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Application of a Reaction Kinetic Model for On-Line Model Dynamic Control and Optimisation
Copyright t!;, IFAC Dynamics and Control of Process Systems. Corfu, Greece, 1998
APPLICATION OF A REACTION KINETIC MODEL FOR ON-LINE MODEL DYNAMIC CONTROL AND OPTIMISATION
D. M. Feord., P. J. Carlberg 2, J. L. Bixbyl, D. T. Campl, P. K. Moore 2
IDow Deutschland Inc., Rheinmuenster, Germany 2The Dow Chemical Company, Freeport, Texas, USA JThe Dow Chemical Company, Midland, Michigan, USA
Reactors form the heart of most chemical processes. Industrial research often leads to development of mechanistic kinetic simulations to describe the unit operation. With proper considerations these models can be used for optimisation and control of industrial reactors. This paper shows how the same kinetic model and physical property predictor has been adapted for on-line control and optimisation in both batch and continuous reactor systems Copyright © 1998 IFAC
1. INTRODUCTION
known. (Marlin (1996), Crowe (1994), Narasimhan et aL (1987), Cao (1995))
The reactor is the central unit operation in the chemical industry. Subsequent parts of the process exist to clean up or correct deficiencies originating there. Recognising this, industrial researchers often focus on reaction fundamentals, developing kinetic models to accurately describe the chemistry. A good model holds knowledge about the reaction in a statistically relevant form suitable for prediction . When the model monitors and guides the operational staff or guides a real-time operations optimization (RTO) or forms the basis of dynamic control, this knowledge can substantially improve reactor operation .
The process of making mechanistic model based control and optimisation work in an actual process involves careful theoretical development and adaptation of the theory to the details of the targeted process. In this particular case the off-line parameter fitting and on-line parameter estimation play a dominant role.
Unit Op'n Cnt!
The process control hierarchy up to process optimisation is described in Figure I. Any model based control depends first on precise and reliable instrumentation and control in the lower 3 layers. These must be in place before optimization is considered. Process data must be used in real time . The data are properly conditioned as shown in Figure 2, through gross error detection, steady state detection (when using a steady state model), data reconciliation, parameter estimation, and feedback correction. Techniques for each of these operations are well
Single Loop Control Plant Instruments
Figure I. Control Hierarchy
253
I Data !Conditioning
properties can readily be calculated. With the kinetic simulator plus physical property predictor. the user can relate feed composition and reactor operating conditions to final product properties.
Process Model
·Gross Error Detection -Steady State Dete C110n -Oata ReconCIliation ·Parameter Estimat ion I Feedback Correction
Targets & Constraints
3. PROCESS A pre-mix tank is used for mixing A and B. Within the raw material B are quantities of certain impurities which greatly affect the reaction time and physical properties of the finished product. The variability of the impurities in B can be large and can change on a daily basis. This tank is regularly analysed for its chemical content. The mix tank feeds one of a series of batch reactors or the first reactor in a series of continuous stirred tank reactors. The reactors follow a time temperature profile as the reaction occurs. Control strategy for the temperature profile differs slightly depending on the mode of operation . The temperature profile is critical, firstly , for the thermal stability of the reactor, as the reaction occurring is highly exothermic , and secondly, for the product properties and composition. The ultimate product properties and composition are determined in this reactor system. The relationship between reaction temperature, reaction residence time and initial feed concentrations is very important in determining whether the final product will meet the requisite specifications.
Plant Instruments
Figure 2. RTO Data Flow Examples will be given firstly of the application to the improved control and flexibility of a batch reactor in the process and secondly of the dynamic control and optim isation of a series of continuous stirred tank reactors carrying out the same chemistry in a similar plant.
2. KINETIC SIMULATOR A kinetic model of the reactions was developed from laboratory data. In its simplified form the reactions occurring are described below. They are irreversible and either A or B, whichever is in excess, may be reacted to extinction . A
+ B~ C
( I)
C-'- .4~D + E
(2)
D + B~F
(3)
F + B~G
(4)
ATC~H
(5)
F + C ~H
(6)
A is in large excess in this reaction, while B reacts to near completion. However, even at its relatively low concentration, B has a large effect on the reactivity during downstream processing. Maintaining tight control of the concentration of B in the reactor product IS crucial in avoiding upsets during subsequent processing. There are also limits on F, G and H to ensure that the final product leaving the process is within specification.
C and D are the desired products for this reaction. Downstream of this reactor C is fully converted to D and E is converted back to A. H is an unwanted byproduct whose formation must be suppressed by using temperature control. G and F have a large affect on the product physical properties and their formation must be managed through feed composition and temperature control.
4. BATCH PROCESS OPTIMISATION AND CONTROL Two objectives have been set as targets for model control , firstly an increased and variable reactor capacity in line with the rest of the process with no deviation in the product properties and secondly improved reactor product consistency. In order to achieve this the kinetic simulator in the form of a reactor model has been placed on-line, closed loop in the digital control system (DCS) . Off-line studies were made using the model to optimise and reprogramme the temperature profile in the DCS. Due to the structure of the temperature profile manual set point changes to the temperature profile in the DCS are possible but no automatic on-line optimisation changes are possible. Therefore the off-line studies
A homogeneous liquid catalyst is used to catalyse the reaction. Its concentration effect is accounted for in the rate equations. The rate equations, described using the Arrhenius equation . are temperature dependant. The final product consists of a mixture of components. Its measurable macroscopic physical properties depend on the structure and concentration of these components. The kinetic simulator predicts the amounts of these components present based on feed composition and reactor conditions. From the predicted product composition , the product physical
254
are an important guide to the operational staff as to the optimum temperature profile.
This is an effective means of composition and physical properties control for the final product.
Both current and historical data are automatically retrieved by the model and model data sent to the DeS. A date and time stamp sent every time the model runs is used to by the DeS to detennine that the model is still active . The table below indicates the data transfer.
Having defined an operation region for the temperature profile off-line, in which physical properties and impurity levels can be held within their specification, the implementation of the model control has delivered reduced batch times coupled with better reactor product consistency. This is shown clearly in Figure 3 where the end concentration of B is plotted against the reactor residence time. A concentration below but as close to 0.7 is desired . Before the model implementation, with a fixed residence time the variability in this quantity is large. With the implementation of the model, not only is this scatter greatly reduced, but batch cycle times have been significantly reduced through temperature profile optimization using the model off-line.
INPUT FROM DeS
OUTPUT TO Des
i\ & 13 raw material !lows
TimeHHMM Date MMDD Alarm to stop batch Reactor heel volume Reactor Concentrations Product properties
13 impurity concentrations Batch catalyst charge Reactor temperatures Reactor hatch step Max product B conc. Minimum product property
08
Table I. Data transfer used for reactor model
• 07
In terms of process monitoring the model has three important functions to fulfil in order to be able to detect the concentrations required at the end of the batch. These are described below,
•
Recognises reaction initiation when the catalyst added with or to the raw material.
•
Estimates actual reactor concentrations by looping through on a regular defined basis, collecting and conditioning temperature data, integrating concentration profiles and storing this as the initial conditions for the next integration .
• •
I
42
mp lcrrcnlallOn
I . A ft crn1)d cl l"l'icncntallOn
•• •
.
03 40
• Be rore nud cl -
I
04
48
50
Rnilkncc'Tilllt'
Figure 3 model influence on the reaction residence time and conc. of B Figure 4 shows that the product property has stayed well within its limits, indicating that reactor temperature optlmlsation and earlier reaction completion did not adversely affect this product physical property. It is rather, indicated that the variation of this product is reduced, due to the better finishing control on the reaction
Before the model was used as an on-line closed-loop tool detailed analyses and comparison of its predictions against measured values were made . This was best accomplished with the model in on-line open-loop mode in order to satisfy the functions previously mentioned .
Art ....,. modd implc=mcnc.lIcion
~ lo>
',I"
"" IIIon 73
1
~ ;,: ~ :~~~
Once the model parameters were estimated and verified the model was used in an on-line closed loop mode. The aspects of the model control are detailed below. Within the DeS the model :
•
05
<
G
Monitors feed stock concentrations using component mass balances from raw material flows and concentrations for B and its important impurities. Concentration profiles for each feed tank are saved .
•
•
06
<
•
• • • •• • • ••• • • • • 0 = 0 ,05 0 = 01 7 • I
=
S.ampk
:£.
i ~
!
·," W-------------;. :-A27-" -1'/
'U I.,
II SO. KIl l
7:;0 _________ _ ________ _
",
;;
Sample
Detects when B has been reduced to below the maximum final concentration. Sends a warning to the operational staff via the DCS and the DCS automatically starts the batch cooling step.
Figure 4 Variation Product Property
Calculates how much of the current batch is to be left in the reactor as a "seed" for the next batch.
Figure 5 below also indicates that the by-product H has not significantly changed due to the model
255
finishing the temperatures.
batch
hl"folt motltl i Olpltmrnl"l:tflOn ~
early
with
higher
process This strategy of holding reactor levels and temperatures at their steady state values works quite well in handling small feed disturbances and gradual rate changes. In these situations. making a smooth adjustment in the temperature setpoints of the various reactors is sufficient to avoid a significant variation in the reactor product. However, large disturbances can result in unsatisfactory product properties during the transition to the new steady state conditions. In particular, downstream processing constraints might force an abrupt reduction of as much as 40% in the production rate . The reactor heat exchangers are not sized to adjust the temperatures quickl y enough to avoid excessIve overreaction at the increased residence time.
,'ftt," modtl impltmrntanon
" 1'(1"
r: -' 0 UI
Lt- 0:: ::::
Samplt
Samplt
Figure 5 Variation of H
5.
CONTINUOUS PROCESS OPTIMISA nON
As a first step in addressing this problem, an on-line version of the dynamic model was developed and installed. It presently operates in a monitoring mode and provides dynamic estimates of the concentrations of species A through H. Intermediate reactors are not routinely sampled. However, the limited measurements that are available agree quite well with the model estimates and support the use of the model for improving the response to rate changes and other of the species process disturbances . Many concentrations were relatively insensitive to the observed disturbances . Species B, however, did show significant variability . In particular, a review of data generated before and during a period of instability in the downstream processes showed it had deviated from its normal value during this time.
AND CONTROL
As with the batch reactor, the objectives for a continuous reactor train are to produce more product and to improve reactor product consistency. Here reactor operation is controlled by manipulating reactor residence times and temperatures. For a given rate. this translates to the maintenance of liquid levels and temperatures in each of the reactors . Figure 6 shows the continuous tank reactor system.
• conversion profile controlled by t and T • manually set feed rate, levels, product type • model manipulates temperatures
The second step in improving the transient response involves the evaluation of candidate control strategies using off-line simulation of the combined process plus controllers. Given the observed sensitivity of the process to the species B concentration, its domination of the objective function , and the desire to have the simplest feasible controller, the initial effort is focusing on controlling species B. There is reason to expect that if this is done, the other species will be "pulled" along in a suitable manner. The level in the final reactor of the series is traditionally maintained near 50% to provide " surge" capacity. The strategy currently being tested in simulation involves varying the flow into the final reactor to control the B concentration while the temperature moves toward its new steady state value. This contrasts with the present controller which maintains the level at setpoint during the transition.
Serial CSTR Reactor System
Figure 6. CSTR Reactor System
For steady state operation, plant engineers set the feed rate and reactor levels. The model manipulates the reactor temperatures to achieve the targeted product composition and final properties. In this mode, the optimisation is implemented on-line with closed-loop control. This type of optimisation structure and implementation into the DCS has been described (1997». previously (Carlberg and Feord Temperatures are manipulated to minimize an objective function which scales and balances desired composition and physical property targets. These targets are identical to those described for the batch reactor. The resulting set points are transferred to the DCS for implementation in the Unit Operation or Loop Control layers of the control pyramid.
A parallel strategy applies to the control of the upstream reactors. Note that while the desired B concentration in the final reactor does not change, the set points for this species in intermediate reactors are affected by feed composition and rate changes. However, as demand for increased throughput 256
(productivity) requires level set points nearer their feasible maximums. there is less freedom to allow these intermediate levels to float. Such constraints mean that there is only limited control of these intermediate B concentrations during transitions. While direct control of B is lost when a level constraint is met. the model is able to track its concentration . In particular. an estimate of the concentration in the penultimate reactor is available in calculating the flow required into the last reactor.
In the anticipated event that these simulations uncover a control strategy which promises an attractive improvement over the present one in plant use. the final step in the process will be to install. test. and provide documentation for plant personnel to use. to maintain. and. as conditions evolve, to modify the controller. For a new controller ultimately to be successful , its concept must be understood and appreciated by plant management, engineering staff, and operators. In this context the quotation attributed to Einstein seems appropriate, "Keep it as simple as possible. but not simpler."
6. CONCLUSIONS This paper illustrates how the dynamic on-line control and optimisation of a reactor can be accomplished. Its basis is a simulation model which reflects the fundamental kinetics and predicts key product properties. Improved product quality and increased capacity are two of the benefits of this work. The simulation has been adapted for both batch and continuous reactors .
7. BIBLIOGRAPHY Carlberg, P. J. and Feord. D. M., 1997. Model Based Optimisation and Control of a Reactor System with Heterogeneous Catalyst, Proceedings of PSE 97 ESCAPE- 7, pp 385-390, Trondheim Cao. S.. and Rhinehart, R.R ., 1995, An Efficient Method for On-Line Identification of Steady-State, .Journal of Process Control. Crowe. C.M., 1994. Data Reconciliation - Progress and Challenges, Proceedings of PSE 94, pp I 11-121 , Seoul. Korea. Marlin. T. E.. and Hrymak, A. N., 1996, Real-Time Operations Optimization of Continuous Processes, Chemical Process Control- V, Tahoe City, Ca. Narasimhan. S., C.S. Kao and R.S .H.Mah, (1987), Detecting Changes of Steady State Using the Mathematical Theory of Evidence, AIChE J" 33, 1930.
257
APPLICATION OF A REACTION KINETIC MODEL FOR ON-LINE MODEL DYNAMIC CONTROL AND OPTIMISATION
D. M. Feord., P. J. Carlberg 2, J. L. Bixbyl, D. T. Campl, P. K. Moore 2
IDow Deutschland Inc., Rheinmuenster, Germany 2The Dow Chemical Company, Freeport, Texas, USA JThe Dow Chemical Company, Midland, Michigan, USA
Reactors form the heart of most chemical processes. Industrial research often leads to development of mechanistic kinetic simulations to describe the unit operation. With proper considerations these models can be used for optimisation and control of industrial reactors. This paper shows how the same kinetic model and physical property predictor has been adapted for on-line control and optimisation in both batch and continuous reactor systems Copyright © 1998 IFAC
1. INTRODUCTION
known. (Marlin (1996), Crowe (1994), Narasimhan et aL (1987), Cao (1995))
The reactor is the central unit operation in the chemical industry. Subsequent parts of the process exist to clean up or correct deficiencies originating there. Recognising this, industrial researchers often focus on reaction fundamentals, developing kinetic models to accurately describe the chemistry. A good model holds knowledge about the reaction in a statistically relevant form suitable for prediction . When the model monitors and guides the operational staff or guides a real-time operations optimization (RTO) or forms the basis of dynamic control, this knowledge can substantially improve reactor operation .
The process of making mechanistic model based control and optimisation work in an actual process involves careful theoretical development and adaptation of the theory to the details of the targeted process. In this particular case the off-line parameter fitting and on-line parameter estimation play a dominant role.
Unit Op'n Cnt!
The process control hierarchy up to process optimisation is described in Figure I. Any model based control depends first on precise and reliable instrumentation and control in the lower 3 layers. These must be in place before optimization is considered. Process data must be used in real time . The data are properly conditioned as shown in Figure 2, through gross error detection, steady state detection (when using a steady state model), data reconciliation, parameter estimation, and feedback correction. Techniques for each of these operations are well
Single Loop Control Plant Instruments
Figure I. Control Hierarchy
253
I Data !Conditioning
properties can readily be calculated. With the kinetic simulator plus physical property predictor. the user can relate feed composition and reactor operating conditions to final product properties.
Process Model
·Gross Error Detection -Steady State Dete C110n -Oata ReconCIliation ·Parameter Estimat ion I Feedback Correction
Targets & Constraints
3. PROCESS A pre-mix tank is used for mixing A and B. Within the raw material B are quantities of certain impurities which greatly affect the reaction time and physical properties of the finished product. The variability of the impurities in B can be large and can change on a daily basis. This tank is regularly analysed for its chemical content. The mix tank feeds one of a series of batch reactors or the first reactor in a series of continuous stirred tank reactors. The reactors follow a time temperature profile as the reaction occurs. Control strategy for the temperature profile differs slightly depending on the mode of operation . The temperature profile is critical, firstly , for the thermal stability of the reactor, as the reaction occurring is highly exothermic , and secondly, for the product properties and composition. The ultimate product properties and composition are determined in this reactor system. The relationship between reaction temperature, reaction residence time and initial feed concentrations is very important in determining whether the final product will meet the requisite specifications.
Plant Instruments
Figure 2. RTO Data Flow Examples will be given firstly of the application to the improved control and flexibility of a batch reactor in the process and secondly of the dynamic control and optim isation of a series of continuous stirred tank reactors carrying out the same chemistry in a similar plant.
2. KINETIC SIMULATOR A kinetic model of the reactions was developed from laboratory data. In its simplified form the reactions occurring are described below. They are irreversible and either A or B, whichever is in excess, may be reacted to extinction . A
+ B~ C
( I)
C-'- .4~D + E
(2)
D + B~F
(3)
F + B~G
(4)
ATC~H
(5)
F + C ~H
(6)
A is in large excess in this reaction, while B reacts to near completion. However, even at its relatively low concentration, B has a large effect on the reactivity during downstream processing. Maintaining tight control of the concentration of B in the reactor product IS crucial in avoiding upsets during subsequent processing. There are also limits on F, G and H to ensure that the final product leaving the process is within specification.
C and D are the desired products for this reaction. Downstream of this reactor C is fully converted to D and E is converted back to A. H is an unwanted byproduct whose formation must be suppressed by using temperature control. G and F have a large affect on the product physical properties and their formation must be managed through feed composition and temperature control.
4. BATCH PROCESS OPTIMISATION AND CONTROL Two objectives have been set as targets for model control , firstly an increased and variable reactor capacity in line with the rest of the process with no deviation in the product properties and secondly improved reactor product consistency. In order to achieve this the kinetic simulator in the form of a reactor model has been placed on-line, closed loop in the digital control system (DCS) . Off-line studies were made using the model to optimise and reprogramme the temperature profile in the DCS. Due to the structure of the temperature profile manual set point changes to the temperature profile in the DCS are possible but no automatic on-line optimisation changes are possible. Therefore the off-line studies
A homogeneous liquid catalyst is used to catalyse the reaction. Its concentration effect is accounted for in the rate equations. The rate equations, described using the Arrhenius equation . are temperature dependant. The final product consists of a mixture of components. Its measurable macroscopic physical properties depend on the structure and concentration of these components. The kinetic simulator predicts the amounts of these components present based on feed composition and reactor conditions. From the predicted product composition , the product physical
254
are an important guide to the operational staff as to the optimum temperature profile.
This is an effective means of composition and physical properties control for the final product.
Both current and historical data are automatically retrieved by the model and model data sent to the DeS. A date and time stamp sent every time the model runs is used to by the DeS to detennine that the model is still active . The table below indicates the data transfer.
Having defined an operation region for the temperature profile off-line, in which physical properties and impurity levels can be held within their specification, the implementation of the model control has delivered reduced batch times coupled with better reactor product consistency. This is shown clearly in Figure 3 where the end concentration of B is plotted against the reactor residence time. A concentration below but as close to 0.7 is desired . Before the model implementation, with a fixed residence time the variability in this quantity is large. With the implementation of the model, not only is this scatter greatly reduced, but batch cycle times have been significantly reduced through temperature profile optimization using the model off-line.
INPUT FROM DeS
OUTPUT TO Des
i\ & 13 raw material !lows
TimeHHMM Date MMDD Alarm to stop batch Reactor heel volume Reactor Concentrations Product properties
13 impurity concentrations Batch catalyst charge Reactor temperatures Reactor hatch step Max product B conc. Minimum product property
08
Table I. Data transfer used for reactor model
• 07
In terms of process monitoring the model has three important functions to fulfil in order to be able to detect the concentrations required at the end of the batch. These are described below,
•
Recognises reaction initiation when the catalyst added with or to the raw material.
•
Estimates actual reactor concentrations by looping through on a regular defined basis, collecting and conditioning temperature data, integrating concentration profiles and storing this as the initial conditions for the next integration .
• •
I
42
mp lcrrcnlallOn
I . A ft crn1)d cl l"l'icncntallOn
•• •
.
03 40
• Be rore nud cl -
I
04
48
50
Rnilkncc'Tilllt'
Figure 3 model influence on the reaction residence time and conc. of B Figure 4 shows that the product property has stayed well within its limits, indicating that reactor temperature optlmlsation and earlier reaction completion did not adversely affect this product physical property. It is rather, indicated that the variation of this product is reduced, due to the better finishing control on the reaction
Before the model was used as an on-line closed-loop tool detailed analyses and comparison of its predictions against measured values were made . This was best accomplished with the model in on-line open-loop mode in order to satisfy the functions previously mentioned .
Art ....,. modd implc=mcnc.lIcion
~ lo>
',I"
"" IIIon 73
1
~ ;,: ~ :~~~
Once the model parameters were estimated and verified the model was used in an on-line closed loop mode. The aspects of the model control are detailed below. Within the DeS the model :
•
05
<
G
Monitors feed stock concentrations using component mass balances from raw material flows and concentrations for B and its important impurities. Concentration profiles for each feed tank are saved .
•
•
06
<
•
• • • •• • • ••• • • • • 0 = 0 ,05 0 = 01 7 • I
=
S.ampk
:£.
i ~
!
·," W-------------;. :-A27-" -1'/
'U I.,
II SO. KIl l
7:;0 _________ _ ________ _
",
;;
Sample
Detects when B has been reduced to below the maximum final concentration. Sends a warning to the operational staff via the DCS and the DCS automatically starts the batch cooling step.
Figure 4 Variation Product Property
Calculates how much of the current batch is to be left in the reactor as a "seed" for the next batch.
Figure 5 below also indicates that the by-product H has not significantly changed due to the model
255
finishing the temperatures.
batch
hl"folt motltl i Olpltmrnl"l:tflOn ~
early
with
higher
process This strategy of holding reactor levels and temperatures at their steady state values works quite well in handling small feed disturbances and gradual rate changes. In these situations. making a smooth adjustment in the temperature setpoints of the various reactors is sufficient to avoid a significant variation in the reactor product. However, large disturbances can result in unsatisfactory product properties during the transition to the new steady state conditions. In particular, downstream processing constraints might force an abrupt reduction of as much as 40% in the production rate . The reactor heat exchangers are not sized to adjust the temperatures quickl y enough to avoid excessIve overreaction at the increased residence time.
,'ftt," modtl impltmrntanon
" 1'(1"
r: -' 0 UI
Lt- 0:: ::::
Samplt
Samplt
Figure 5 Variation of H
5.
CONTINUOUS PROCESS OPTIMISA nON
As a first step in addressing this problem, an on-line version of the dynamic model was developed and installed. It presently operates in a monitoring mode and provides dynamic estimates of the concentrations of species A through H. Intermediate reactors are not routinely sampled. However, the limited measurements that are available agree quite well with the model estimates and support the use of the model for improving the response to rate changes and other of the species process disturbances . Many concentrations were relatively insensitive to the observed disturbances . Species B, however, did show significant variability . In particular, a review of data generated before and during a period of instability in the downstream processes showed it had deviated from its normal value during this time.
AND CONTROL
As with the batch reactor, the objectives for a continuous reactor train are to produce more product and to improve reactor product consistency. Here reactor operation is controlled by manipulating reactor residence times and temperatures. For a given rate. this translates to the maintenance of liquid levels and temperatures in each of the reactors . Figure 6 shows the continuous tank reactor system.
• conversion profile controlled by t and T • manually set feed rate, levels, product type • model manipulates temperatures
The second step in improving the transient response involves the evaluation of candidate control strategies using off-line simulation of the combined process plus controllers. Given the observed sensitivity of the process to the species B concentration, its domination of the objective function , and the desire to have the simplest feasible controller, the initial effort is focusing on controlling species B. There is reason to expect that if this is done, the other species will be "pulled" along in a suitable manner. The level in the final reactor of the series is traditionally maintained near 50% to provide " surge" capacity. The strategy currently being tested in simulation involves varying the flow into the final reactor to control the B concentration while the temperature moves toward its new steady state value. This contrasts with the present controller which maintains the level at setpoint during the transition.
Serial CSTR Reactor System
Figure 6. CSTR Reactor System
For steady state operation, plant engineers set the feed rate and reactor levels. The model manipulates the reactor temperatures to achieve the targeted product composition and final properties. In this mode, the optimisation is implemented on-line with closed-loop control. This type of optimisation structure and implementation into the DCS has been described (1997». previously (Carlberg and Feord Temperatures are manipulated to minimize an objective function which scales and balances desired composition and physical property targets. These targets are identical to those described for the batch reactor. The resulting set points are transferred to the DCS for implementation in the Unit Operation or Loop Control layers of the control pyramid.
A parallel strategy applies to the control of the upstream reactors. Note that while the desired B concentration in the final reactor does not change, the set points for this species in intermediate reactors are affected by feed composition and rate changes. However, as demand for increased throughput 256
(productivity) requires level set points nearer their feasible maximums. there is less freedom to allow these intermediate levels to float. Such constraints mean that there is only limited control of these intermediate B concentrations during transitions. While direct control of B is lost when a level constraint is met. the model is able to track its concentration . In particular. an estimate of the concentration in the penultimate reactor is available in calculating the flow required into the last reactor.
In the anticipated event that these simulations uncover a control strategy which promises an attractive improvement over the present one in plant use. the final step in the process will be to install. test. and provide documentation for plant personnel to use. to maintain. and. as conditions evolve, to modify the controller. For a new controller ultimately to be successful , its concept must be understood and appreciated by plant management, engineering staff, and operators. In this context the quotation attributed to Einstein seems appropriate, "Keep it as simple as possible. but not simpler."
6. CONCLUSIONS This paper illustrates how the dynamic on-line control and optimisation of a reactor can be accomplished. Its basis is a simulation model which reflects the fundamental kinetics and predicts key product properties. Improved product quality and increased capacity are two of the benefits of this work. The simulation has been adapted for both batch and continuous reactors .
7. BIBLIOGRAPHY Carlberg, P. J. and Feord. D. M., 1997. Model Based Optimisation and Control of a Reactor System with Heterogeneous Catalyst, Proceedings of PSE 97 ESCAPE- 7, pp 385-390, Trondheim Cao. S.. and Rhinehart, R.R ., 1995, An Efficient Method for On-Line Identification of Steady-State, .Journal of Process Control. Crowe. C.M., 1994. Data Reconciliation - Progress and Challenges, Proceedings of PSE 94, pp I 11-121 , Seoul. Korea. Marlin. T. E.. and Hrymak, A. N., 1996, Real-Time Operations Optimization of Continuous Processes, Chemical Process Control- V, Tahoe City, Ca. Narasimhan. S., C.S. Kao and R.S .H.Mah, (1987), Detecting Changes of Steady State Using the Mathematical Theory of Evidence, AIChE J" 33, 1930.
257