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BETTER PLANT PERFORMANCE THROUGH BETTER

ANALYZER MANAGEMENT

Hans van Nuenen

KROHNE Oil & Gas

Breda, the Netherlands

KEYWORDS

ANALYZER MANAGEMENT, DATA ACQUISITION

INTERFACING, DCS, LIMS, ERP

ABSTRACT

For decades, refineries and chemical plants have been spending a significant

amount of money on projects for the installation of Quality Measuring Instruments

(QMI) including in-line process analyzers, environmental quality analyzers, gas

detection sensors etc. In comparison, almost no investment is done on an integrated

plant quality data infrastructure which creates means for considerable

improvements on the availability and accuracy of these capital assets. Present

techniques for automatic data acquisition and data handling minimize the clerical

work inherent to manual procedures, hence improving the reliability of data

information. Decision makers will benefit from the maximized transparency on

true performance of the analyzers in the plant, in particular for custody transfer

applications (QMI-release) where the return on investment will become even

obvious.

INSTRUMENT VALIDATION

The high manpower cost of maintenance for QMI’s makes it essential to have better means for

cost control. However, reducing the direct cost of maintenance could well lead to unacceptable

risk levels of plant unavailability, the cost of which could far outweigh the direct cost avoidance.

Therefore a balance has to be found to optimize controlled cost cutting.

One of the many time consuming activities in the maintenance work on QMI’s is the validation

and calibration of instruments. The validation intervals, if specified by the manufacturer, are

typically worst case figures to guarantee maximum accuracy. However, by continuously

monitoring the performance of the instruments, it often appears that the validation intervals can

be extended considerably, hence reducing cost substantially.

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A maintenance person may adjust (calibrate) an instrument if a deviation of the expected value

occurs. However, by applying some statistics, it often appears that this is not necessary, or even

worse, may make the instrument less accurate. In order to apply statistics, historical figures are

required.

FIGURE 1. INAPPROPRIATE INSTRUMENT CALIBRATION

Specifically the current generation of instruments is far more reliable and accurate than they are

given credit for. Where operators have tended to place blame on analyzers, these instruments are

seldom at fault. By monitoring the instruments, a historical record of past performance can be

built up to substantiate an assertion, avoiding cost.

When the method used to check the instrument accuracy, is not well developed or procedures are

not adhered to, the blame of inaccurate operation will normally be put on the instrument. Means

have to be provided to detect these situations as early as possible. The normal scatter in results

and the variation of the drift in time make it necessary to obtain validation measurements over

longer periods in time. The validation process will have to be based upon the use of statistical

control techniques. These analytical validation results have a relation to the maintenance

activities executed and determine those to be executed. Combination of analytical and

maintenance data will lead the way to elimination of error sources in the measuring and checking

process, which otherwise go unnoticed. When this is to be executed by hand it is a time

consuming exercise and therefore normally not performed.

It has been shown at various plant sites that this approach, when automated, leads to better

performance of the instruments and more efficient use of maintenance manpower. It has been

shown that simplification of the data acquisition and data entry process is required to get

sufficient and continuous cooperation of all parties involved, to make the application a success.

Data entry facilities have to be present at the actual job locations, to eliminate the need for

intermediate hand written notes. Tedious data input can be eliminated. Vague procedures can be

replaced by well defined dialogues via operator stations with the use of automatic logging and

registration of events and personnel identification. Unnecessary time delays in execution of these

guided procedures can be traced [1].

- 2

- 1.5

- 1

- 0.5

0

0 .5

1

1 .5

2

1 4 7 10 13 16 19 2 2 25 28 31 34 3 7 40 43 46 49 52 55 58 61 64 67 70 7 3 76 79 82 85 8 8 91 94 97 100

Instrument variation

Instrument calibration

Warning Limit

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STAKEHOLDERS

The bottom-line reason why you need better analyzer management is to be able to give key

people better information to make decisions on how to run the plant equipment. Such people

include:

Process Operators in order to keep track of the operational state and overall availability

of on-line analyzers

QMI Engineers as they are informed about the actual analyzer performance against

target

Laboratory Personnel responsible for the quality of all traceable standards on site and of

the certification of quality of all products that are transferred to customers against

established contracts

Maintenance Staff in their effort to achieve optimal efficiency in analyzer maintenance

activities and to ensure highest availability of process analyzer assets

Plant Management as the ultimate custodian of the equipment concerned with the

responsibility for improvement in plant and refinery effectiveness

ANALYZER MANAGEMENT

FIGURE 2. INTEGRATED ANALYZER MANAGEMENT SYSTEM

Integrated Analyzer Management is the overall term used to describe the combination of:

Analytical Performance – on a continuous basis

Analyzer Availability – keeping track of operational states

Analyzer Maintainability – making accessible to maintenance

Decision maker

Distributed Control System (DCS)

Enterprise Resource Planning (ERP)

Laboratory Info. Management System(LIMS)

Plant Information System (PI)

Integrated Analyzer Management System

Plant I T infrastructure

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ANALYTICAL PERFORMANCE

Validation is the process of confirmation of actual analyzer performance against traceable and

accepted standards and is, in contrast with calibration, a non-corrective metrological procedure.

Standards are referred to as ‘reference samples’ or ‘calibration materials’. Standards are usually

kept within confines of the plant and either maintained by the technicians responsible for the

analyzer system or by the laboratory.

Traceability shall be achieved through methodology, e.g. as per ASTM [2][3] or SMS [4]

methods

Accepted refers to mutual agreement between partners about the used standards and

methodology followed. Partners are all who have a defined interest in the use of the analyzer

system[5].

To verify that an instrument is working correctly, various procedures have to be executed. These

procedures are related to accuracy and time response.

Methods of validation [6] in relation to accuracy are:

Reference sample method – The sample reference material is directly introduced at the

sample port of the analyzer. The recorded analyzer result is compared with the accepted

reference value of the reference sample.

Line sample method – During normal operation of the analyzer a sample is drawn at the

analyzer sample port and the corresponding analyzer result is recorded. The sample is

analyzed by the laboratory. The analyzer result is then compared with the accepted

reference value produced by the laboratory.

Reference measurement method – With this method a portable analyzer is temporarily

lined-up in series with the one-line analyzer. The reading of the on-line analyzer is

compared with the reading of the portable analyzer. The reference measurement can be

treated for data handling in the same way as the line sample method.

Filter method – This is a simple method which resembles the reference sample method

and has the same data handling. E.g. an optical filter is used to simulate a change in

process conditions.

Methods of validation in relation to timing are:

Response time method – The time measured for the detector to reach its threshold upon

exposure to a reference sample

Timed response method – The response is measured after a predetermined period of

exposure to a reference sample

AVAILABILITY

Availability is defined as a measure of the degree to which an item is in an operable and

committable state at the start of a mission when the mission is called for at a random point in

time. Availability is the parameter that translates system reliability and maintainability

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characteristics into an index of effectiveness. It is based on the question, “is the equipment

available in a working condition when it is needed?”

Mean Time Between Failures (MTBF) is the predicted elapsed time between inherent failures

of a system during operation. MTBF can be calculated as the arithmetic mean (average) time

between failures of a system. The MTBF is typically part of a model that assumes the failed

system is immediately repaired (zero elapsed time), as a part of a renewal process.

Mean Time To Failure (MTTF), in contrast with MTBF, the MTTF measures the average time

between failure with the modeling assumption that the failed system is not repaired but replaced.

Mean Time Between Maintenance (MTBM) includes all corrective and preventive actions

compared to MTBF which only accounts for failures.

Mean Time To Repair (MTTR) is a basic measure of the maintainability of repairable items. It

represents the average time required to repair a failed component or device. Expressed

mathematically, it is the total corrective maintenance time divided by the total number of

corrective maintenance actions during a given period of time. It generally does not include lead

time for parts not readily available, or other Administrative or Logistic Downtime (ALDT).

Administrative and Logistics Down Time (ALDT) is the time spent waiting for parts,

administrative processing, maintenance personnel, or transportation (no maintenance time).

Mean Down Time (MDT) is the average time that a system is non-operational. This includes all

time associated with repair, corrective and preventive maintenance, self imposed downtime and

any logistics or administrative delays. The difference between MDT and MTTR (mean time to

repair) is that MDT includes any and all delays involved; MTTR looks solely at repair time.

Availability, in the simplest form, is: A = Uptime / (Uptime + Downtime) .

Inherent Availability looks at availability from a design perspective:

Ai = MTBF / (MTBF+MTTR).

Operational Availability

In the operational world we talk of the operational availability equation. Operational availability

looks at availability by collecting all of the breakdowns in a practical system

Ao = MTBM / (MTBM+MDT).

MAINTAINABILITY

Preventive maintenance is generally considered to include both condition-monitoring and life-

extending tasks which are scheduled at regular intervals. Some tasks, such as QMI validation,

must be done while the equipment is operating and others, such as internal cleaning, must be

done while the equipment is shut down.

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Corrective maintenance is defined as maintenance work which involves the repair or

replacement of components which have failed or broken down. For failure modes which lend

themselves to condition monitoring, corrective maintenance should be the result of a regular

inspection which identifies the failure in time for corrective maintenance to be planned and

scheduled, then performed during a routine plant outage. When corrective maintenance is done,

the equipment should be inspected to identify the reason for the failure and to allow action to be

taken to eliminate or reduce the frequency of future similar failures. These inspections should be

included in the overall maintenance plan.

Predictive Maintenance is the art of knowing in advance which maintenance will be needed

and when. The discussion on the value of predictive maintenance has long been decided. There is

no doubt that cost, time, personnel, profit, even company reputation can benefit from doing what

is needed at the most convenient time.

ANALYZER MANAGEMENT IN HISTORY

Historically, Analyzer Management has typically been a manual operation, if done at all.

If authorized maintenance personnel start a validation, he first has to inform the operator about

the fact that he intends to take the instrument off-line. If not, then normal process operation

might be interrupted.

Normally a work instruction has been made per type of instrument. In this instruction all steps to

be performed to validate and /or calibrate an instrument has been listed. These instructions have

to be followed each time a validation has to be performed.

The danger in this approach is that the written instructions might be used only the first few times.

After a while, the engineer will typically execute the validation from his memory. Of course this

will be correct in most cases, but invariably mistakes will be made especially in case of

temporary replacement of manpower. Also instructions might change over time without the

maintenance engineer noticing. An integrated Analyzer Management System can present on the

spot the up to date instructions for each instrument and even more guide the engineer through the

procedure.

According to the written instructions, readings from instruments will have to be taken. This can

be the cause of possible misinterpretations, misreading and typographical errors. Automatic

reading at the right moment can be dealt with by an integrated Analyzer Management & Data

Acquisition System.

Often calculations have to be made, e.g. the percentage of range. The readings have to be entered

in a calculator, the calculation has to be made and the result must be written down. Again there

are ample possibilities for errors. Calculations can, of course, also be performed by an integrated

Analyzer Management System.

The results of a validation session will normally be written down on an instrument work sheet.

This is a historical overview of all validations, calibrations and repair. Although this gives a

good overview per instrument, it is not very well suited to get an overview over several

instruments. Therefore, these data are being retyped into a computer database to allow for easy

manipulation. It is obvious that this is a time consuming effort with plenty possibilities for

mistakes.

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INTEGRATED ANALYZER MANAGEMENT

To avoid the above mentioned problems, an integrated Analyzer Management and Data

Acquisition System (AMADAS), such as CalSys® could be implemented with the following

objectives in mind:

Independence with respect to the Distributed Control System (DCS) and instruments –

Complete independence of any particular brand of equipment means that the system can

be used in any plant without the need for re-design and/or re-implementation.

Independence also means that future developments are easier to handle, e.g. it is more

likely that new instruments of other vendors can be hooked to the independent integrated

Analyzer Management System.

Communication interfaces to the major DCS and instrument brands – The system should

be capable of communicating with various on the market DCS systems and also with

different analyzers of any brand. Communication between the DCS and the integrated

Analyzer Management System allows for:

o The process operator to initiate validations via the DCS

o The DCS to give the integrated Analyzer Management and Data Acquisition

System (AMADAS) permission to start a validation

o The DCS to perform the data-acquisition and process control for the Integrated

Analyzer Management System, but only after permission has been granted by the

operator to go into maintenance mode. Then the actual validation is executed by

the Analyzer Management System. AMADAS can request the DCS for preparing

an instrument validation and wait for the permissive signal. This ensures that

AMADAS validates the entire chain including A/D conversion and processing in

the DCS system.

o Automatic or semi-automatic software calibration by means of changing signal

conversion constants in the DCS system.

Communication between instruments and AMADAS can be useful in cases where the

DCS cannot provide all data of a particular instrument. Instruments sometimes use

different data channels for measurement data and validation/calibration data. A direct link

between AMADAS and the particular instrument will by-pass these limitations and

provide the means for automated validation/calibration.

Elimination of the need for redundancy in AMADAS – As the zero and span correction

values resides in the DCS, an interrupted AMADAS operation will not interfere with the

process operation of the instruments and with the presentation of the data to the operator

via DCS. Hence, there are no strict redundancy requirements for AMADAS which allows

for a straight forward and robust IT infrastructure.

Communication interfaces with Laboratory Information Management System (LIMS) – In

order to support the Line Sample Method of validation, a direct link between LIMS and

AMADAS is required. Time stamped data sets are used to link the laboratory analysis

results to the on-line analyzer readings at the moment of sample taken.

Communication interfaces with Enterprise Resource Planning (ERP) system – Preventive

maintenance actions can result from the validation sessions executed. Interfacing between

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AMADAS and ERP will allow for instant job activity registration and maintenance job

ticket submission via the ERP system.

Direct process interfaces – In some circumstances data required for proper maintenance

are not be acquired via the DCS or DCS has no control over the stream selection valves

that connect an instrument to the process and calibration equipment. AMADAS is, in

these circumstances, capable of having its own analog and digital I/O interfaces to

perform the data acquisition and control directly. An interface with DCS is still required

in case automatic authorization is required.

De-centralized architecture with central database – It is unrealistic to assume that all

validations and calibrations can be automated and controlled remotely. It is more likely

that some can be initiated from a central control room while others require that the

instrument is within reach. Ideally, the AMADAS workstations are located where the

validation/calibration takes place and data is stored in a centralized database server. De-

centralization also increases the system availability and allow for multi-disciplinary use

of AMADAS.

FIGURE 3. PLANT WIDE INTERFACING

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KEY PERFORMANCE INDICATORS

Key performance indicators show where an asset is performing well or even over-performing.

But more importantly is to know where the assets are underperforming, to what extent and to

how to schedule any necessary maintenance.

FIGURE 4. KEY PERFORMANCE INDICATORS

Such information is highly transparent. It detects areas of immediate, medium and long-term

concern. It can help predict sources of trouble. It focuses on bad actors in the plant assets. But

also, it gives the operator the flexibility to schedule his improvements and maintenance in a

timely manner. This saves on:

Time

Costs

People

Convenience

Other Resources

PLANT-WIDE PERFORMANCE SUMMARY

The most obvious advantage of the plant wide available information is to track the performance

of each individual analyzer. It can also be used to compare different types of analyzers amongst

each other to determine the best type for the particular task in terms of performance and

reliability. It can even be done on a manufacturer basis.

Behind each performance summary is naturally a number of layers of information that can be

viewed and interpreted.

The bottom line is a drastic increase in transparency on individual analyzer and plant

performance.

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TYPICAL PETROCHEMICAL PLANT

PERFORMANCE SUMMARY REPORT

Report period

Site

Plant

Unit

01-Aug-09 01-Sep-09

<Site>

<Plant>

<All>

Performance Summary # %

Total number of measurements

Measurements validated

Total number of accepted results

Average accepted results per measurement

Measurements used for performance calculation

Success rate

381

376

351

0.9

362

313

98.7

100%

86.5%

Breakdown Summary # %

Measurements used for performance calculation

No results

Insufficient results

Below expectations

Adequate

Tighten control

Minor problems

Above expectation

362

5

3

41

32

33

27

221

100%

1.4%

0.8%

11.3%

8.8%

9.1%

7.5%

61.0%

FIGURE 5. PERFORMANCE SUMMARY REPORT

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CONCLUSION

Judgment of the analyzer performance is not an easy task and when the accuracy checking

method is not well developed or procedures are not followed in detail it may happen that the

analyzer will be blamed for inaccurate operation. Therefore means has to be provided to detect

these situations as early as possible.

The normal scatter in results and the variation of the drift over time makes it necessary to use

and interpret validations over longer periods of time. This is why statistical control needs to be

applied in the validation process.

Tools, like AMADAS, will assist to eliminate sources of potential error which otherwise will not

be noticed by showing the relation between analytical and maintenance data.

It has been shown at various installations that the application of automated data collection

systems have lead to a better performance and a more efficient use of maintenance manpower.

However, it has been shown that a high degree in automation of the data generation and data

entry is required to obtain sufficient cooperation of all parties involved and so to make the

application a success.

AMADAS will not only assist to avoid unnecessary calibration (adjustments), but will also make

unnecessary time delays visible and so preventing repetition of it in future.

Summary of the improvements [7] by application of an AMADAS system:

The obtained accuracy of the analyzer result is expected to increase. In some cases

improvements by a factor 2 are not unusual. When this is taken into account for the

‘critical analyzers’, as not only applied for QMI release purposes, the yield may become

enormous when operating a process plant unit more accurately to specs. less ‘GIVE

AWAY’

Reduction of man-hours used for checking of the analyzers. The reduction is caused by

Automatic Validation, most efficient when ‘reference method’ is applied. In addition,

reduction of man-hours is achieved by minimizing unnecessary validation and calibration

due to the consistent use of control charts and statistical methods. it is estimated that

the reduction of man-hours will be between 10 to 50%

Validation initiated by the operator to check whether an analyzer is still performing

accurately without the need to call-in an analyzer technician

Availability and reproducibility rates of the analyzers will improve. at locations where

analyzer management systems are used, it was shown that figures can be obtained above

95%

The historical performance interpretation of AMADAS is capable to early notify the

presence of a deteriorated performance It has been estimated that this will increase the

QMI-release rates of blends by as much as 20%, especially during the first years of

operation.

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REFERENCES

1. Duysings, “Description of the Analyzer Management And Data Acquisition System”, MF

93-1410, Shell International Petroleum Maatschappij, April, 1994.

2. ASTM D3764, “Standard Practices for Validation of Process Stream

Analyzers”.

3. ASTM D6299, “Applying Statistical Quality Assurance Techniques to evaluate

Analytical Measurement System Performance”.

4. SMS 1006, “Guidelines for Statistical Control of Test Methods”.

5. Cusell, “Analyzer Management And Data Acquisition System”, OP 97-30425, Shell

International Oil Products, October, 1997.

6. IP 340, “Code of practice for calibrating and checking process analyzers”

7. Horst, Dick, “AMADAS Benefits”, Hamburg, Germany, November, 2000.