Fully Automated Stripping Column

Copyright © IFAC 12th Triennial World Congress. Sydney. Australia. 1993

FULLY AUTOMATED STRIPPING COLUMN A. Aujesky ICI Australia Engineering. 486 Albert Street , East Melbourne. 3002 Victoria, Australia Abstract.

Advanced control has been applied to a VCM stripping column to fully automate its operation. This resulted in significant improvements in the column's operation: optimised throughput, improved product quality, elimination of losses and energy savings. The control system features a blend of carefully selected modem control techniques (user-friendly and robust sequencing, continuous control using an on-line model and rule based algorithms) "welded" together into a harmoniously working unit.

Key words. Rule based control; sequencing; intelligent control; process control; stripping column; on-line model.

1. 1.1.

INTRODUCTION

1.2.

Intelligent Control - The Perfect Operator

To fully automate a chemical process can be a daunting task. Full automation includes not only the steady state operation, dealing with disturbances and ramping the throughput, but also shutdown , startup, cleaning operations etc. The requirements are often such that the resulting control sytem must provide sequential and continuous control. Sequential control must be able to take the plant or unit from one state to another state. For instance, from shutdown conditions to a startup and finally to production. Each major state consists of a number of sub-states executed sequentially (valves open/closed pumps started/stopped ete). Continuous control must be able to cope with a wide range of conditions. It is not only required to maintain the process at the setpoints (steady or ramped) but also controlling to changing criteria. A fixed structure of standard building blocks connected together to form control loops may no longer be adequate. Flexible connections may have to be established to dynamically invoke different control algorithms in response to changing conditions. Continuous control must be closely linked with the sequential control to ensure coordinated actions.

New methods of controlling chemical plants are being introduced to improve operation, save energy, enhance safety and ultimately to increase profits. These may be expert systems, artificial neural networks, fuzzy control and many others. All of these methods, regardles of their names and the underlining theoretical foundations, have one thing in common - they exercise "intelligent" control. Because of this, they have the potential of delivering stunning results. To illustrate the point, lefs consider a level control in a reflux drum of a stripping column. A standard PlO controller would maintain a steady level at the expense of changing the reflux and consequently upsetting the conditions of the column - Fig. 1 below. FIG 1. . PlO CONTROL 100 90

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Fully Automated Process

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1.3.

The Challenge

0 2

To meet the challenge of implementing full automation of a process, one has to adopt the right aproach and use the right 'control tools'. The following sections show how this has been done for the automatic control of a Vinyl Chloride Monomer (VCM) stripping column at ICI Plastics - Laverton Site.

TIme (hrsl

With an expert system implemented on the column a compromise was achieved between the conflicting requirements of level control and reflux control. The result was a much smoother operation - Fig 2 below. FIG 2. • EXPERT CONTROL 100 , _ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ,

2.

THE PROCESS

90

The stripping column (shortly the 'stripper' ) is used in Polyvinyl Chloride ( PVC ) production. PVC is made from VCM in polymerisation vessels, called autoclaves. The autoclaves run independently of each other, but they discharge their product - PVC / water slurry - into a common holding vessel. The slurry contains some unreacted VCM which must be stripped off to guarantee a clean final product. To do that, the slurry is fed to the stripper, where the VCM is released by steam stripping. The clean slurry is then pumped into one of the slurry tanks, where it is stored before further processing. The stripped off VCM is fed to the VCM recovery plant to be recycled .

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This example has only shown a small application of advanced control. To get major benefits from advanced control, one may have to expand its scope, say, to a fully automated process.

235

After some time on production the column needs cleaning. This is done by applying a series of cleaning operations (a rumble cycle) . When clean, the column is returned to its normal operation. From time to time the plant production switches onto a different grade . For the stripper this means execution of a complex cleaning procedure . Care must be taken to prevent cross contamination of products. 3.

4.4

CONTROL STRATEGY While ACTIVE the Unit continuously monitors all its digital "on/off" devices ( block valves and motors) . The checking criteria are phase dependent, for example, while on slurry some valves must be closed, others open or just "healthy" (no alarms) . On detection of any problems the Unit drives itself to HOLD (with appropriate messages). This means that the unit operation is very safe . Malfunctions in the field, accidental operation of the wrong valve by people or by a control program are detected and treated immediately. 4.6.

Slave Units Control - Easy _De~ig,o

SEQUENTIAL CONTROL Some phases require coordinated actions in other plant areas outside the stripper. The stripper unit communicates with units responsible for those areas and coordinates their actions . This is implemented in a proven Master/Slave style of operation which simplifies design and enhances safety .

During its operation the column goes through a number of stages called phases. In each phase, different activities take place. For instance, there is a phase for heating up the column , a phase for running it on water only, another one for processing slurry etc. Most of the time the column stays in the slurry processing phase. However, to handle certain conditions (eg rumble) other phases may be invoked. To implement smooth operation, transition from one phase to another is done automatically. This means that the operator does not have to keep a close watch on the stripper and can concentrate on other tasks. However, should the need arise, he may intervene and force a switch to a phase of his choice . 4.2.

Sav~s. Ti~

On restart from HOLD the control of the current phase automatically resumes execution from the best possible' point within its sequence logic . This means a smooth restart , minimum time wastages and simplicity of operation .

The overall control strategy aims for total automation of the process. It requires using sequential and continuous control integrated into a harmoniously working unit. This was achieved by utilizing the already installed MOD 300 distributed control systems with the PLANTMASTER batch control package, together with extensions described here. PLANTMASTER is an in-house developed control concept, proven as being user-friendly and robust Aujesky( 1990,1991), Georgakopou los (1991).

4.

Intelligent Restart Facility -

Afu~Unit

5.

CONTINUOUS CONTROL

Continuous control plays a very important role in the overall control strategy . It is implemented as a sub-system within the Unit. It responds to the requests issued by the phases. There are several tasks the continous control performs. Each task is concerned with a selected area of control or an aspect of control. The tasks are tightly coordinated to work together to achieve a common set of goals.

- Makes it Sin:)Q!~

For the purpose of control the whole column and its associated equipment is presented to the operator as a single unit. This greatly simplifies the operator's job. He does not have to think in terms of individual loops and their interactions, but only of the unit as a single device.

5.1.

Process Model - Ensures the Right Conditions

As a "single device" the Unit has a Status associated with it (like other devices, say, valves which may be open, closed, in travel etc. The Unit Status can be either ACTIVE, HOLD or MAINTENANCE. While ACTIVE the Unit performs actions appropriate for the current phase (eg processing slurry) . The Unit is continuously monitoring certain critical inputs (say block valves) or time based activities. On detection of errors it turns itself to HOLD. (The Unit can be also turned to HOLD by the operator.) While on HOLD the Unit interrupts the current phase and performs a series of safety actions. From HOLD the Unit may be restarted to ACTIVE or be driven to MAINTENANCE. When restarted the phase resumes execution from "the best" restart point. In exceptional circumstance the operator may decide not to resume automatic control and drives the Unit from HOLD to MAINTENANCE.

The model design is very robust. This is due to definition of the control goal. The goal is to keep the unit within its operational limits. For process within the limits the results of the model's calculations are of no consequence to the control. On the other hand, for process outside limits there is usually more than one calculated value indicating the condition. Thus if one of the indicators is miscalculated the others are sufficient to trigger a corrective action. Imprecise inputs or minor mismatch between the model and the reality do not affect its performance greatly. This point has been proven in practice, when it has been found that a slurry inlet temperature (deemed to be an 'absolutely essential' measurement) did not register temperature changes, but there was no degradation in the control system performance .

This task IS responsible for maintaining the column within operational limits. It is implemented as an on-line (static) process model. It gets measurements, recipe and other data and calculates a set of indicators for use by other control tasks.

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5.2.

gQ]~rT1rl .Control

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- Smooth Operation

There are two tasks responsible for the actual dynamic control of the column. They read plant inputs and data provided by the on-line model and the phase logic and generate control outputs. In order to ensure the best control for different modes of operation the tasks reconfigure themselves dynamically. The control contains some advanced features , such as rule based feedback and feedforward control. To explain the principles of design, control of steam (while processing slurry) is described below .

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While processing slurry , the column's steam supply is regulated to maintain steady stripping conditions. Stripping conditions are indicated by the overheads condensate flow . Keeping this flow at its setpoint means keeping the column steady. For purpose of control the overheads flow is a very good indicator as it exhibits sensitivity and very short response time to changing steam of feed rates . As the condensate flow was not available a computational block was set up to calculate it from the reflux and level in the overheads separator.

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FIG. 3 - RULE BASED FEEDBACK CONTROL

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While processing slurry , the control must Maintain overheads condensate flow within limits (near its setpoint) Maintain steam within its limits(ex Model) React to the changes in feed rate and temperature React to requests by the Model Cope adequatelly with non-linear characteristics of the process Avoid unnecessary sudden changes to steam as these may adversely affect product quality and destabilise control

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CONTROL ACTION DECISION TABLE

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To keep the column running within its operational limits, the Process Model keeps checking continuously on the stripping conditions. As the result of these calculations, it sets the high and low limits for the overheads flow and the steam flow . To handle transient conditions the Process Model may also set an internal request to increase or decrease the steam flow . An external hardware PlO controller is used to exercise the low level function of maintaining the steam flow at setpoint value. The controller receives its setpoint from the MOD 300 control scheme. The backbone of the control scheme is a feedback control on the overheads condensate flow , which manipulates the steam setpoint of the external PlO controller. A rule base algorithm is used . This type of control has been choosen, in preference to a PlO , mainly for its flexibility . It is very similar in style to the fuzzy logic control described by Tetsua Ohtani(1990) and by MurphY(1992) . However, due to computational constraints fuzzy sets were not used. All the decisions were based on a "crisp " logic . The rules cover feedback and Process Model constraints. For the general principles involved , see the figures 3 and 4 . Note that control stability is achieved via placing NIL actions at a "strategic" locations in the decicision table.

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FIG. 4 - RULE BASED FEEDBACK CONTROL CONTROL ACTION DECISION TABLE

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FIG 6 - PRODUCT IMPURITY

Changes to slurry rate have a drastic effect on the stripping conditions. It would be hard for the feedback control to cope with this. To improve control, feedforward compensation on slurry rate has been applied. As the process exhibits non-linearities the scheme includes a "self tuning" feature, as shown in the figure 5. The linear gain is set at the highest value ever required for the given operating range . It is then trimmed down automatically by a built-in gating mechanism. The idea is to disconnect the feedforward signal whenever it appears to cause an overshoot of the controlled variable (overheads flow) .

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When extrapolated, the data indicate the potential annual savings of $ 220 K. (At full production rates the projected savings will be much higher.) CALCULATE FF INCREMENT

FF INCR There are also intangible benefits arising from freeing the operator from routine control tasks, thereby allowing him to concentrate more on high level scheduling and supervisory tasks.

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CONCLUSION

The results (measured and observed) confirmed that advanced process control, if implemented properly, can bring substential benefits to the chemical industry.

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8.

The author wishes to acknowledge the contributions made by Mr Spiro Georgakopoulos of ICI Laverton, by getting the project started and by participating in the design and commissioning of the system.

FINAL FF ---> INCR

(for steam SP)

9.

Throughput Optimiser - Removes Bottlenecks

The purpose of this task is to adjust the slurry feed rate to eliminate production bottlenecks. It is based on a predictive constrained algorithm, specificaly designed for this purpose . 5.5.

Tanks Control Tasks - Ensures Quality

This task is responsible for slurry tanks management. Its main function is to ensure that the nominated tank contains a high quality product. "Suspect" or "off specs" product is automatically channeled to other tanks. It also performs other functions such product tracking to prevent accidential cross contamination of diferent grades, level monitoring etc. 6.

REFERENCES

Aujesky A. (1990) "MOD 300 in PVC Plant Control" MOD 300 User Conference, Global Forum, Rochester NY, USA. Aujesky A. (1991) "Software Structure for Large Batch Applications" MOD 300 User Conference, Global Forum, Rochester NY, USA. Aujesky A. (1992) "Principles of Robust Batch Control" The Second Conference on Automation, Robotics and Computer Vision, Singapore, Sept. 1992. Georgakopoulos S. (1991) "Computer Control of Stripper - Expenditure Proposal" ICI Plastics Laverton internal document. Georgakopoulos S. (1988) "Stripper Operation Notes" ICI Plastics Laverton - intemal document. Georgakopoulos S. (1991) "Commissioning a New Process Computer System Without an Extended Plant Shutdown" Automation 91 Conference, lE Aust, Melbourne. Murphy P. (1992) "Fuzzy Logic Smooths System Control" I&CS March 1992 Tetsua Ohtani, Masako Negishi, Joji Murakami(1990) "Fuzzy Control of Basis Weight Profile for Papermachines" Yokogawa Technical Report - English Edition, No 11 (1990)

FIG. 5 - RULE BASED FEEFORWARD CONTROL

5.4.

ACKNOWLEDGEMENTS

RESULTS

Data collected over the period of last three month show remarkable improvements in the stripper operation, due to the advanced control. (For example product impurities dropped drastically as shown in the figure 6. )

238