Motivation for CIPAC
In recent years, the importance of safety and efficiency in process
operations management has been well recognized and emphasized by
industry. The discrete parts manufacturing sector in the U.S., under
severe competition from Japan, was first in recognizing the importance
of computer integrated manufacturing paradigms. The process industry
has since them followed suit, stressing the necessity of such
approaches for batch and continuous processes. Nonetheless, from a
technical perspective, most of the progress in CIM has occurred in the
domain of discrete parts manufacturing, with comparatively very little
headway made in batch and continuous process settings. The vital need
and demand for the development of a CIM-like framework for process
operations, the absence of any systematic attempts to address such a
need, and the presence in our School of a strong faculty group with
expertise and interests in this domain combined to create an unusual
opportunity for us to make a leading contribution to this field. To
that end, we have formed the Computer Integrated Process
Operations Consortium (CIPAC), an industry-university research
consortium involving the Purdue University School of Chemical
Engineering.
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Themes and Methodology of CIPAC
The goal of CIPAC is to investigate integrated approaches to
process operations management in order to improve safety, efficiency,
quality, flexibility, responsiveness, and the overall competitive
posture of batch and continuous process industries. The underlying
theme of CIPAC in addressing these problems is two-fold
integration - the integration of operations tasks and
the integration of solution technologies. The spectrum of
tasks in process operations and their interdependencies are shown in
the figure. Task integration involves addressing process monitoring,
regulatory control, fault diagnosis, optimization and supervisory
control, scheduling, and planning in a unified framework. Solution
integration involves the synthesis of an effective solution strategy
from different techniques such as knowledge-based systems, neural
networks, mathematical programming, statistical methods, and nonlinear
modeling.
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