improve safety and productivity, using wireless solutions in ammonia plants

IMPROVE SAFETY AND PRODUCTIVITY

USING WIRELESS SOLUTIONS IN AMMONIA PLANTS

In a fast paced multi-variable environment where decisions are required on a daily basis, the

information management systems available within an organization forms a core part in ensuring that

decisions are based on current, accurate and reliable information. Technology and software now

gives us the ability to automate data manipulation for increased efficiency. How we integrate these

applications into our existing systems and utilize their functions will play a significant role in

determining how efficient we are in today’s industrial climate.

Treveno Stenn Mowassie

Yara Trinidad Limited

Kamla Balgobin


Kelvin Ramoutar


1.0 Introduction

roper analysis of meaningful data is the

key criteria to ensure the outcome of any

sound decision. In an ever-changing

multi-variable environment where decisions are

required to be made on a continuous basis, the

acquisition and analysis of data is critical in

ensuring that the decision making process is

informed and that decisions will result in the

desired outcome.

Process plant operations is one such field in

which the dynamics of multi-variable systems

continuously demand informed decisions at

many levels in the organisation. As a result a

key part of successful plant operations lies in the

ability to reliably gather, historically store and

analyse different types of data on a real time

basis.

Traditionally data acquisition has been done

using paper log sheets and manual spreadsheets,

which focuses more on documentation. As a

result, the exercise of taking plant readings has

become routine, to the point where the true

benefit of the exercise is lost. The ability to

extract meaningful data from this type of

manual system is arduous, labour intensive and

vastly inefficient. In addition, the filing of paper

based systems makes acquiring historical data

very difficult while long-term performance,

acquired learning, and trends are lost over time.

In a sector that demands efficiency, this type of

data management system cannot support the

demands of a fast-paced and decisive

environment.

It is with this in mind that Yara Trinidad

Limited embarked in a change process to reform

its process data management system.

Incorporated in this change were not only

operations field data but also all other data that

is used for operational decision making.

P

323 AMMONIA TECHNICAL MANUAL2008

The final intention is to put meaningful data into

the line of sight of those who could effect

immediate change. In addition, all data would

be available for viewing and analysis by the

respective engineers, lab personnel and even

management. However, the collection and

storage of data is just the tip of the iceberg.

Automated reporting and real time asset

monitoring has also been developed. Equipment

performance can be evaluated automatically in

real time every time a new set of data is

uploaded. The results are then stored creating a

historical trend of the performance of assets

with a frequency never before possible.

This article will detail some of the various uses

of an electronic data acquisition and

management system and lessons learned during

its installation and commissioning on the Yara

Trinidad Site.

1.0 Data Sources

The main sources of data used for data

collection on the site are:

1. DCS Data – Data acquired from the

DCS systems on each of the three (3)

ammonia plants on the site (Yara Plant,

Tringen 1 Plant and Tringen 2 Plant).

The Yara and Tringen 1 plants both have

Bailey Infi 90 DCS systems. When the

project was initiated, Tringen 2 plant had

ProVox DCS systems. Long term DCS

data was generally stored in the form of

optical hard discs. Therefore the only

readily available DCS data was from the

local DCS console and stored in history

for 2 weeks. If data further back in time

was required, it would involve

requesting assistance from a trained

administrator and even possibly

requiring previous optical discs.

Exporting the data into an editable

format for further use was only available

through a system administrator

2. Field Data – All field data was collected

in log-sheet format. Each plant would

have a routine of 2 readings sets per shift

and 3 shifts per day. Completed log-

sheets were filed after use. Field data

history was only available by manually

extracting readings from a number of

past log-sheets before any manipulation

was possible. The time taken and

resources required to conduct this

exercise made extraction from the log-

sheets impractical.

3. Laboratory Data – Lab data was stored

in an Excel Spreadsheet and updated in

this format. Lab samples for all three

plants were stored in this format.

Laboratory data, though stored in an

electronic form, was not the most user-

friendly because multiple sheets and

files existed for various plant and

samples.

2.0 Data Management System Upgrade

In light of the deficiencies that existed with the

data management system, the proposed upgrade

Figure 1 – Typical Log-sheet used to take Field Manual Reading

C O M P

S P E E DP R O C E S S G A S

I T E M : C O M P S U C T I O N C O M P D I S C H A R G E S T E A M T O T U R B I N E L .O . F i l t e r V A C C O N 9 5 0 C

S p e e d P r e s s T e m p T e m p P r e s s . F lo w P r e s s D i f f . P r e s s . P r e s s

I N S T R : S I - 3 9 8 2 P I - 3 2 3 T I - 5 1 5 T I - 5 1 7 P I - 3 2 4 F I - 2 0 4 P I - 3 1 4 P I - 1 4 6 5

U N I T : R P M P S I G º F º F P S I G L B /H R P S I G P S I I n H g

N O R M A L : 1 1 5 0 0 1 8 5 9 0 2 9 0 4 6 5 1 6 7 ,0 0 0 4 4 0 1 2 - 2 4

1 0 :0 0 H R

2 : 0 0 H R

0 6 :0 0 H R

1 0 :0 0 H R

2 : 0 0 H R

C -9 0 1 J F E E D G A S C O M P R E S S O R & C -9 0 2 J P R O C E S S A IR C O M P R E S S O R F IE L D

L O G S H E E T 1 O F 5

C T - 9 0 1 J T U R B I N E S T E A M &

C O N D E N S A T E S Y S T E M

324AMMONIA TECHNICAL MANUAL 2008

involved revamping of the data collection

mechanisms to achieve the following:

1. Storage of all acquired data in an

electronic format that is easily

transferable

2. Accessibility of the data from remote

locations (e.g. desktop computers)

3. Ability to integrate all information into

one platform

4. Ability to use all information in

automated reports.

2.1 Upgrade of Field Log-Sheets

As a solution to the manual field log-sheets,

Yara Trinidad Limited implemented a wireless

data gathering system in all three ammonia

facilities to replace the paper-based system. All

plant readings are now taken using mobile,

wireless, personal data loggers as known as

Personal Digital Assistants (PDAs). These data

loggers fully replace the manual log-sheet and

can manage large amounts of data in an

electronic format.

Hardware

The handheld PDA device is intrinsically safe

and is a Class 1 Division II pocket PC-based

device that has Radio Frequency Identification

(RFID) capability. Radio Frequency

Identification (RFID) is an automatic

identification method that relies on remotely

retrieving data from various RFID tags, each

one unique to a particular data point. Using the

scanner on the handheld PDA, the RFID tag for

the point can be scanned and all the necessary

information about the tag is displayed on the

PDA. When the RFID tag is scanned, location,

tag ID, previous/historical data, design values,

alarm values all are displayed. The operator is

now equipped with all the related information to

ensure that the reading being taken is consistent

with design values, and to determine if a

deteriorating condition exists.

Figure 2 – PDA Handheld Device

Audible alarms also exist for each of the reading

points. An audible alarm will activate if the

reading taken in not within the desired range.

This feature now provides a mechanism for

alerting the operator of a deteriorating condition


Figure 3 – Snapshot of PDA Display

Figure 4 – Snapshot of PDA Trend


on each reading point on the plant. As these

points may not exist on the DCS, the alarm

generates an additional layer of reliability. With

the previous log-sheet system, a deteriorating

condition could have been easily missed since

no trigger would be activated. This provides

increased reliability and the potential for

increased uptime.

Once the field data has been taken the readings

are uploaded to a secure database (either web-

based or local server based on company policy)

via a synchronization cradle after which the data

can be extracted and manipulated. At Yara

Trinidad Limited the data is stored in a local

server and available for any desktop PC user in

the organisation. The ability to trend, extract

and manipulate data from the server is provided

via the PlantMS application provided and

installed by Fitiri.

The fact that the data is available from any local

PC on the system network makes the

information available to all levels in the

organisation. In addition it provides the

flexibility required for integration with other

systems, which will be discussed later in this

article.

The PDA data acquisition system ensures:

1. Operator Interface with Tagged Devices

The RFID tag ensures that the tagged

device is scanned in the field before

accepting a data entry against the tag.

2. Immediate Alert to Alarm Conditions—

The alarm ensures that every reading

point has alarms associated with them to

catch any deteriorating conditions even

those that do not generally come back to

the DCS e.g. Lube Oil/ Seal Oil Filter

DP

3. Real Time Data is available which is

critical for troubleshooting

4. Historical Data is immediately available

for troubleshooting

5. Data Availability - Integration with

other systems is possible

2.2 Upgrade of Laboratory Data

Laboratory data was traditionally stored either

in manual log-sheets or as manual entries in

Microsoft Excel spreadsheets. However, these

methods did not provide the historical data in

easy-to-use format , nor did it provide the

flexibility of making integrating with other

software possible.

These factors led to the installation of a

laboratory-dedicated software program

LabEntry with mechanisms for storage of mass

amounts of historical data, ease of use, ease of

manipulation and the ability to integrate with

other software programs to make the data

available for automatic reporting.

Imagine having the ability to know when your

boiler feedwater co-ordination is out of

specification the instant that the results are

available, and also utilize DCS data (e.g. boiler

blowdown, cycles) to calculate automatically

the required adjustment to bring the feedwater

back into the desired range. Trend data that is

available at the click of a button can show the

progression of any variable, e.g., boiler pH and

PO4, lube oil moisture on any rotating

equipment being tested, CH4 slip ex Primary

and Secondary Reformers, CO slip ex HTS and

LTS, synloop N2/H2 ratio, CO2 absorbent

chemistry. The list goes on to include all the

recorded sample points.

Yara Trinidad Limited has also embarked in a

joint project with Johnson Matthey Catalyst to

automate the generation of catalyst performance

reports. These reports are fed information by

DCS data, field PDA data and laboratory data

and using the proprietary Catper®

software,

automatic catalyst performance reports are

generated.


Figure 5 – Snapshot of LabEntry Screen (HP

Boiler)

2.3 Upgrade of DCS Historical Data

The availability of historical DCS data has been

a major limitation in the troubleshooting of

imminent issues that may come up on a daily

basis. In most troubleshooting exercises the

availability of accurate historical data is key in

determining a solution for the given problem.

With most DCS systems, the required data is

available only from the DCS consoles or from

an engineering workstation. In a fast paced

environment, the DCS does not provide the

required platform to make the assimilation and

processing of critical data efficient. In addition,

the ability to export this data into a format that

is editable is at best tedious and consumes

valued time and resources.

To satisfy the demand for this type of

efficiency, Yara Trinidad Limited has installed

Aspen InfoPlus21.

Aspen InfoPlus21 works on the following

principle:

1. Using an open source software application

(OPC interface), the data from the DCS

highway is sampled on a periodic basis. The

period of the sample can be individualized

for each DCS point, thereby providing more

frequent sample times for DCS points that

can change quickly (e.g. compressor speeds,

pressures) or less frequently for others. Each

selected point on the DCS has a configured

tag in Aspen InfoPlus21. The data sampled

from the DCS is stored in the respective tag

in Aspen InfoPlus21 and is historized. The

number of tags can vary depending on the

extent of data required to be stored. The

sampled data is stored in a data historian

(usually a dedicated server).

2. This data is then transferred to a dedicated

secured server. This may be on an off-plant

area. The server is currently hosted by the IT

department in Yara Trinidad Limited (YTL).

This does not in any way affect the function

of the DCS nor does it stop or replace any

DCS mechanisms for data historization.

3. From the secure server this data can be

accessed in read-only format from any local

computer on the system network. In essence,

this means that anyone with a PC (desktop

or otherwise) who has access to the network

can access real time DCS data. The

possibilities from this point are endless. It

must be noted that the data traffic occurs in

one direction only. Data can only be read

from the DCS highway. No information can

be written to the highway using Aspen

InfoPlus21.

Located in Plant

DCS Information Highway

Located in IT Throughout Organization

Aspen Data Collector

Firewall

Network PC

Network PC

Network PC

Network PC

OPC Server Secure IT

Server

Figure 6: Structure of data path using InfoPlus 21


Aspen InfoPlus21 offers trending capabilities of

all stored tags with minimal effort from any PC

on the network. Multiple persons can access and

analyse data simultaneously from different

locations, both on and off-site. This concept can

be taken even further: data can be viewed online

by experts in specialized fields to troubleshoot

problems. However, trending capability is just

scratching the surface of possibilities: the

“Integration” section gives more options.

Graphics development is also a tool being

developed by YTL. The intention is to replicate

the DCS graphics for each plant on the Aspen

InfoPlus21 system and therefore be able to see

process parameters change on a real time basis

(and historically) on the graphics. Therefore,

from a PC you can see what the DCS operator is

seeing, or you can visualise historical actions on

a DCS graphic. This is a tremendous tool for

troubleshooting and training.

The possibilities are endless and the

customisable format of the data allows the

flexibility to produce reports for specific needs.

Figure 7: Tringen2 Plant Steam Letdown Graphic replicated in Aspen InfoPlus21


2.4 Integration

Now that all the essential data is being collected

and historically stored in an easily retrievable

format, manipulation and use of the data is

required to generate the results required for

effective operations.

Once the databases have been established

queries can be made to the PDA, LabEntry and

Aspen InfoPlus21 and the data is extracted from

the databases onto a single common platform.

All historical data can be extracted from the

server database into third party software like

Microsoft Excel ®

or any other data management

software. The window to automated reporting

just became wide open. Using the two programs

above, data can be extracted and aggregated in

any way possible over any required time frame.

Reports can be generated to automatically

monitor plant performance and asset

performance on a real time basis, hourly basis,

shift basis, daily basis, monthly basis or even

yearly basis. Any frequency required can be

automatically generated.

Yara Trinidad Limited has developed automated

reporting using this system for the generation of:

1. Key Performance Indicators of plant

operability on a shift basis.

2. Real Time Operations Reports geared to

give operating personnel key

information on plant performance

3. Energy Monitoring on a real time and

shift basis

4. Plant and Site Daily Key Figures

Reports

5. Plant and Site Monthly Key Figures

Reports

6. Equipment/Asset Performance on a real

time basis

7. Catalyst Performance

8. Graphic development provides the

ability to see DCS graphics from a

desktop computer.

Automated asset and equipment performance

calculations continue to be generated so that the

performance of the asset can be tracked on a real

time basis. Traditionally equipment performance

was evaluated on a quarterly, bi-annually or

annual basis. Now with the advent of

automation, the performance calculations can be

done as frequent as a reading set is taken.

Compressor performance can be tracked on a

daily basis or better and the results are then

stored historically. Exchanger heat duties and

heat transfer coefficients all tracked as frequent

as operators take readings. Over time we will be

able to pull up trends on polytropic efficiency,

isentropic efficiency and heat transfer

coefficients rather than the process parameters

that imply performance (e.g. temperature and

pressure) around the equipment. Trends,

calculations and results are being continuously

generated automatically in the background while

“routine” readings are being taken. This gives

the ability to make meaningful decisions about

equipment performance based on calculated

parameters rather than implied process

parameters.

The automation also brings consistency in the

manner in which the calculations are being

done. The frequency of data gathering also

allows step changes in the performance of the

assets to be highlighted almost immediately,

which significantly assists in the troubleshooting

exercise and determining the root cause of the

event. A step change in performance that is

recognised further down the road (e.g. by

quarterly surveys) only leads to speculation into

the root cause as the defining moment where the

change occurred cannot be determined. Possible

integration with SAP is also being explored.


Figure 8 – Integration of Data at Yara Trinidad Limited (Key – KPI – Key Performance Indicator)

3.0 Conclusion

With competitive markets and increasing global

prices for raw materials it is critical that all

measures be taken to increase efficiency in

today’s industrial climate. This increase in

efficiency can only be attained if the necessary

information is at our fingertips to ensure that our

decisions are made from real-time reliable

information. The mechanisms that bring the

required information together must be structured

in such a way that allows consistency, ease of

use, integration and productivity.

Modern mechanisms for data acquisition,

storage and handling is critical in ensuring that

we are spending our time where it matters - on

data analysis rather than data collection.

4.0 References

1. Tech Houston Section Week Two of

October 14-20,2005 – Ashe Menon,

FITIRI Inc., Houston, TX – Mobile

workforce automation solutions

changing data management in daily

business operations.

2. http://www.fitiri.com/Products.html

3. http://en.wikipedia.org/wiki/RFID

4. Houston Business Journal- Ashe Menon

– Friday Dec2nd 2005 – Tracking assets

through life cycles saves time, avoids

duplication.
