Avanced process control enteprise management system

Advanced Process Control Enterprise Management
System
1. Gyan Prakash,

2.Mr.P.T.Shivasankr

(B.E, Third Year)
Associate Professor
Department of computer science Engineering
Aarupadai Veedu institute of Technology ,
OMR, Paiyanoor ,Chennai, Tamilnadu (India)
1.prakashgyan90@yahoo.co

2sivasankar@avit.ac.in

ABSTRACT
In the evolution of digital control systems, inherent processing capability has skyrocketed in the last
decade; this is due to the availability of the same powerful processors, used in home/office
computers, used in these control systems. The processes being controlled are typically field device
limited in terms of speed of response; today’s processors can provide logic control, I/O scanning and
other main functions far faster than the field can respond.
This “latent” excess processing capacity within the digital control systems presents opportunities to
take on new tasks that now reside in servers or other stand-alone processing devices. Plant
Performance monitoring, maintenance monitoring, and other asset management functions can now
reside fully within the same hardware platform as that being used for actual process control. There
is already a body of work within academia that identifies a similar trend (peer-to-peer or grid
computing) in latent/underutilized processor capacity within networks that can be harnessed for
solving massively complex analysis in many research applications an within various industries. The
use of the latent capacity of the DCS in some aspects mirrors the approach within the academic
studies.
The author show why and how the plant DCS can and should be looked at no longer as simply the
implementation of process control logic (optimized by another stand-alone device), but also as the decisionmaking tools (i.e. software tools) that enhance plant performance, unit dispatch, and other functions
that can and should be embedded in the control system itself. This way, operational decision making
(i.e. business decision making) can become far more real time and far more meaningful with the
ability
to
instantly
understand
the
commercial
(read
profitability)
impact.
INTRODUCTION
Digital control systems have evolved significantly since the mid 1970s, when some of the first distributed
controls systems began to be installed in industry. The capabilities of these systems have been largely
defined (enhanced in some cases, restricted in others) by the processors that have been available and
utilized.
Early control systems, due to memory and computational limitations, often had multiple processors within
a ‘controller chassis’. Each processor was dedicated to a specific function (i.e. I/O communication;
floating point calculations; digital logic processing; communication link processing and others).
As processor technology evolved, the sophistication and computational capabilities of the systems evolved
as well. Controller chassis were ‘slimmed down’ from six, seven, eight or more boards to
Three or four. Performance improvements in terms of logic execution speed, speed of communication,
status updates, and quality indicators became possible, and began to approach near real-time. Operators
became confident that the plant DCS gave them the same (and more) touch and feel of the plant as they
had been used to in the pushbutton/MA station days. Other industries adopted the use of embedded

processors for specific control functions. From microwave ovens to automobiles, the proliferation of
processors in aspects of daily life mushroomed. This allowed control systems suppliers and others needing
Programmed control functions to ‘borrow’ from technologies that were also proliferating in the home and
office PC world and embed them into the industrial world for use in a wide gamut of controls
applications. Since industrial usage involves other design aspects that are more costly to modify or change,
the pace of use of these industrial class processors has typically been slightly behind the adoption of new
processor technology in home/office PCs. These trends of ever-more powerful processor capabilities and
the demand/need for plant performance improvements have continued to feed into each other. Today,
suppliers of digital control systems have a wide range of state-of-the-art processor technologies to choose
from and performance and capabilities of DCS are far greater than ever imagined. Whether embedded
386s or Pentiums (including Pentium IVs and others), control system suppliers are rapidly catching up
with the use of processors being offered in the home/office environment. Whereas five years ago, the
controls industry might be one or two generations of processors behind, today the industry offers nearly
the same processors as found on many home/office machines.
This ‘bulking up’ of control processor technology has led to a strange anomaly: underutilization of the
processor capabilities. The controls industry struggled for years to add valuable features and provide
certain performance levels (i.e. one second CRT updates; high speed communication link throughput) so
that operators could be in touch real time with the process. As processors became more powerful, this goal
became increasingly easier to achieve.
While processor speeds and capabilities have dramatically changed, field responses haven’t changed, and
due to physical plant limitations, can’t change: valves, motors, diverter gates, and the various controlled
physical processes have defined limits in terms of speed of operation. Logic can be resolved 10, 100, 1000
times per second; the field can only respond as fast as the physical limitations.
Hence, today’s systems have significant overcapacity in terms of processor load ‘memory’ is often
bundled with the processor or is easily added in circuit design, there is more than sufficient memory
capacity for complex software to co-exist in the same environment .Against the backdrop of ever-more
capable platforms is the reality of the pressure of competition faced by the plants using these systems.
Whether in process industries or power generation, decisions that affect the bottom line must be made
daily, hourly, minute-by-minute. All too often these decisions are made based on trends, averages, last
week’s data, or last month’s data. Under pressure of global competition, plants need to know immediately
the impact of decision-making: what is the cost of deferred maintenance; what is the cost of using a
different blend of coal; what is the cost of using a higher level of ammonia in an SCR; what is the
opportunity cost of making the next mega-watt from the gas unit versus the oil unit against a backdrop of
ambient temperature, cost of fuel, ramp rate, etc. A real-time informed decision can mean incremental
changes in the profit profile that add up to real money.
security issues that plague the ‘front office’ end of
PLANT AND RESOURCE SCARCITY AND the business.
DIVERSION
The burden of security is adding costs to the price
With the adoption of open architecture in control of process products (cement, steel, water, power,
systems, instrumentation, analyzers, etc., the same others). This is straight overhead cost with no
issues of denial of services (viruses, hacking) and matching revenue to offset, no value-added (in
system security and integrity that plague corporate terms of the end user’s additional value). Plants
America, are increasingly becoming problematic at already under extreme pressure due to a wide
the lowly control system level. As transparency variety of competitive issues (outsourcing, global
between systems and software forges ahead, the competition, deregulation--the list is nearly endless)
systems running the process or power plant become are trimming staff. Often the staff that are
increasingly vulnerable to the same types of IT ‘trimmed’ are the most knowledgeable about the
plant process operations (senior staff being reduced

for cost savings). The IC suppliers (whether control
system hardware, analyzer hardware, software
packages) are attempting to plug the knowledge
gap’ through more sophisticated control system
offerings, bundling more and more intelligence into
the platform. Given the continued desire for open
architecture, the increasing reliance on software
solutions running on commonly available
platforms, there is a collision of interests. The same
systems that are meant to plug the knowledge gap
(and meant to reduce operating costs to enhance
profitability) are in fact costing huge amounts of
money to protect. Process-area staff reductions are
being traded off for IT staff (or consultants)
increases. Plant staffs normally focused on refining
and improving operations are increasingly sidelined
to address system security issues.
CURRENT REALITY
While there exist myriad front office tools to
evaluate plant performance, to optimize plant
maintenance, to make other kinds of business
decisions, the implementation at the control system
level remains the “unabridged” gap. How are the
desires of the business communicated to the
functions of the control system? Bits and pieces of
hardware and software exist for highly specialized
functions neural networks do various functions to
optimize NOx emissions or help identify
maintenance trends. Advanced analyzers provide
real time data on coal quality;. Instruments can
report not only field values, but also report on the
quality of data being provided. The control systems
execute control logic along exceptionally tight and
fast parameters.
The pieces are all doing ‘things’ faster, better,
smarter. The problem remains that the
implementation of all this equipment, coupled with
the issues of system integrity, starts to blur the real
end goal better: cheaper, higher quality product.
The system ‘design’ and ‘maintenance’ start to
become their end goals. End users must sift through
all the claims of capabilities on each individual
piece (whether instrument, control system, analyzer,
software package), trying to ascertain which really
‘plugs and plays’, which piece really satisfies the
need, which piece will actually do what it says it
will do. And if that process at the plant can be

successful, does it really plug into the corporate
decision making tools and databases? In too many
the example in Fig. 1 below shows a typical current
approach. A server or other dedicated PC is
Connected to a data highway to run the plant asset
management or plant performance improvement
software. More times than not, a separate database
must be developed within each device to allow the
software to perform. Data values have to be
imported from some type of archiving software,
which in turn usually has its own database. The
archiving software and the plant performance/asset
management can co-exist in one server but still are
often configured as separate ‘service’.

Typical DCS Configuration Fig. 1
The various subsystems initiate requests and obtain
responses based on various parameters and
operations inquiries.
The issue of maintaining various stand-alone
servers, the network connections, and the separate
databases, creates substantial overhead costs to
plants in terms of long term maintenance. Each
‘system’ has its own unique programming tools,
unique database requirements and other unique
features. On top of that, there must be developed
and maintained a complex IT security strategy. The
complexity often defeats the end user: Many of
these “bolt-on” systems, initially touted with such
fanfare, end up being unused or underused because
of the burdensome maintenance issues. Any new
reports, new analyses that Operations may
want/need
(beyond
those
configured
at
initialization), amount to a major IT request and get
queued up, perhaps never to see the light of day.
Meanwhile, the plant control system, with a host of
state-of-the-art CPUs and significant memory

capacity, is relegated to the more mundane tasks of
reading/writing to I/O and running the logic
algorithms. With today’s processors, this is the
equivalent of the plant control system idling much
of the time! The question begging to be asked is if
there is such a reserve of computing power and
memory within the typical DCS, why it isn’t being
tapped. If it can be tapped, how can it most
effectively be deployed?
A NEW REALITY
The computing power and access to huge amounts
of memory (for data storage) inherent in today’s
digital control systems, it maximize this largely
untapped, ’latent” power achieve to plant goal:
make electricity better, cheaper, faster. Any Plant
owners and operators must be able to tap instantly
the real time data and quickly be able to analyze
various ‘what if’ scenarios for real-time decisionmaking. In addition, they must have confidence that
the bridge to the control system, where the
decision-making is given implemented, is seamless
and that the control system will in fact be
sufficiently reliable to execute the decisions.
If a systems supplier can harness a Pentium IV
grade controller and supply 2 Gigabytes of flash
memory for less than 1/10th the cost of yesteryear’s
mega-controller chassis, why not use such
technology to augment the total plant decisionmaking capacity? Today, plant maintenance
software, plant performance monitoring software,
and data archiving all are running in various standalone servers, with software/interfaces, etc. that
preclude seamless connectivity without substantial
effort. Getting that data to the next higher level
(corporate) for the ‘global’ business decisions also
comes at a substantial effort to make connectivity
work. And both of these, given the issues of
vulnerability to open architecture, create huge IT
overhead burdens to plant operating costs.
In benchmark tests, logic execution rates of 10
times/second far and away exceed the ability of the
plant physical dynamics to respond. While the
newer processors also allow for even greater
resolution of data in floating point calculations,
instrument transmitters are likely more limited (12
or 16 bit transmitters vs. 64 bit processors) than the
processor. The computing power of the current and
future processors, using the historic approaches of

system deployment isn’t being turned into truly
meaningful
improvements
in
performance.
Performance enhancement applications are running
in yet another layer of computational equipment.
Rather than stand-alone islands of automation, the
computing power of today’s digital control systems
should be the engine of significant amounts of
analysis and decision-making. The potential results
in plant operation and profitability should be
instantly and obviously available to all.
A ROAD MAP
Sometimes everything old is new again, but not
quite the same. Aspects of proprietary systems can
be utilized to assure the system integrity and also to
enhance the business decision making and simplify
life at the plant level. An approach suggested (and
being followed by at least one major systems
vendor) is to create additional nodes within the
control system environment that are essentially
embedded servers, whether for advanced
computation, data storage, operator HMIs,
engineering workstations, etc. Operating within the
DCS environment, sharing the same database and
programming tools, these nodes can run
sophisticated software services (plant asset
management, performance monitoring, and neural
network applications) yet create a more seamless
approach to IT maintenance and can even obviate
some of the security concerns.
These approaches reduce the burden on plant staff
from a maintenance perspective. Rather than having
to be IT experts trying to weld the unwieldy
disparate
“bolt-on”
systems,
software,
servers(having to constantly battle ant-virus
upgrades that aren’t compatible across-theenterprise despite claims to the contrary), all bolt-on
software runs under one platform. Security becomes
a non-issue if certain proprietary features are
maintained.
More importantly, with better integration of the
various analytic software under one platform, better
decision-making will result. An umbrella enterprise
management software package can weld together
with all the disparate pieces of stand-alone
software, running under the digital control system
platform.
Figure 2 below shows a suggested architecture for
this new approach.

The Cyber Secure Zone
Fig. 2
This presents a totally integrated approach. All
optimizing software (whether plant operations
improvement in any function, or asset management
or other usage) is embedded directly in the
controller(s) of the DCS. The utilization of
processor capacity and capacity limitations do need
to be observed but is far less a consideration given
the capabilities of Pentium™ ‘M’ class processors
or other similar class processors now able to be
incorporated into DCS systems. Rather than simply
idling 80%+ of the time, the processors can be more
fully utilized running these specialty software
packages.
Many benefits accrue from this approach:
• Unified database. Rather than maintaining
separate databases, one common database is the
source of archiving, NOx optimization, asset
management. One change updates all software;
there’s no need to keep track of whether multiple
databases are updated and current.
• Simplified hardware maintenance and reduced
investment. No longer is there a need to
Consider the IT security issues on disparate
platforms. Use of flash memory reduces the concern
of hard disk failure.
• Control room clutter (or wherever one decides to
store the servers) is reduced.
• Issues of communication interfaces (is anything
bought from multiple vendors really ‘plug-and
play’?) disappear. In this approach, plants can

analyze a host of ‘what if’ scenarios and calculate
the instant cost impact.
No longer dealing in the mostly abstract, a decision
to boost mega-watt production can be analyzed in a
number of alternate scenarios: blended versus high
quality coal; offsets of additional ammonia; cost of
added soot-blowing; efficiency improvements in
gas turbine versus oil-fired conventional unit. With
a few clicks, a decision maker understands the
bottom line impact of virtually any action, whether
that is deferred maintenance, boiler tube impact,
water quality, or fuel switching. The list is endless.
Consider a typical intervention technique to address
NOx formation. Neural networks are being used in
coal-fired applications to improve burner
performance and minimize NOx formation. This is
typically running on a server (which could be the
historical archiving system or some other platform
using the historical archiving system [HAS]
information). If the neural network is awaiting input
from the HAS, it is then dependent upon the
scanning time/logging time of the HAS, and then
the delay in recalling that information for use.
Building in the I/O scan time, processor scan time
in the DCS, the data to the neural network could be
3, 4, 5 seconds or more; intervention techniques are
then usually behind the curve. If the neural network
knew more about the coal coming in, it could
anticipate the resultant burn and even more
effectively reduce NOx formation. If the on-line
coal analysis information was instantly available,
how much more effective would be the intervention
technique with little or no time delay.
The expected end result of installing such
performance improvement software is to realize a
definable performance improvement in terms of
burner operation and formation of NOx. The net
effect by extension is the expectation that
downstream intervention techniques (SCR size,
ammonia consumption) will be minimized in terms
of size, operating costs. But this is resolving only
one dimension of the plant operations cost and
profitability. The dispatcher and operations staff
don’t necessarily know the real time cost/profit
impact that NOx optimization may cause in related
areas of the plant (tube damage, back pass soot
formation and loss of heat transfer surface, lagging
(with similar results), impact to heat transfer

surfaces due to firing conditions differing from
original design basis (including use of fuels not
necessarily compatible with original design
conditions).
IS IT AVAILABLE?
There are numerous approaches in the market that
couple various aspects of the concept presented but
not in a fully formed solution and not fully bundled
into the control system processors. Most advanced
process solutions that are touted in the trade
journals have bundled lots of hardware and
software and interfaces and while some very
specific results have been obtained (better NO x
numbers, increased cost awareness of soot blowing,
thermal stresses, some/all of which have led to
some improvement in bottom line results), each of
these projects are narrowly focused. Bolting on the
next change, the next improvement project, has to
be treated nearly independently since it will involve
yet another vendor, another platform, another
software package.
A recent survey which highlighted recent initiatives
implementing various schemes to incorporate plant
improvement hardware and software was presented
in the September, 2005, issue of Power magazine
(“Unify the Approach to Process Control and
Automation” by Jason Makansi). The survey shows
many interesting narrow-focused projects with
varying levels of success. What the survey also
illustrates is how disjointed the approach within the
industry is: as many types of software or hardware
for various types of optimization as are being
touted, is only the tip of the number of solution
schemes that have been implemented. And each
implementation requires an entirely different
staging, planning, and integration effort than any
previous similar undertaking at a given plant. The
author submits that this is extraordinarily
burdensome on end users. While there has been
significant effort along the lines of providing
advanced software for improving fossil plant
operations, it may well end up that the nuclear
power industry, despite being generations behind in
digital controls, leads the effort towards integrated
solutions.
Korea, for example, in an effort to provide an
advanced core protection calculator system is
moving ahead with a version based on digital

control platform in which the calculations, rather
than being run on a server or other stand-alone
processor, are actually embedding the C+++ code in
‘macros’ that are executed by the DCS processor,
reading and writing from/to the I/O directly wired
to the DCS.
Other analogies come from the computing world
directly. “In a sense, the idea presented here draws
from the much larger concepts collectively known
as grid computing, peer-to-peer computing, or
distributed. All are devoted in one way or another
to harnessing the computing power and storage
capacity of idle desktop machines.” [Waldrop,
Mitchell M., Technology
May,
2002,
Review
p.31,”GridComputing.”]Substitute idle capacity of
processors in plant DCS systems for desktop
machines and you can visualize how software
modules can be embedded to make the system more
unified and efficient (see references at the end of
this paper for further insights into parallel or grid
computing for use in processor-intensive
applications).
SUMMARY
The dynamics of today’s power generation markets
have changed the manner in which utilities must
perform their business-making decisions.
The financial environment, in which utilities
operate today, demands real-time knowledge of
profit-loss impact for decisions both large and
small. Coupled with that pressure, many vendors
are offering various solutions to resolve one or a
few operational and/or maintenance issues at the
plant level; some vendors offer sophisticated asset
management performance monitoring/enhancement
software solutions. But the integration of these,
along with the financial management tools
necessary to make sound business decisions, has
not yet been approached in a unified manner. The
power of today’s DCS has more than sufficient
resources to accomplish this integration and to
provide the supporting platform.
For all manner of business-decision making. The
benefits in terms of real time quality information
leading to informed decisions and the ability to
enforce those decisions through the plant control
system, will enhance the enterprise’s total financial
performance.

Avanced process control enteprise management system

Avanced process control enteprise management system

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