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Syngenta Technical PresentationHuddersfield, October 2002

PROCESS OPTIMISATION

with

ON-LINE SIMULATION

by

John E. Edwards

P & I Design Ltd MNL045 10/022 Reed Street, Thornaby, UK, TS17 7AF Page 1 of 16

Tel:+ 44 (01642) 617444

Fax: + 44 (01642) 616447

[email protected]


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Process Optimisation with On-line Simulation

MNL045 10/02Page 2 of 16

Contents

1.0 Introduction

2.0 Process Simulation Fundamentals

3.0 On-line Simulation Techniques

4.0 Process Optimisation

5.0 On-line Simulation Benefits

6.0 Case Studies

References

1. J.E.Edwards, Dynamic Modelling of Batch Reactors and Batch Distillation, Batch Reactor

Systems Technology Symposium, 2001. (Available as download www.pidesign.co.uk)2. F.G. Shinskey, Process Control Systems, McGraw-Hill, 1967.

3. E.Sorensen, Principles of Dynamic Simulation, IChemE CAPE Subject Group Mtg, 2002.4. J.E.Edwards, M.M. Morgan, SCADA Enabled Batch Reactor Modelling P&I Design Ltd.5. J.E.Edwards, SCADA Dynamic Data Exchange-DDE Excel Interface P&I Design Ltd.

6. M.Dodge, C.Kinata, C.Stinson Microsoft Excel 97, Microsoft Press.7. J.E.Edwards, Thermodynamics Guidance Note, UK CHEMCAD Symposium, 2001.


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MNL045 10/02Page 3 of 16

1.0 INTRODUCTION

Chemical processes are carried out using two main types of operation namely batch and

continuous process. There are general characteristics which can be associated with eachtype of operation as follows:

Batch processes Low to medium capital investment.

Low to medium cost to rectify design errors.

Low volume high value products.

Multi-purpose production capability.

Low to medium production costs.

Limited savings from yield improvements and reduced cycle times.

Reproducible processing essential for regulation compliance.

Continuous processes

Medium to high capital investment.

Medium to high cost to rectify design errors.

High volume low value products.

Dedicated production facility.

Medium to high production costs.

Significant potential for savings from increased quality and plant throughput.

Significant benefits form energy and waste minimisation.

A review of the above characteristics would suggest that there are very significant benefits

to be gained on continuous process plant whereas on batch processes the benefits will bemore problem specific with optimum batch process scheduling being the priority.

Batch processes are transient in nature requiring dynamic simulation whereas continuousprocesses are inherently stable making them ideal for steady state simulation which can be

considered a special case of dynamic simulation. However dynamic simulation is used tostudy the behaviour of continuous processes, subject to feed flow and compositionchanges, and the control system performance in achieving optimum conditions.

It will be shown that once a process simulation has been calibrated against real plant

performance it can be used to optimise the production process, predict unmeasuredparameters, monitor equipment condition and as an aid to diagnose operational problems.It has been found that simulation sometimes produces counter intuitive results.

This paper presents an easy to implement, economic solution for interfacing operating

plant SCADA systems with on- line simulation engines, in this case the Chemstationsintegrated range of software products incorporating CHEMCAD. Simulation software issupported by extensive component physical property databases and thermodynamic

options providing the capability to realistically model the process conditio ns.

A training simulator based on a typical batch reactor control system is presented whichallows a detailed study to be made of the reaction characteristics by design and operations

personnel to ensure safe and efficient operation. A steam generation and distribution

system dynamic simulation is also presented in which the excess steam is controlled to apreset rate.


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MNL045 10/02Page 4 of 16

1.0 Introduction (Cont.)

Relatively inexpensive simulation tools can achieve very significant savings in the design

and build process. A study of the project cost impact curves below demonstrate the wellestablished fact that mistakes are more costly to rectify the further you are into the projectlife cycle so to use all available design tools makes sound economic sense.

PROJECT COST IMPACTS

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

PROJECT TIME

COSTIMPAC

Problem Discovery Design Decisions

Process simulation provides a powerful design tool to establish the adequacy of theequipment design and performance at an early stage in the design process.

The SCADA enabled model also provides the facility to integrate proprietarymanufacturers data into the simulation and to study the impact of parameter changes

during a dynamic simulation.

Conceptual Detail Design Procurement Construction Commission


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MNL045 10/02Page 5 of 16

2.0 PROCESS SIMULATION FUNDAMENTALS(3)

In a chemical process total mass, individual component mass and total energy can not be

generated or consumed; in other words mass and energy balances are maintained over atime period. Process dynamics is determined by these fundamental principles.

Accumulation of total mass = Flow of total mass In Flow of total mass Out

Accumulation of component A = A(Flow In Flow Out) + A(Generated Consumed)

A batch process, being a closed system as no mass crosses the system boundary during the

time period covered by the balance, has an energy balance of the form:Net system energy transfer = Final system energy Initial system energy

Where net system energy transfer is work done on the system by its surroundings and heat

transfer between the surroundings.

A continuous process, being an open system as mass crosses the system boundaries as theprocess occurs, has an energy balance of the form.

Total energy in = Total energy out

Input stream total energy (kinetic, potential, internal) + Net system energy transfer =

Output stream total energy (kinetic, potential, internal)

Note that the accumulation, generation and consumption terms have disappeared for a

continuous steady state process yielding the special case of Input = Output.

Process dynamics are further determined by the constitutive relationships of heat transfer,

vapour liquid equilibrium and reaction kinetics:

TAUQ = xKy iii = CCkr BAA =

Before a simulation can be solved certain input variables and chemical/physical property

parameters need to be specified. The degrees of freedom (DoF) is the number ofindependent input variables that must be specified in order to define the process

completely. A process defined by two equations and six variables has a DoF of four.A process stream condition is determined by the intensive variables Pressure, Temperature,Enthalpy and Vapour Fraction. The stream condition is defined by any two of the four

variables, giving a DoF of two.

Selection of the correct thermodynamic models(7) for enthalpy and equilibrium are criticaland must be validated against known data. Incorrect thermodynamic model selectionrenders the simulation meaningless. The simulation engines consist of stand alone equation

based models, which can be user specified, for specific unit operations which can be

connected to each other in an open format manner using streams to create the simulationflowsheet.

A dynamic simulation is performed by initialising and re-initialising a sequence of

calculation steps until the convergence parameters are satisfied. The convergenceprocedure compares the selected parameter values of the current step with the previous

step until the specified tolerance is achieved. A dynamic simulation can be considered tobe a series of steady state simulations which is a feature taken advantage of in on- linesimulation such that steady state simulators can be used for pseudo dynamic simulation.

Simulation failures (95%) occurring at initialisation are due to inconsistent equations, badchoice of specification or wrong syntax. Failures result from illegal arithmetic operations

such as negative number raised to fractional power and division by zero. Failures duringthe step are due to incorrect variable bounds, calculation accuracy or incorrect equations.


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MNL045 10/02Page 6 of 16

3.0 ON-LINE SIMULATION TECHNIQUES

Process plants have been operated using distributed control systems (DCS), complete with

graphical user interfaces (GUI), since early 1970. The advent of PC based supervisorycontrol and data acquisition (SCADA) software systems in early 1980 and their acceptance

as reliable monitoring tools with appropriate operational and safety integrity has seen their

increasing use. Dynamic and steady state process simulation software with open interfacecapability can now be linked on- line with operating process plant. The dynamic data

exchange (DDE) capability of Microsoft Excel can provide a platform for this interface.A typical batch reactor(4) training simulator using these features is shown:

DDE DATA EXCHANGE

CHEMCAD MODEL

DIRECT HEAT / INDIRECT COOL

1

TC

2

3

45

67

12

5 6

7

8 9

10

810

TC

11

1213

341213

15

16

17

18

11

T 300

W 13697

T 138

W 45000

T 138

W 0

T 49

W 45000

T 49

W 13696

T 49

W 31304

T 138

W 45000

T 138

W 45000

Coolant

Heat Transfer OilSupply

Heat Transfer Oil

Return

OLE DATA EXCHANGE


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MNL045 10/02Page 7 of 16

3.0 On- line Simulation Techniques (Cont.)

The data exchange interface(5) can transfer data between Excel and any enabled data cell inthe process simulation by treating CHEMCAD as an Automation Server using object linkingand embedding (OLE) data transfer.

The stream condition is specified using the intensive variables of pressure, temperature andweight fraction. The total stream flowrate and component flowrates are given in the units

selected.On-line process data is downloaded from the SCADA system and data transfer to Excel isachieved by using a DDE interface. In this way data is transferred via Excel to or from the

CHEMCAD model and to or from the on-line SCADA system.Once the data is in Excel(6) the possibilities for improving plant performance on-line are

limitless depending on the users own ingenuity. For example the data can be manipulated tocalculate process target conditions, equipment operational abnormalities and predict process

parameters that cannot be measured.

Data flow and control is shown in the diagram below together with the system format rules.An extremely powerful feature allows a cell conditional statement or function to control the

CHEMCAD model and the SCADA data field. This provides the facility to develop and testvarious advanced control strategies such as adaptive control and control parameterscheduling.

SYSTEM UNIT OP DATA

RULES & DESCRIPTION FORMAT

Equipment 1 Contents Reactor Wall Contents Liquid

REACTOR Temperature Temperature Pressure Volume

SCADA 2CC Ref Row UnitOp

Blank

Description

SCADA Ref Row Data 0.00 0.00 0.00 0.00

Units

UNIT OP DATA

& DESCRIPTION FORMAT

Equipment 11 Contents Controller Proportional Integral Derivative

Reactor Temperature SP On/Off Band Time Time

SCADA & 2CC Ref Row Contents Data 120.00 1.00 25.00 0.00 15.00

Temperature Units C % Mins Mins

Controller

PARAMETER CONTROLLED BY EXCEL

IF((D21-D13)


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MNL045 10/02Page 8 of 16

3.0 On- line Simulation Techniques (Cont.)

Data arrays can be used to predict the performance of specialised process equipment using

manufacturers third party software. This allows the modelling of specialist equipment inCHEMCAD without the need to develop new UnitOps providing significant saving indevelopment time.

For example special heat exchanger configurations such as plate/plate, plate/shell andgraphite cubic blocks can be modelled dynamically by establishing an unfouled OHTCdata array based on fluid temperature and process flow from manufacturers proprietaryrating software and then using a control cell set up for Indirect/Address/Match functions to

predict prevailing the overall heat transfer coefficient from the current operatingconditions.

The operating temperature(C4) and flow(C5) are downloaded from the plant controlsystem and Excel locates the appropriate cell for clean OHTC(K9) which is then applied to

the heat exchanger in the simulation.

The heat exchanger simulation, as shown below, will provide the inlet outlet temperatures

that can be expected and these can be compared with the operating temperatures to predictfouling. It can be shown that the impact of fouling on the effective area is given by

h

U1

A

A

f

C

C

f+=

where AC is the clean HTA, Af is the dirty and UC is the clean OHTC.

T1 T2

t1

t2

T 80

W 600000

T 115

W 600000

T 280

W 95500

T 121

W 95500

tubeside

shellside


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MNL045 10/02Page 9 of 16

3.0 On-line Simulation Techniques (Cont.)

The data that can be made available to Excel enables parameter manipulations to be madefor the study of advanced control techniques(2) in the dynamic process model.

Dual mode control where the control modes Proportional(P), Integral(I) and Derivative(D)can be switched on and off dependent on some derived process parameter is readily

achieved. For example when heating a batch reactor to a set temperature there is apermanent error between controller set point and the reactor contents temperature. If theIntegral control mode was switched on the controller would continue to integrate this error

resulting in its output driving to 0% or 100% leading to measured variable overshoot. Theexample shows the use of P&D modes only until the deviation (error) is less than 10%

when the I mode is switched on.

Inspection of the above example indicates that all the key controller parameters are availablefor manipulation in Excel including driving the controller between Automatic and Manual

modes. The blue cells will contain DDE data call formula to access parameters in the plantSCADA system.

Adaptive control where the control parameters are automatically adjusted to compensate forvariations in selected process parameters is also achievable.

Multiple loop control techniques, such as cascade control, ratio control and override control,

can be investigated for performance and interaction problems.

For steady state on line optimisation the data calls from the SCADA server need to be based

on preset time intervals. This timed data call can be controlled using the VBA macros. Theuse of timing macros also allows for controller set point ramping strategies to be developed

which is an important feature in the batch process industry.


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MNL045 10/02Page 10 of 16

3.0 On-line Simulation Techniques

This technique can be used on steady state simulation, where accumulation in the systemcan be ignored, or on dynamic simulation provided the appropriate Chemstations processmodelling software module is used. The basic software structures available for steady state

and dynamic process simulations are shown below:

The on-line mode is used for the calibration of the dynamic process model with real plant

data and process optimisation. The parameters in the model are adjusted until agreement isachieved with real plant data.

For example the dimensional and operational data for a batch reactor and the componentdata can be entered for the heat transfer fluid and the reactor contents into the CCReACS

dynamic model. The on-line SCADA system monitors the jacket inlet temperature which isdownloaded to the model via the Excel DDE interface. We can now compare the reactor

contents temperature derived by the model with the actual temperature from SCADA andoptimise accordingly.

These techniques can be used in an off-line mode for design validation and optimisation. The designer can study equipment performance under anticipated process fluid physicalconditions. Equipment design parameters can be modified as a function of the measured

and calculated process variables in real time to optimise equipment sizing and scale up.Control system configurations and strategies can be tested for suitability and tuning

parameters optimised.

The off-line mode can also be used as a training simulator providing operators with the

opportunity to study the behaviour of a virtual process plant using the actual SCADAdisplay used on the real process plant.

SCADA

SYSTEM

OPEN

INTERFACE

PROCESS

MODEL

PROCESS

PLANT

EExxcceell

Interface

PPhhyyssiiccaall PPrrooppeerrttiieess

DDaattaabbaassee

CCHHEEMMCCAADD

CCCC--TTHHEERRMMCCCC--RReeAACCSS

CCCC--

DDCCOOLLUUMMNN


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MNL045 10/02Page 11 of 16

4.0 PROCESS OPTIMISATION

Process optimisation with on-line simulators is now recognised as an essential management

tool to achieve optimal asset utilisation and performance. This technique has been appliedsuccessfully to continuous process plant using steady state simulators in a pseudo dynamic

mode where process data is sampled on a timed interval basis and compared with the steady

state simulation results. For any on-line optimisation application to be a success thesimulation must be calibrated to represent the real process as closely as possible and be

operationally robust.

Once a process simulation has been calibrated the potential for improving plant performanceand operational efficiency are significant as it provides a powerful management tool to assistin identifying process and equipment problems at an earlier stage than might otherwise have

been practicable. There are several key areas where this can be achieved.

1 Operations Optimise process cycle times

Predict plant shutdowns for catalyst change and equipment cleaning Optimise process yields Study sensitivity of yields to process parameter changes

Study sensitivity of energy consumption to process parameter changes Test different control strategies and tuning parameters

2 Monitoring Data reconciliation and process fault diagnosis

Predict process parameters that cannot be measured on-line Predict temperature and composition profiles in columns Predict composition profiles in reactors

Estimation of physical properties in real time Environmental auditing and reporting

3 Maintenance Equipment fault diagnosis

Predict hydraulic conditions in columns to identify internals failures Identify deterioration in equipment performance

Predict heat transfer surfaces fouling Troubleshooting and instrumentation fault diagnosis

4 Economic Stream properties can be used for detailed heat and energy balance studies

Equipment efficiencies can be calculated on-line and compared with thetheoretical efficiencies

Total plant energy consumption can be calculated on-line and compared with the

theoretical energy consumption to reduce manufacturing costs and save energy


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MNL045 10/02Page 12 of 16

5.0 ON-LINE SIMULATION BENEFITS

One of the main benefits that can be attributed to on-line simulation is providing

management with a significant potential to improve their economic performance in theoptimisation of asset utilisation.

Specific benefits accruing from the use of this capability are extensive and include:

Improved operator understanding reduces costly errors and achieves consistentoperation at economic optimum. It allows operations personnel to identify,

troubleshoot and correct problems more readily enhancing production efficiency andsafety.

Improved process yields and efficient use of energy due to the better understanding.

Monitoring, diagnostic and maintenance planning tool reducing costly equipmentfailures and minimizing down-time.

The ability to change model parameters during the simulation run time provides a powerful

feature for the study of dynamic process responses to process upsets. Specific benefitsresulting from this capability are summarised below:

Study effect of feed and recycle composition changes. Study effect of process parameter change such as agitator speed and circulation rates. Study effect of control loop tuning parameter changes.

Investigate utility stream restrictions on production throughputs.

Linking the process simulation to SCADA provides improved visualisation facilities which

are of benefit in the on- line and off- line modes:

SCADA real time display facilities allows trending of measured and calculatedprocess parameters leading to enhanced process understanding.

SCADA history facilities allows storage of process variables and settings for future

analysis.

Dynamic simulation techniques allow the development of inexpensive process specifictraining simulators which can reproduce specific process situations to be studied. Start upor shutdown sequences, upset or emergency conditions and new control strategies are

typical scenarios. Correct application of training simulators can lead to accelerated start upof new or modified plants, more reliable operation and faster recovery from process

disturbances.

Finally using these techniques as an off-line design tool allows identification of potential

problems at an early less costly rectification stage of the project, provides an increasedconfidence level in the design and minimises costly delays attributable to design errors

during start up.


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MNL045 10/02Page 13 of 16

6.0 CASE STUDIES

Case studies include bleaching chemical production by continuous process, methanol

distillation by batch and continuous and training simulators for batch reactor control andboiler control and steam distribution systems. The batch reactor simulator is shown.

6.1 Batch Reactor Training Simulators(1)

A series of system configurations, which are commonly used in the pharmaceutical and finechemicals industry, have been developed. Two typical reactor systems are shown with other

configurations having various heat exchanger and control arrangements.

6.1.1 Basic Direct Heat / Direct Cool Configuration

6.1.2 Direct Heat / Indirect Cool Configuration

The batch reactor simulator shown demonstrates the use of a typical cascade control systemwhere the primary measurement is the reactor contents and the secondary measurement isthe jacket inlet temperature. The controllers in the model receive the set points, tuning

parameters and auto/manual select logic from the SCADA system and the simulationprovides process variables, controller outputs and heat exchanger parameters to the SCADA

for display and trend.

1

2

3

4

56

7

TC

8

TC

9

1 2 3

5

67 8 9

11

12

10

13

14

15

10

4

STEAM

9 barg

COOLING WATER

SUPPLY

OVERHEAD

CONDENSATE

COOLING WATER

RETURN

BATCH REACTOR JACKET SERVICESBASIC HEAT/COOL CONTROL SYSTEM

1

TC

2

3

45

67

12

5 6

7

8 9

10

810

TC

11

1213

341213

15

16

17

18

11

T 300

W 13697

T 138

W 45000

T 138

W 0

T 49

W 45000

T 4 9

W 13696

T 4 9

W 31304

T 138

W 45000

T 138

W 45000

Coolant

Heat Transfer Oil

Supply

Heat Transfer Oil

Return


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MNL045 10/02Page 14 of 16

6.1.2 Direct Heat / Indirect Cool Configuration (Cont.)

The SCADA screen shot below shows a typical graphical representation of a batch reactor

system incorporating the primary and secondary controller face plates for the operatorinterface. The graphic operates dynamically during the run time and the operator is unable todistinguish its behaviour from the real plant. The trend plots show a dual mode control

strategy where the Integral Action Time (Ti) is switched on when the deviation between

reactor contents temperature and primary set point is


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MNL045 10/02Page 15 of 16

6.2 Steam Generation and Distribution System

A typical steam distribution system simulation is shown with two fixed flow users (streams

17 & 30) and one variable flow user(stream 31) which is specified in the model as theexcess steam requirement. Excel is used to calculate the total heat duty of the steam boiler

based on a total system heat balance using the CHEMCAD stream enthalpies and specific

heats. The boiler level is controlled dynamically using a three element feedforward controlmodel. This example, though mathematically trivial, would involve complex modelling

techniques to achieve the same result in a simulation engine.

13

FC

4

5

2

6

2

LC

6

8

5

11 12

Blowdown

Boiler Steam

Drum

Level Trim

SteamFlow (S)

Generic Steam Generation Systemwith Three Element Level Control

ProcessSteam (Fp)

1011

13

9

19

16

21

12

15

13 14

15

20

9FC

17

3

22

18

423

Deaerator

BFW Make Up (Fm)

Turbine (10/3 bar)BFW Flow (F)

19

16

24

20

1025

F = S

Vent

Steam (V)

Recycle (R)

Fm = Fp + V

21

22

826

27

BFW Flow (F)

Steam Flow (S)

7

24

17

29ExcessSteam

30

31

1

14

7 18


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MNL045 10/02Page 16 of 16

6.2 Steam Generation and Distribution System (Cont.)

The simulation flowsheet presentation does not provide a clear view of the process under

study to untrained personnel and has serious limitations for visualisation. The SCADAmimic provides a clear presentation together with a real time indication of the process andsimulation parameters. However drawing features available in Excel allows for a clear

visualisation of the simulation, see below, which could be used by operations personnel. The

data cells are positioned in such a way to allow for the graphical presentation and cellswhich allow model parameter changes are masked by a graphical object.

State

0

Total mass rate Temperature Enthalpy

19142 180 53132

kg/h C MJ/h

Calc lev 1

3.0 Enthalpy Total mass rate Enthalpy Total mass rate Enthalpy

Heat Absorbed Fuel Usage(SCF) m Heat Duty 2.776 9142 25376 5000 13878

44320 91142 44320 MJ/kg kg/h MJ/h kg/h MJ/h

MJ/h MJ/h

Flow Set Point Enthalpy

Enthalpy Total mass rate Enthalpy 10000 22160

0.56 29620 16652 kg/h MJ/h

MJ/kg kg/h MJ/h

5.5

3.0

Equip. 5 PIDC

Ctrl output

20

1.0

Excess Steam Settin

Stream 31 Stream 17

Stream 6

Equip. 1 DVSL

Equip. 22 FIRE

Stream 8

Firing Section

Boiler

Mud

Drum

V101

SP

MV

OP

1/ 0

mBOILER LEVEL

EQPT 01

P

I

D

SETTINGS

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