of the manufacturer.

Combined Operation PossibleThe IO-Link integrates conventional and intelligent actuators and sensors

software & Networks

IO-Link is designed to facilitate communication between sensors/actuators from different

manufacturers and higher-level systems. By Klaus Ebinger, product manager of

process sensors, Hans Turck

To The

Last MeterIntelligentDown

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