19-08-2014, 11:17 AM
Advanced Process Control Enterprise Management
System
Advanced Process.pdf (Size: 339.42 KB / Downloads: 6)
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.
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.
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
PLANT AND RESOURCE SCARCITY AND
DIVERSION
With the adoption of open architecture in control
systems, instrumentation, analyzers, etc., the same
issues of denial of services (viruses, hacking) and
system security and integrity that plague corporate
America, are increasingly becoming problematic at
the lowly control system level. As transparency
between systems and software forges ahead, the
systems running the process or power plant become
increasingly vulnerable to the same types of IT
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
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.