Process Systems Engineering
Process systems engineering (PSE) is the application of systems
engineering to biophysicochemical processes, where systems
engineering is the methodical approach for the design of systems via
mathematical modelling, data analytics, optimization, and control, and asystem is a set of units that interact. Numerous journal papers
have been published in the last decade in which authors provide various
perspectives on process systems engineering.16 Some
papers argue that the discipline of process systems engineering was
founded in the 1950s, whereas others argue for the 1960s or even later.
The process systems engineering field actually can be traced back to the
1930s to early 1940s, when the use of mathematical modeling to design
controlled process systems was already practiced in the chemical
industry.17 18 1920 21 Process systems engineering
was an active research area by the 1950s, with pioneering work by
Rutherford Aris, Neal Amundson, and Roger Sargent in various types of
chemical reactors and separations.2223 24 2526 27 The use of numerical computing
in process systems engineering, which had already been applied in
industry in the 1950s, became widely studied by the 1960s, with
additional research teams including that of Ernst Gilles, Lowell Koppel,
Leon Lapidus, Dale Rudd, W. Harmon Ray, Larry Evans, Reuel Shinnar, and
Art Westerberg.28 2930 31 3233 34 3536 37 3839 40 4142
The 1970s saw the development of many research groups in process systems
engineering, including by G.V.
Reklaitis, George Stephanopoulos, Iori Hashimoto, John Perkins, Thomas
Edgar, Ignacio Grossmann, Dale Seborg, Michael Doherty, Manfred Morari,
Charles Cutler (at Shell), and Jeffrey Siirola (at Eastman
Chemical).43 44 4546 47 4849 50 Much of the research was in
developing PSE solutions for specific classes of processes. Other
research considered processes more generally, and precise mathematical
formulations were derived for much of the PSE technologies used in
today’s industry, including for model predictive control, data
reconciliation, and process scheduling. Methods were developed for
better handling of practical considerations such as uncertainties and
mixed continuous-discrete operations. At the same time, several research
groups were making an impact outside of the field of chemical
engineering, alongside their contributions within the field. As in
earlier years, many individuals within the PSE community were widely
known outside of PSE, including in reaction engineering and separations.
The 1980s to mid-1990s saw the founding of a large number of PSE
research groups worldwide, with a large proportion focusing their
attention only within the PSE community, with very little interaction
outside of PSE, either within or outside of chemical engineering. A
small proportion became highly engaged outside of PSE, in some cases
becoming very well known outside of PSE, including in the areas of
optimization and control theory and in applications such as to energy
systems, pharmaceutical manufacturing, and biomedical devices.
Individuals at such interfaces included James Rawlings, Lorenz Biegler,
Nikolaos Sahinidis, Babatunde Ogunnaike, and Frank Doyle.
Data analytics, which was an active area of research within a subset of
the PSE community and applied in industry for decades, has become of
increased activity with the meteoric rise (and some rebranding) of
machine learning. Recent years have seen PSE faculty increasingly
collaborating with non-PSE faculty to apply PSE methodologies to their
research problems, which has resulted in an increase in the number of
publications by PSE faculty in highly scientific venues includingNature , Science , Cell , and PNAS . These
collaborations include applications of machine learning, systems
analysis, and process design, operations, and control. The “PSE
community” is largely two distinct groups today, with one group being
very inward-looking and focused on publishing papers in traditional PSE
publication venues, and another group whose primary goal is to make an
impact in other communities. The interactions between the two groups are
rather limited, although some individuals have received some level of
acceptance by both groups. The outward-looking community is likely to
continue to grow, as automation, high-throughput experimentation, sensor
technologies, and computing continue to favor methods that make best use
of these trends.
With the growth of computing and data, process systems engineering
continued to grow in methods, software, and applications to the point of
becoming indispensable in the design and operation of modern chemical
and biotechnological processes. Today software for computer-aided
process systems engineering such as Aspen Plus, gPROMS, DMCplus, and
DeltaV is ubiquitous in the process industries. While systematic methods
for addressing such characteristics as time delays, nonlinearities,
disturbances, and uncertainties have been developed since the early days
of the process systems engineering discipline,51algorithms and software have become increasingly powerful since then,
enabling increasingly complex systems and types of design problems to be
addressed. The engineering designs are only as good as the mathematical
formulation of the process systems engineering problem to be solved, and
chemical engineers need to continue to be trained in the language of
mathematics and in chemical and process systems engineering fundamentals
to be able to address the increasingly complex chemical and biological
systems that arise in today’s and tomorrow’s technological problems.