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An Advanced Operations Support System for a Batch Pilot Plant
Copyright © IFAC Dynamics and Control of Process Systems, Corfu, Greece, 1998
AN ADVANCED OPERATIONS SUPPORT SYSTEM FOR A BATCH PILOT PLANT Z. H. Liu and S. Macchietto Centre for Process Systems Engineering. Imperial College. London SW7 2BY. UK.
Abstract: This paper describes the design, implementation and operation of an integrated system to support: I} the on-line operation of batch plants; 2} the off-line development and testing of new operation and control procedures. The system integrates with an industrial control system a generic simulator, a generic supervisory control system, a planning and scheduling system, and model based control functions. The functionality and use of this system are demonstrated on a complex batch pilot plant. The system greatly facilitates the rapid development, testing and commissioning of new production control schemes, new product recipes and new software/application products. Copyright © 1998 IFA C Key Words: batch plant, operations support, on-line scheduling, control, simulation
I. INTRODUCTION
The goal of this paper is to describe the design of an operations support system which provides both support functions , and to demonstrate its practical implementation on a mUltipurpose batch pilot plant.
Batch plants are typically more flexible and able to cope with an uncertain production environment (Rippin, 1991; Sawyer, 1993). However, such flexibility also results in an increased complexity of operation. both from planning, scheduling and control perspectives. A crucial aspect is presented by the strong interaction between continuous and discrete aspects. Thus batch plants are intrinsically "hybrid" . Taking into account these aspects, two key requirements for support systems are:
2.
OPERATIONS SUPPORT SYSTEM Design and Implementation
The diversity of the activities to be enabled/ supported (in terms of time scale, as well as nature of problems involved) and the complexity of batch operations mandates the use of a modular and hierarchical design of the operations support system. A hierarchical structure is also favoured due to safety, maintenance , testing and validation, and flexibility considerations.
• a production environment capable of supporting various production control functions (from process planning and scheduling, to supervisory production control and advanced control of individual unit operations). aimed at achieving high operations efficiency, product quality, operation responsiveness to changes in orders (desired) and process variations (undesired). • a development environment supporting the rapid of new development and implementation manufacturing procedures (designs and retrofit options. new product recipes, process control and automation options. operator training, etc.), aimed at achieving a high efficiency of these engineering activities.
A key requirement for an operations support system of general use is the ability to represent process operations problems in such a way that information can be structured, translated, analysed and validated at various levels of abstractions and aggregation . It should allow specific decisions to be made in a systematic way, through the use of generic support tools and techniques. Therefore, a model based approach is considered here .
595
An advanced operations support system was developed that performs both on-line control execution (utilising actual control hardware/software and running a plant) and various operations support functions . These include both a set of production planning. monitoring and advanced control functions . and a set of simulation functions. A schematic representation of the system is shown in Fig. I.
3.
MODELLING
One of the key issue is the modelling of the manufacturing system. The use of mathematical models enables the information to be represented in a formal way , and to be translated into knowledge in the form in which decisions can be made in an algorithmic manner. Due to the complexity and diversity of the problems. a variety of model representations must be considered . However. a number of modelling issues must be addressed in order to ensure that decisions can be transferred between different applications and appropriately utilised .
The box on the right includes a mUltipurpose pilot plant hardware and its industrial control system (Macchietto. 1993). The box on the left utilises the gPROMS simulator (Pantelides. 1996). which is capable of modelling and simulating generic batch plants and operations. The supervisory batch control functions are carried out using the SUPERBA TCH 2.0 software (User Manual. 1996). while optimal production planning and scheduling are performed using gBSS (Shah. 1992). The model based control function utilises nonlinear techniques described by Liu and Macchietto (1995a) for the advanced control of batch reactor operation.
3.1 Model aggregation The need for appropriate aggregation of process models (e.g. equipment. tasks . demands. etc .) has been addressed by many authors (e .g. Macchietto. 1993; Bassett. et al .• 1994). The level of aggregation required within each application largely depends on the types of decisions to be made. the local objectives and the ability of the algorithms used to handle complexity and problem size . A proper aggregation of process models is important both for reducing the complexity of problems. as well as for ensuring feasible and accurate production schedules, control. etc. For planning/scheduling. recipes and procedures are represented in the form of State-TaskNetworks. At the planning level material transfer tasks and sequential processing tasks performed in the same unit are aggregated into a single task to ensure that decisions made at different levels of aggregation are consistent and the resulting models are simple.
The production planning and scheduling. supervisory control and model based control functions can drive both the on-line and the simulated portions of the system. The simulations can be initiated from actual plant meters. SUPERBATCH or within the simulation package. The individual applications may be executed In a heterogeneous computing environment over a network. The link between all applications and functions is provided by ControlLink. a software package based on clientsserver architecture. The package ensures information (data. control commands. messages. alarms. etc.) can be transferred asynchronously among various application programs and/or control system(s).
PLANNmNG~ON1TORING
FUNCTIONS Business Planning (enterprise)
1
Planning & Scheduling (site I plant)
SIMULATION FUNCTIONS
Plant
EXECUTION FUNCTIONS I
status of and producti~
proJuction schedule
'f Supervisory Batch Control & Management !
I I
!
••
y Simulated Co;;trc;il Primitives
I I
status of Kq£,ces inventorV. ~ev'ls
finishing ~ ,.,..- C
!<--"', control
i
L_ _ __ _ ---'
iDttiate sequences
'r
set parameters time
. ........., ommumcations ./'<-
act':'
feasu~ents
Model Based Control
Information I Management System I A
y
Sequence Control
i
A ._......._. . ___._ ·!.L... _. _ Digital & Analog Control
A
y
Batch Plant
L......_ __ __
Fig. I Schematic architecture of the advanced operations support system
596
3.2 Models hierarchy and coordination
sequence is updated at high frequency by the sequence control system and polled (at a frequency of once a minute) by the supervisory system. This in turn updates its own finite state model explicitly defining the state of equipment items , batches. control phases , ete. as modelled at this level. Such states may be fed back to the planning/schedule optimization level , at the frequency of, typically , once every few hours , and may be used for reinitialising/updating optimization models at that level, as described by Chua (1995) .
To ensure feasible execution, the more aggregated tasks used for process planning and scheduling are decomposed into more detailed control entities (phases and procedures) suitable for the supervisory control level. For example, the aggregated reaction task in a recipe for making a dextrin intermediate (Fig. 2) is decomposed into its individual phases (feeding, pH control, gelatinization and degradation). Furthermore, these control phases must be mapped to even more detailed equipment control sequences. Proper coordination between these entities must be established to enable them to be correctly linked. Disaggregated phases are mapped to "top sequences" at the highest level of the control sequence hierarchy (e.g. sparge control). A schematic representation of the model hierarchy used is illustrated in Figure 2.
4.
Within this design, each application can be operated/tested individually or in combination with others. For example, an off-line simulation of a process can be performed individually. In this case, the order of the tasks to be executed may be predetermined and fixed a priori (in an open-loop manner) and programmed as a specific "schedule" in the gPROMS language. This mode of operation is useful for evaluating new control sequences (Liu and Macchietto , 1992), testing a model based control algorithm (Liu and Macchietto , 1995a, 1995b), debugging a dynamic model, etc. Then, the simulator may be connected to and driven by the supervisory controller. The control sequences (simulated) will then be executed according to the supervisory control algorithm, which will modify the schedule in a closed-loop manner. This provides a useful test bed for production planning and supervisory level control strategies. In this mode, it is possible to evaluate the effect of any variations on operations (built into the simulation model) and to test the effectiveness of the supervisory decisions .
water
0--- . '"""' °cQ.~ :,.J-'
int-dcx
slurry
0.94- make_slurry
i- ~--- i reaction ~
starch
1·~· ""Ph~~~~tr.o.lj '.~~:I~:i~.;~~~~~J ·ldegra.da~~~J I ··-.. ·..-:.:.:::::::..=
T,- ----···-·-·-
J.::::::::::.::
I: sparge control i ;
!
:level monitor i :.:_~:':~~::~ j
OPERATIONS
i interlock !
Fig. 2 Model hierarchy and coordination The above modelling guides were used to develop batch pilot plant models at various level of detail. These include a state-task-network model for production planning, a supervisory control model for on-line scheduling and batch management, and a detailed dynamic model of the plant in gPROMS. These models are mapped and properly coordinated to ensure a proper communication between each level of controls. Detailed descriptions can be found in Liu (1995), and Liu and Macchietto (1994).
The following section describes in detail just the use of the operations support system for on-line scheduling and supervisory batch management. The use of the system for other applications, such as developing and validating new control procedures, model based control algorithms, etc. is described in Liu (1995).
4.1 On-line scheduling and supervisory control The SUPERBATCH 2.0 system is used to perform supervisory control functions. connected to the actual plant control system (ACCOS). Supervisory control lies at the transition between on-line production control and off-line planning/ scheduling activities. Its main functions are :
3.3 General remarks As observed, the key to proper coordination between all production control levels is a careful analysis and definition of model aggregations to be used at each level. In order to support communication between levels, in the upward directions, the state of equipment and control function models at each level is explicitly defined and updated . Such model contains states significant for control/measurement purposes. For example, the state of each top level
i)
597
to coordinate off-line planning and scheduling activities with those of the production control system;
lines) between batches and cleaning at end of production (The interfaces from Batch-Manager. a software product of APV built around SUPERBATCH). The Y-axis represents main plant equipment and the X-axis is the time. The vertical line shows the current time and moves to the right as the schedule is updated once a minute. The part of the chart on the left of the vertical line represents the production history of the plant and that on the right part is the planned schedule. Storage units are displayed as the predicted inventory level profiles (0100% scale) and final status (material types and inventory levels) . All materials being processed (raw, intermediate and final products) are coloured coded and each object on the screen is linked to its entry on the production model database.
ii)
to initiate , supervise and coordinate the execution of the scheduled activities carried out by lower level controllers (e.g. sequence control); iii) to accommodate any variation at either higher or lower levels by rescheduling production, while enforcing feasible and safe operation.
Towards that end, the supervisory control level requires knowledge of and data from both the lower level controls and the higher level production planning and scheduling models/activities. At the supervisory leveL operational feasibility and computational efficiency are very important since this is an on-line activity. The major economic decisions (batch SIzes , sequence of batches and maIn equipment allocations) from the external higher level planner/scheduler are imported to the system and verified for feasibility. A detailed production schedule is generated first in an off-line version of the software . Upon satisfactory confirmation that the more detailed schedule is acceptable (typically by a production manager, supervisor or operator, depending on management delegation in a plant), it is then passed on to an on-line portion of the system , an on-line monitor, which runs continuously. New requirements are processed and executed through the control systems. The supervisory system structure is schematically shown in Figure 3.
A rmduct lon
de mands
~t atc
v
off-line ", _ _scheduler
j
"'production ,.
v
models
,.--- - - -,If-
commands
queries
->
I '.
on-line
- --monitor -- _./
A! Iml1a(u . .. phascs
pI ant'phase I status ~
set parameters
Control System i
Fig. 3 SUPERBATCH system structure
Figure 4 shows in the form of Gantt chart an initial schedule of a starch degradation process carried out In the pilot plant, with intermediate cleaning (hatch Catch-Tracker
Tt... : 16:06 _
~...
~.-.,--.....
""- _ _ _ _ _ _ _ _ IIIio¥o
~ton Iioat;;;;-s.;i.;-'"~~ ~ Ai"';-DiS'Pl~
_
ih
__ -_- :--'4
Fig . 4 Supervised operation of batch pilot plant for starch conversion process with CIP at start time 16:06
598
The execution of the production schedule is initiated and supervised by the on-line monitor in real-time at 16:06, and control commands (e.g. start a sequence) and control parameters (e.g. batch size. setpoints. etc .) are passed to the control system. The first operation, which is transferring process water into tank T I , is started and others are waiting. During the operation, the status of the control sequences (e.g. start, complete, alarm. hold, etc.) and of the plant (inventory level , final batch size achieved) are fed back from the control system to the on-line monitor component of the supervisory control. where updating and rescheduling is performed. Detailed information related to the current production schedule and the status of the execution can be obtained through an operator terminal. Execution and reVISIOn of production schedule is essentially performed in a closed-loop manner.
allows the operator to make an early decision before the problem actually occurs. For mmor production variations such as 111 processing times. the on-line scheduler adjusts the production schedule automatically and updates the schedule every minute . For major yariation s/ disturbances such as equipment breakdown . ne\\' product orders. etc. an off-line production schedule is typically generated first , taking into account the variations and the current status of the production schedule. Here. alternative production scenarios (involving inclusion/deletion of new hatches . alternative assignments or batch sequences , etc.) may be easily generated. displayed with alerts similar to Fig. 4 and 5, and assessed. Once an off-line schedule is approved by the operator/supervisor, then this new schedule can be passed to the on-line monitor for execution. During the above off-line scheduling process (which typically takes a few minutes), the execution of the original production schedule is continuing.
Figure 5 shows the new production schedule at time 16:28. By then, two operations (making slurry in Tl and transferring slurry to the reactor T3) have finished successfully (on the left of the vertical line) . Two other operations are being executed ( pH control in the reactor T3 and cleaning of TI through He3 and A V 1-40). Due to an unforeseen delay of the pH control operation (gaps on the left of vertical line) , the scheduler detected that the batch with ID 00 I (producing Malto-dextrins) is expected to finish late, even after rescheduling in view of the delay. The monitor thus sends an alert signal to the operator, displaying the alert message and highlighting the late finishing batch (Fig. 5). This
In above example. production control is carried out in a closed-loop manner. Apart from ensuring operational feasibility at all times and effective use of all production resources, the supervisory controller leads to a considerable reduction in operator load . Another major advantage is that the integrity of the operation and the repeatability of the operation are assured by executing all processing procedures in the correct order, precisely and correctly at all time .
Fig.5 Supervised operation at time 16:28
599
Proceeding of D YCORD '95, June 7-9, Helsingor. Denmark. Liu, Z. H. and S. Macchietto (1995b). Model based control of a mUltipurpose batch reactor - An experimental study. Computers Chem. Engng Vol. 19, Suppl. , pp. S477-S482. Liu, Z. H. (1995) . An advanced process manufacturing system - design and application to a food processing pilot plant. PhD Thesis, Imperial College of Science, Technology and Medicine, University of London . Macchietto, S. (1993). Bridging the gap - integration of design. operation scheduling and control , proceeding of FOCAPO 11, Colorado, July 18-23. Pantelides, C. C. (1996). An advanced tool for process modelling, simulation and optimisation. Proceeding of Computers Europe Ill. Frankfurt Rippin, D.W.T. (1991), Batch process planning, Chemical Engng., Vol. 98, No .5, ppl00-107, May. Sawyer, P. (1993). Computer-controlled batch processing. IChemE, Rugby , UK. Shah, N. (1992). Efficient scheduling, planning and design of mUltipurpose batch plants. PhD Thesis. Imperial College of Science, Technology and Medicine, University of London. SUPERBATCH 2.0 (1997) . User Manual. Process Systems Enterprise Ltd. London .
All development and testing were carried out on the simulation first, then on the actual pilot plant.
5.
CONCLUSIONS
A hierarchical and model-based operations support system was presented in the paper. The system supports optimal production planning, supervisory control, sequence control and advanced model based control either individually or in an integrated manner. To ensure the feasible execution of scheduled activities and production plan, and to ensure local control decisions are properly propagated, a proper coordination of models at various levels of resolution was established. The need for a better control of unit operations becomes more important in the integrated operation environment to suppress disturbances locally and to provide a robust performance in response to process variations. The application of the advanced support system to the batch pilot plant has led to improved utilization of the plant resources, reduced total batch cycle time by 50%, and improved batch consistency and product quality through the nonlinear model based control. In relation to the second objective, that of increasing the productivity of engineers developing new manufacturing procedures, the system proposed makes development, testing and implementation a much easier and faster job. Many new control procedures and controllers were tested and tuned in the simulator first, then ported to the real plant, leading to a safe and easy on-line commissioning and implementation.
6.
REFERENCES
Bassett, M . H, F. J. Doyle, G. K. Kudva, J. F. Pekny, G. V. Reklaitis, S. Subrahmanyam, M. G. Zentner, and D. L. Miller (1994). Perspectives on modelbased integration of process operations, Proceeding of PSE'94, Kyongju, Korea. Chua, E. S. (1995). Integrated management system for multi-purpose batch chemical plants. PhD Thesis, Imperial College of Science, Technology and Medicine, University of London . Liu, Z. H. and S. Macchietto (1992) . Cleaning in place policies for a food processing batch pilot plant, Food and Bioproduct Processing Journal, Vol. 71, September. Liu, Z. H. and S. Macchietto (1994). Dynamic modelling and simulation of a multipurpose batch pilot plant. ADCHEM '94, 60-65, May 25-27, Kyoto Research Park, Kyoto, Japan. Liu, Z. H. and S. Macchietto (1995a). Partitioningspace approach for nonlinear process control.
600
AN ADVANCED OPERATIONS SUPPORT SYSTEM FOR A BATCH PILOT PLANT Z. H. Liu and S. Macchietto Centre for Process Systems Engineering. Imperial College. London SW7 2BY. UK.
Abstract: This paper describes the design, implementation and operation of an integrated system to support: I} the on-line operation of batch plants; 2} the off-line development and testing of new operation and control procedures. The system integrates with an industrial control system a generic simulator, a generic supervisory control system, a planning and scheduling system, and model based control functions. The functionality and use of this system are demonstrated on a complex batch pilot plant. The system greatly facilitates the rapid development, testing and commissioning of new production control schemes, new product recipes and new software/application products. Copyright © 1998 IFA C Key Words: batch plant, operations support, on-line scheduling, control, simulation
I. INTRODUCTION
The goal of this paper is to describe the design of an operations support system which provides both support functions , and to demonstrate its practical implementation on a mUltipurpose batch pilot plant.
Batch plants are typically more flexible and able to cope with an uncertain production environment (Rippin, 1991; Sawyer, 1993). However, such flexibility also results in an increased complexity of operation. both from planning, scheduling and control perspectives. A crucial aspect is presented by the strong interaction between continuous and discrete aspects. Thus batch plants are intrinsically "hybrid" . Taking into account these aspects, two key requirements for support systems are:
2.
OPERATIONS SUPPORT SYSTEM Design and Implementation
The diversity of the activities to be enabled/ supported (in terms of time scale, as well as nature of problems involved) and the complexity of batch operations mandates the use of a modular and hierarchical design of the operations support system. A hierarchical structure is also favoured due to safety, maintenance , testing and validation, and flexibility considerations.
• a production environment capable of supporting various production control functions (from process planning and scheduling, to supervisory production control and advanced control of individual unit operations). aimed at achieving high operations efficiency, product quality, operation responsiveness to changes in orders (desired) and process variations (undesired). • a development environment supporting the rapid of new development and implementation manufacturing procedures (designs and retrofit options. new product recipes, process control and automation options. operator training, etc.), aimed at achieving a high efficiency of these engineering activities.
A key requirement for an operations support system of general use is the ability to represent process operations problems in such a way that information can be structured, translated, analysed and validated at various levels of abstractions and aggregation . It should allow specific decisions to be made in a systematic way, through the use of generic support tools and techniques. Therefore, a model based approach is considered here .
595
An advanced operations support system was developed that performs both on-line control execution (utilising actual control hardware/software and running a plant) and various operations support functions . These include both a set of production planning. monitoring and advanced control functions . and a set of simulation functions. A schematic representation of the system is shown in Fig. I.
3.
MODELLING
One of the key issue is the modelling of the manufacturing system. The use of mathematical models enables the information to be represented in a formal way , and to be translated into knowledge in the form in which decisions can be made in an algorithmic manner. Due to the complexity and diversity of the problems. a variety of model representations must be considered . However. a number of modelling issues must be addressed in order to ensure that decisions can be transferred between different applications and appropriately utilised .
The box on the right includes a mUltipurpose pilot plant hardware and its industrial control system (Macchietto. 1993). The box on the left utilises the gPROMS simulator (Pantelides. 1996). which is capable of modelling and simulating generic batch plants and operations. The supervisory batch control functions are carried out using the SUPERBA TCH 2.0 software (User Manual. 1996). while optimal production planning and scheduling are performed using gBSS (Shah. 1992). The model based control function utilises nonlinear techniques described by Liu and Macchietto (1995a) for the advanced control of batch reactor operation.
3.1 Model aggregation The need for appropriate aggregation of process models (e.g. equipment. tasks . demands. etc .) has been addressed by many authors (e .g. Macchietto. 1993; Bassett. et al .• 1994). The level of aggregation required within each application largely depends on the types of decisions to be made. the local objectives and the ability of the algorithms used to handle complexity and problem size . A proper aggregation of process models is important both for reducing the complexity of problems. as well as for ensuring feasible and accurate production schedules, control. etc. For planning/scheduling. recipes and procedures are represented in the form of State-TaskNetworks. At the planning level material transfer tasks and sequential processing tasks performed in the same unit are aggregated into a single task to ensure that decisions made at different levels of aggregation are consistent and the resulting models are simple.
The production planning and scheduling. supervisory control and model based control functions can drive both the on-line and the simulated portions of the system. The simulations can be initiated from actual plant meters. SUPERBATCH or within the simulation package. The individual applications may be executed In a heterogeneous computing environment over a network. The link between all applications and functions is provided by ControlLink. a software package based on clientsserver architecture. The package ensures information (data. control commands. messages. alarms. etc.) can be transferred asynchronously among various application programs and/or control system(s).
PLANNmNG~ON1TORING
FUNCTIONS Business Planning (enterprise)
1
Planning & Scheduling (site I plant)
SIMULATION FUNCTIONS
Plant
EXECUTION FUNCTIONS I
status of and producti~
proJuction schedule
'f Supervisory Batch Control & Management !
I I
!
••
y Simulated Co;;trc;il Primitives
I I
status of Kq£,ces inventorV. ~ev'ls
finishing ~ ,.,..- C
!<--"', control
i
L_ _ __ _ ---'
iDttiate sequences
'r
set parameters time
. ........., ommumcations ./'<-
act':'
feasu~ents
Model Based Control
Information I Management System I A
y
Sequence Control
i
A ._......._. . ___._ ·!.L... _. _ Digital & Analog Control
A
y
Batch Plant
L......_ __ __
Fig. I Schematic architecture of the advanced operations support system
596
3.2 Models hierarchy and coordination
sequence is updated at high frequency by the sequence control system and polled (at a frequency of once a minute) by the supervisory system. This in turn updates its own finite state model explicitly defining the state of equipment items , batches. control phases , ete. as modelled at this level. Such states may be fed back to the planning/schedule optimization level , at the frequency of, typically , once every few hours , and may be used for reinitialising/updating optimization models at that level, as described by Chua (1995) .
To ensure feasible execution, the more aggregated tasks used for process planning and scheduling are decomposed into more detailed control entities (phases and procedures) suitable for the supervisory control level. For example, the aggregated reaction task in a recipe for making a dextrin intermediate (Fig. 2) is decomposed into its individual phases (feeding, pH control, gelatinization and degradation). Furthermore, these control phases must be mapped to even more detailed equipment control sequences. Proper coordination between these entities must be established to enable them to be correctly linked. Disaggregated phases are mapped to "top sequences" at the highest level of the control sequence hierarchy (e.g. sparge control). A schematic representation of the model hierarchy used is illustrated in Figure 2.
4.
Within this design, each application can be operated/tested individually or in combination with others. For example, an off-line simulation of a process can be performed individually. In this case, the order of the tasks to be executed may be predetermined and fixed a priori (in an open-loop manner) and programmed as a specific "schedule" in the gPROMS language. This mode of operation is useful for evaluating new control sequences (Liu and Macchietto , 1992), testing a model based control algorithm (Liu and Macchietto , 1995a, 1995b), debugging a dynamic model, etc. Then, the simulator may be connected to and driven by the supervisory controller. The control sequences (simulated) will then be executed according to the supervisory control algorithm, which will modify the schedule in a closed-loop manner. This provides a useful test bed for production planning and supervisory level control strategies. In this mode, it is possible to evaluate the effect of any variations on operations (built into the simulation model) and to test the effectiveness of the supervisory decisions .
water
0--- . '"""' °cQ.~ :,.J-'
int-dcx
slurry
0.94- make_slurry
i- ~--- i reaction ~
starch
1·~· ""Ph~~~~tr.o.lj '.~~:I~:i~.;~~~~~J ·ldegra.da~~~J I ··-.. ·..-:.:.:::::::..=
T,- ----···-·-·-
J.::::::::::.::
I: sparge control i ;
!
:level monitor i :.:_~:':~~::~ j
OPERATIONS
i interlock !
Fig. 2 Model hierarchy and coordination The above modelling guides were used to develop batch pilot plant models at various level of detail. These include a state-task-network model for production planning, a supervisory control model for on-line scheduling and batch management, and a detailed dynamic model of the plant in gPROMS. These models are mapped and properly coordinated to ensure a proper communication between each level of controls. Detailed descriptions can be found in Liu (1995), and Liu and Macchietto (1994).
The following section describes in detail just the use of the operations support system for on-line scheduling and supervisory batch management. The use of the system for other applications, such as developing and validating new control procedures, model based control algorithms, etc. is described in Liu (1995).
4.1 On-line scheduling and supervisory control The SUPERBATCH 2.0 system is used to perform supervisory control functions. connected to the actual plant control system (ACCOS). Supervisory control lies at the transition between on-line production control and off-line planning/ scheduling activities. Its main functions are :
3.3 General remarks As observed, the key to proper coordination between all production control levels is a careful analysis and definition of model aggregations to be used at each level. In order to support communication between levels, in the upward directions, the state of equipment and control function models at each level is explicitly defined and updated . Such model contains states significant for control/measurement purposes. For example, the state of each top level
i)
597
to coordinate off-line planning and scheduling activities with those of the production control system;
lines) between batches and cleaning at end of production (The interfaces from Batch-Manager. a software product of APV built around SUPERBATCH). The Y-axis represents main plant equipment and the X-axis is the time. The vertical line shows the current time and moves to the right as the schedule is updated once a minute. The part of the chart on the left of the vertical line represents the production history of the plant and that on the right part is the planned schedule. Storage units are displayed as the predicted inventory level profiles (0100% scale) and final status (material types and inventory levels) . All materials being processed (raw, intermediate and final products) are coloured coded and each object on the screen is linked to its entry on the production model database.
ii)
to initiate , supervise and coordinate the execution of the scheduled activities carried out by lower level controllers (e.g. sequence control); iii) to accommodate any variation at either higher or lower levels by rescheduling production, while enforcing feasible and safe operation.
Towards that end, the supervisory control level requires knowledge of and data from both the lower level controls and the higher level production planning and scheduling models/activities. At the supervisory leveL operational feasibility and computational efficiency are very important since this is an on-line activity. The major economic decisions (batch SIzes , sequence of batches and maIn equipment allocations) from the external higher level planner/scheduler are imported to the system and verified for feasibility. A detailed production schedule is generated first in an off-line version of the software . Upon satisfactory confirmation that the more detailed schedule is acceptable (typically by a production manager, supervisor or operator, depending on management delegation in a plant), it is then passed on to an on-line portion of the system , an on-line monitor, which runs continuously. New requirements are processed and executed through the control systems. The supervisory system structure is schematically shown in Figure 3.
A rmduct lon
de mands
~t atc
v
off-line ", _ _scheduler
j
"'production ,.
v
models
,.--- - - -,If-
commands
queries
->
I '.
on-line
- --monitor -- _./
A! Iml1a(u . .. phascs
pI ant'phase I status ~
set parameters
Control System i
Fig. 3 SUPERBATCH system structure
Figure 4 shows in the form of Gantt chart an initial schedule of a starch degradation process carried out In the pilot plant, with intermediate cleaning (hatch Catch-Tracker
Tt... : 16:06 _
~...
~.-.,--.....
""- _ _ _ _ _ _ _ _ IIIio¥o
~ton Iioat;;;;-s.;i.;-'"~~ ~ Ai"';-DiS'Pl~
_
ih
__ -_- :--'4
Fig . 4 Supervised operation of batch pilot plant for starch conversion process with CIP at start time 16:06
598
The execution of the production schedule is initiated and supervised by the on-line monitor in real-time at 16:06, and control commands (e.g. start a sequence) and control parameters (e.g. batch size. setpoints. etc .) are passed to the control system. The first operation, which is transferring process water into tank T I , is started and others are waiting. During the operation, the status of the control sequences (e.g. start, complete, alarm. hold, etc.) and of the plant (inventory level , final batch size achieved) are fed back from the control system to the on-line monitor component of the supervisory control. where updating and rescheduling is performed. Detailed information related to the current production schedule and the status of the execution can be obtained through an operator terminal. Execution and reVISIOn of production schedule is essentially performed in a closed-loop manner.
allows the operator to make an early decision before the problem actually occurs. For mmor production variations such as 111 processing times. the on-line scheduler adjusts the production schedule automatically and updates the schedule every minute . For major yariation s/ disturbances such as equipment breakdown . ne\\' product orders. etc. an off-line production schedule is typically generated first , taking into account the variations and the current status of the production schedule. Here. alternative production scenarios (involving inclusion/deletion of new hatches . alternative assignments or batch sequences , etc.) may be easily generated. displayed with alerts similar to Fig. 4 and 5, and assessed. Once an off-line schedule is approved by the operator/supervisor, then this new schedule can be passed to the on-line monitor for execution. During the above off-line scheduling process (which typically takes a few minutes), the execution of the original production schedule is continuing.
Figure 5 shows the new production schedule at time 16:28. By then, two operations (making slurry in Tl and transferring slurry to the reactor T3) have finished successfully (on the left of the vertical line) . Two other operations are being executed ( pH control in the reactor T3 and cleaning of TI through He3 and A V 1-40). Due to an unforeseen delay of the pH control operation (gaps on the left of vertical line) , the scheduler detected that the batch with ID 00 I (producing Malto-dextrins) is expected to finish late, even after rescheduling in view of the delay. The monitor thus sends an alert signal to the operator, displaying the alert message and highlighting the late finishing batch (Fig. 5). This
In above example. production control is carried out in a closed-loop manner. Apart from ensuring operational feasibility at all times and effective use of all production resources, the supervisory controller leads to a considerable reduction in operator load . Another major advantage is that the integrity of the operation and the repeatability of the operation are assured by executing all processing procedures in the correct order, precisely and correctly at all time .
Fig.5 Supervised operation at time 16:28
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Proceeding of D YCORD '95, June 7-9, Helsingor. Denmark. Liu, Z. H. and S. Macchietto (1995b). Model based control of a mUltipurpose batch reactor - An experimental study. Computers Chem. Engng Vol. 19, Suppl. , pp. S477-S482. Liu, Z. H. (1995) . An advanced process manufacturing system - design and application to a food processing pilot plant. PhD Thesis, Imperial College of Science, Technology and Medicine, University of London . Macchietto, S. (1993). Bridging the gap - integration of design. operation scheduling and control , proceeding of FOCAPO 11, Colorado, July 18-23. Pantelides, C. C. (1996). An advanced tool for process modelling, simulation and optimisation. Proceeding of Computers Europe Ill. Frankfurt Rippin, D.W.T. (1991), Batch process planning, Chemical Engng., Vol. 98, No .5, ppl00-107, May. Sawyer, P. (1993). Computer-controlled batch processing. IChemE, Rugby , UK. Shah, N. (1992). Efficient scheduling, planning and design of mUltipurpose batch plants. PhD Thesis. Imperial College of Science, Technology and Medicine, University of London. SUPERBATCH 2.0 (1997) . User Manual. Process Systems Enterprise Ltd. London .
All development and testing were carried out on the simulation first, then on the actual pilot plant.
5.
CONCLUSIONS
A hierarchical and model-based operations support system was presented in the paper. The system supports optimal production planning, supervisory control, sequence control and advanced model based control either individually or in an integrated manner. To ensure the feasible execution of scheduled activities and production plan, and to ensure local control decisions are properly propagated, a proper coordination of models at various levels of resolution was established. The need for a better control of unit operations becomes more important in the integrated operation environment to suppress disturbances locally and to provide a robust performance in response to process variations. The application of the advanced support system to the batch pilot plant has led to improved utilization of the plant resources, reduced total batch cycle time by 50%, and improved batch consistency and product quality through the nonlinear model based control. In relation to the second objective, that of increasing the productivity of engineers developing new manufacturing procedures, the system proposed makes development, testing and implementation a much easier and faster job. Many new control procedures and controllers were tested and tuned in the simulator first, then ported to the real plant, leading to a safe and easy on-line commissioning and implementation.
6.
REFERENCES
Bassett, M . H, F. J. Doyle, G. K. Kudva, J. F. Pekny, G. V. Reklaitis, S. Subrahmanyam, M. G. Zentner, and D. L. Miller (1994). Perspectives on modelbased integration of process operations, Proceeding of PSE'94, Kyongju, Korea. Chua, E. S. (1995). Integrated management system for multi-purpose batch chemical plants. PhD Thesis, Imperial College of Science, Technology and Medicine, University of London . Liu, Z. H. and S. Macchietto (1992) . Cleaning in place policies for a food processing batch pilot plant, Food and Bioproduct Processing Journal, Vol. 71, September. Liu, Z. H. and S. Macchietto (1994). Dynamic modelling and simulation of a multipurpose batch pilot plant. ADCHEM '94, 60-65, May 25-27, Kyoto Research Park, Kyoto, Japan. Liu, Z. H. and S. Macchietto (1995a). Partitioningspace approach for nonlinear process control.
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