Streaming Data and Event Infrastructure - OSIsoft

Streaming Data and Event Infrastructure
by Alcoa’s Smelter Control System to either increase or decrease load. An increase load signal
means that the smelter is consuming excess MW’s from Midwest ISO’s system which also
results in an increase in aluminum production. A load decrease signal results in a drop in
aluminum production thereby diverting some of the Warrick Power Plant’s electricity into the
Midwest ISO system to make up load demand of the grid. Either way, Alcoa wins.
With common data, Alcoa talent across the globe will engage in collective innovation and the
pursuit and sharing of best practices. In order to focus Alcoa Talent on management of the
plant, measurement data must meet the following criteria: Accurate, Real-time, Correlated and
Historized. Sharing a common language with other plants will allow Alcoa to truly realize the
Alcoa Advantage. A discovery in one plant can result in procedure, training, and policy changes
in the other plants. SMART measures will become common measures. They will allow Alcoa to
share best practices from one plant to another.
IBM
IBM Burlington is a large semiconductor manufacturing site which consumes 3.2 million gallons
of water PER DAY and 446 million kilowatt hours of electricity annually. The sites potable water
is supplied from the Champlain Water District. This potable water must be purified to meet the
strict Ultra Pure Water (UPW) quality requirements needed for semiconductor manufacturing.
UPW is 10 million times cleaner than the raw potable water. By using the PI System, IBM has
achieved over $10M in annual savings, reduced water usage by 27%, reduced operating costs,
and minimizing environmental impact while increasing manufacturing capability by 30%.
The IBM Corporation is using Advanced Water Management techniques to drive cost
improvements required to compete in a global market. These techniques are applied to Ultra
Pure Water systems, heating and cooling waters, potable, non-potable and waste water
treatment in semiconductor manufacturing. Water systems are closely monitored and data is
collected and analyzed to support the decisions necessary to achieve IBM’s desired water
management goals. These goals include strict control of water quality, reduction in water
consumption, reduction in energy consumption and overall lower cost of operation. The IBM
plant has achieved a 27% reduction in water usage, $3M in annual savings while manufacturing
output increased by more than 30%. IBM’s data driven water management techniques have
wider application that can be applied to address water issues for water stewards, distributors,
industry and consumers.
Water management challenges are largely a data management challenges. From the water
resource all the way through to the water consumer, to the wastewater treatment operations
and back to the resource there exists vast amounts of water data: usage, sustainability, quality,
pricing etc. Organizing the data, analyzing the data and presenting the results in easily
accessible formats transforms the data into manageable pieces of usable information. This
information is the basis for sound decision making around water issues.
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