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\begin{document}

\title{Whither Chemical Engineering?}



\author[1]{Doraiswami Ramkrishna}%
\author[2]{Richard Braatz}%
\affil[1]{Purdue University}%
\affil[2]{Massachusetts Institute of Technology}%


\vspace{-1em}



  
  \date{May 23, 2024}


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\selectlanguage{english}
\begin{abstract}
The strong core base in chemical engineering during the latter half of
the 20th century enabled chemical engineers to contribute extensively to
many areas outside of the traditional. The depth of such involvement has
led researchers to confront questions much more engaging to the field of
application, thus adopting and cultivating expertise more native to it
than to secure chemical engineering as a discipline. The progress of
knowledge in science and engineering must leave a strong trail of
fundamental understanding through developed methodologies that can
assist in continuing progress. If this tenet is acknowledged, this
article yields considerable scope for discussion on whether chemical
engineering research is continuing to provide for a growing core that
has endowed chemical engineers with the ability to formulate and solve
important societal problems in which material systems undergo changes in
composition and energy. We discuss opportunities for hopefully serving
the issues of concern.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Contemporary research in any branch of engineering is seen of late to
transgress its traditional academic borders to an extent that its
relationship to the embryonic beginnings of the field is often unclear.
This occurrence is a consequence of the notable strides in fundamental
understanding of natural phenomena and its powerfully unifying effect.
In this regard, chemical engineering, in view of its empirical roots,
has had the most striking advances in the last several decades. The
early development of any engineering discipline has been to cater to
some selected societal need (or set of needs) served by the formulation
of core subjects towards maintaining some territorial integrity. Thus,
chemical engineering began naturally by being associated with industrial
chemistry, which was the art of producing chemicals for multifarious
applications towards meeting diverse societal needs. This beginning is
reflected well in the manner in which core chemical engineering
progressed from unit processes to unit operations, and further on to the
use of chemical kinetics and thermodynamics, the framework of continuum
mechanics to study transport processes in fluids, with parallel
development of statistical mechanics and molecular theories.

The use of mathematics in chemical engineering climbed precipitously
during the fifties, sixties, and seventies in the last century with a
healthy influx of analysis particularly in chemical reaction
engineering, fluid mechanics, transport phenomena, and the process
systems area. Graduate core courses in most chemical engineering
departments came to include Transport Phenomena, Thermodynamics,
Chemical Reaction Engineering, and Applied Mathematics. Although these
core courses have remained about the same over the years, there has
occurred some withering of intensity, probably because faculty interests
have tended to shift away from areas that benefit directly from such
background. Not infrequently, junior faculty in pursuit of greener
pastures elsewhere have subconsciously compromised the level at which
core courses are taught. These observations cannot be said to be
emanating from obscure corners of the profession. They seem largely
prevalent viewpoints, often expressed but more often held back. As
mentioned earlier, the objective of this article is to provoke
discussion, at least to the extent of examining the evolutionary course
of the profession. There is the familiar quip of ``Chemical Engineering
is what chemical engineers do!'' Ignoring the ``circularity'' of this
definition, we may inquire into whether or not chemical engineering is
served well by such a stance. Arbitrary activity is no one's objective.
Also, the propitiousness of a researcher's direction will be tempered by
the response of the profession at large.

This article seeks to argue for a rationally infinite domain for the
creativity of chemical engineers to flourish by formulating inquiries as
to whether or not their activity results in some attributes on which we
could expect some degree of consensus. While some attributes may not be
contested, others could spark debate:

\begin{enumerate}
\tightlist
\item
  As all research, engineering research must be creative as well as
  impactful. The focus of most researchers is impact as expressed by
  citations and, perhaps to a lesser extent, through recognition by a
  professional arm of the field as awards or keynote invitations to
  perspective conferences.
\item
  Contributions by chemical engineers must display some distinctive
  traits which must contribute to the solution of a significant problem.
  In the absence of such traits, the researcher's message is subsumed
  into the multitude of contributions in the field of application by
  researchers more directly connected to the subject. The area of
  biology, which represents perhaps the most exciting opportunity for
  chemical engineers to contribute creatively, is a particular case in
  point. Often, the only response to a question on relevance of the
  research to chemical engineering has been that the scale of
  observation is a reactor which does not attract the attention of a
  biologist. While reactor-scale work is certainly of importance, the
  contribution derives merit not from the scale of the observed system
  but from how the reactor is coaxed to work with quantitative use of
  biological principles.
\item
  A traditional chemical engineering audience often views askance
  research seminars that are stacked with biological facts or hypotheses
  without a lateral conduit to a clarifying conceptual source in
  chemical engineering for interpretation.
\item
  Another measure that seems important for chemical engineering activity
  in a different field is its potential to modify or enhance the core.
  Indeed, this feature is tied to securing core strength towards
  preserving a more liberal version of ``territorial integrity'' than
  before; ``more liberal'' implying generality rather than insularity.
  One may inquire into whether or not the contribution of engineers is
  generating new perspectives by virtue of tools that define the parent
  discipline, and further if this experience has widened as well as
  sharpened the tools that were used.
\item
  As in all other areas of science and engineering, opportunities abound
  in chemical engineering for the use of data science, machine learning,
  and artificial intelligence methods in dealing with complex systems.
\end{enumerate}

Our deliberations will focus on biology, on which chemical engineering
has had a major impact. Not surprisingly, many ChE departments have
added ``biological'' or ``biomolecular'' engineering to their names.
However, we begin with briefly reflecting on the traditional core areas,
starting first with applied mathematics and transport phenomena.

\textbf{Applied Mathematics and Transport}

A comprehensive account of mathematics in chemical engineering over the
past several decades has appeared in this journal.\textsuperscript{1}The
mathematical culture that prevailed in the latter half of the twentieth
century spurred the use of analytical tools in chemical engineering
applications. While Amundson and Aris were the chief architects of this
movement, there were others such as Horn, Acrivos, Brenner, Churchill,
and Stewart, to mention only a few, whose publications reflected a
significantly higher level of mathematics. It is interesting to note
that scholarship pervaded in the early sixties even among industrial
circles such as the Socony Mobil Oil (now a part of ExxonMobil) hosting
Truesdell's lectures in continuum mechanics.\textsuperscript{2} Numerous
in-depth mathematical applications emerged from industry such as the
determination of rate constants of catalytic reaction systems using
spectral information from suitably designed
experiments,\textsuperscript{3} the analysis of lumping reaction
systems,\textsuperscript{4} and so on. Such applications thrived from an
edifice that espoused analysis as well as computation.

Fluid mechanics thrived through a major pioneering effort by Acrivos and
his group. In particular, Acrivos established the use of matched
asymptotic expansions for problems in transport with several analytical
results.\textsuperscript{5} Aris' \emph{Vectors, Tensors and the
Equations of Fluid Mechanics} provided a rare perspective of the subject
more akin to a physicist's view.\textsuperscript{6} Scriven's study of
fluid interfaces and Marangoni instability and subsequent involvement
with interfacial driven flow set a tone in fluid mechanics distinctly
higher than the prevailing level.\textsuperscript{7} \emph{Transport
Phenomena} by Bird, Stewart, and Lightfoot most effectively changed the
teaching of Transport.\textsuperscript{8} The growth of this development
continued with the publication of their (long delayed) second edition.
The books of Leal\textsuperscript{9} and Deen\textsuperscript{10}deserve
special mention in preserving the graduate version of transport
phenomena. It is puzzling, however, that undergraduate courses in
transport phenomena have suffered some loss of level. There are notable
exceptions to this observation but consensus may be said to exist on
this impression. The unique expertise of chemical engineers on mass
transfer is at risk with dwindling attention to this subject. Often a
single semester course in graduate Transport is unable to include mass
transfer, in spite of its high importance to chemical and biological
systems encountered in research and in industrial practice.

Mathematical software such as Matlab and Comsol have enormously eased
the application of mathematical models in engineering. The capabilities
of symbolic software such as Maple and Mathematica are prodigious in
that computation can be postponed to the very final stage when parameter
values are to be inserted for numerical evaluation of a mathematical
model. Matlab is routinely used in undergraduate courses. Sometimes
their premature use has been at the expense of time that could be better
spent on issues of formulation and analysis that harbor the essence of
innovation. Notwithstanding the strongly positive role of mathematical
software in education, most undergraduate instructors sense a drop in
the comfort level of chemical engineering students in mathematics. Under
these circumstances, elimination of a first-level core course in Applied
Mathematics in ChE graduate programs would appear to be patently unwise.
Schools strong in the systems area are, however, an exception in this
regard since this field of research has made notable strides over the
years.

Generally, computational work can be said to be thriving but often
without recognition that it can sometimes be coaxed to gain from a
timely input of analytical reasoning. For example, instances can be
cited in which parameter estimation can be notably improved by local
analysis of nonlinear dynamic behavior with bifurcation methods. It is
unclear whether the age-old practice of inaugurating the introductory
chemical engineering course with dimensional analysis and the Buckingham
Pi Theorem is still in vogue. Clearly, parameter estimation of nonlinear
models can be aided by non-dimensionalization, as the number of
dimensionless parameters is less than the original set; furthermore, a
better rationale is frequently available for their initial estimates for
iterative computation.

The argument in favor of analytical reasoning is not made to diminish in
any way the importance of computational methods but rather to point to
its effectiveness in organizing computation. A culture of analysis has
the facility to cultivate creative ideas in the profession, for in its
absence, there would not be the likes of Gavalas,\textsuperscript{11}who
introduced topological methods in reactor analysis, Balakotiah and
Luss,\textsuperscript{12} for their work on singularity theory,
Feinberg,\textsuperscript{13} who made intriguing contributions to
chemical reaction network theory, and Jackson,\textsuperscript{14} who
published many interesting papers on particle flows as well as in
chemical reaction engineering. Of course, there are many more who
deserve mention, but the goal here is to argue for more analysis than
provide an exhaustive list of creative analysts.

\textbf{Chemical Reaction Engineering}

Graduate courses in Chemical Reaction Engineering have tended to become
more oriented to catalytic science because of significant developments
in this field. Discussion of transport effects in reaction systems
appears to have lost some of its prevalence. Dispersed phase reactors
receive no more than a modicum of mention due to limited familiarity
with the use of population balance equations. It is heartening, however,
that an industrial reaction engineering course organized by
Hickman\textsuperscript{15} under the auspices of Purdue CISTAR provided
an excellent picture of reaction engineering practice.

\textbf{Process Systems Engineering}

Process systems engineering (PSE) is the application of systems
engineering to biophysicochemical processes, where \emph{systems
engineering} is the methodical approach for the design of systems via
mathematical modelling, data analytics, optimization, and control, and
a\emph{system} 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.\textsuperscript{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.\textsuperscript{17} \textsuperscript{18}
\textsuperscript{1920} \textsuperscript{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.\textsuperscript{2223}
\textsuperscript{24} \textsuperscript{2526} \textsuperscript{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.\textsuperscript{28}
\textsuperscript{2930} \textsuperscript{31} \textsuperscript{3233}
\textsuperscript{34} \textsuperscript{3536} \textsuperscript{37}
\textsuperscript{3839} \textsuperscript{40} \textsuperscript{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).\textsuperscript{43} \textsuperscript{44}
\textsuperscript{4546} \textsuperscript{47} \textsuperscript{4849}
\textsuperscript{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
including\emph{Nature} , \emph{Science} , \emph{Cell} , and \emph{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,\textsuperscript{51}algorithms 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.

\textbf{Biological Engineering}

The traditional core background of chemical engineers constituted an
ideal fit for the study of biological systems in which enzyme-catalyzed
reactions and physical transport occurred aplenty albeit in a very
complex setting. The early entrants to the bio area such as Lightfoot,
Fredrickson, and Tsuchiya, and many other trend-setters were able to
show that chemical engineers can make unique contributions to this area.
Not surprisingly, the models were gross abstractions of the system with
a few ordinary differential equations and lumped chemical species.
Fredrickson et al. \textsuperscript{52,53,54} demonstrated the
applicability of kinetic models of microbial systems with structured
biomass from the perspective of an ``average'' cell. In an early
development of the population balance framework, Fredrickson et
al.\textsuperscript{55} developed models that could accommodate
population heterogeneity. In the course of time, however, the models
grew in sophistication including metabolic networks of increasing
complexity.
Bailey,\textsuperscript{56}Stephanopoulos,\textsuperscript{57}
Palsson,\textsuperscript{58}Lauffenburger,\textsuperscript{59} and many
others developed models with detailed metabolic perspectives. In
particular, metabolic engineering in which the genetic background of
cells is altered to change their metabolic potential was founded by
Bailey. Further, the flow cytometer had its early beginnings in Bailey's
laboratory under the name microfluorometer. The foregoing
accomplishments were impressive as they notably changed the quantitative
approach to modeling biological systems. The development of flow
cytometry also led to assessing the heterogeneity of microbial
populations and identification through population balance
models.\textsuperscript{60,61}

Metabolic modeling brims with many interesting perspectives that
reaction engineers will find attractive. However, its popularity in
ISCRE (International Symposia on Chemical Reaction Engineering
Conferences) has been surprisingly limited. A detailed metabolic network
may appear at first to be of daunting complexity. The recognition that
external nutrients enter the cells at rates much slower than those at
which intracellular reactions occur, however, offers the comfort of a
pseudo-steady state for intracellular components thus effecting linear
coupling of the various intracellular fluxes. Metabolic flux analysis
(MFA) is built on this edifice with matrices of stoichiometric
coefficients accounting for connectivity of metabolites in the network.
An excellent treatment of MFA with numerous examples of applications is
contained in Stephanopoulos et al.\textsuperscript{57} Clearly, the
fluxes obtained by such computation are \emph{regulated} versions. Thus,
if the regulatory scenario is different for one reason or another, the
fluxes would be altered and must again be obtained experimentally.

In a complex network, many ``reaction paths'' are conceivable as
cellular alternatives. A truly creative concept associated with these
alternatives is that of an \emph{elementary} mode. Crudely, it may be
viewed as comprising the uptake of an external nutrient into the cell to
be engaged in a sequence of intracellular reactions culminating in the
excretion of an ultimate product into the environment. Obviously, there
would be a countless number of such reaction paths and it would be
unrealistic to expect that \emph{all} of them will be commissioned by
the cell. (One could imagine, with some comfort, the potential reality
of a single path as arising from the absence of catalytic enzymes that
could divert metabolic species away from the specific reaction path).

It is now known that, even for a network of reasonable size, the number
of elementary modes can run into millions. Palsson's choice of reaction
paths that \emph{maximize} the biomass yield is a stroke of brilliance
because of its capacity for predicting yields of metabolic products with
ease even for relatively large
networks.\textsuperscript{62}Specification of the substrate uptake rate
(readily obtainable experimentally) leads to productivity calculations
of all cellular products. Extension of the flux balance analysis (FBA)
has been made for dynamic predictions (DFBA) by kinetically modeling
uptake rates free of regulatory effects.\textsuperscript{63}

The steady state approach, however, is not suited to accounting for the
phenomenon of \emph{metabolic regulation,} due to which cells
preferentially navigate through their metabolic network by controlling
the syntheses and activities of enzymes. The development of cybernetic
models,\textsuperscript{64,65,66} has led to dynamic modeling of
metabolism comprehensively inclusive of regulatory effects. While
cybernetic models have been successfully used to demonstrate numerous,
dynamic consequences of regulatory phenomena such as different uptake
patterns of mixed substrates, and dynamic effects of specific gene
knock-outs and gene insertion,\textsuperscript{67} their full
exploitation for metabolic engineering is still pending. Song's work on
lumped hybrid cybernetic models based on lumping of elementary modes
towards reducing model parameters deserves commendation as an example of
chemical engineering prowess.\textsuperscript{68}

Regulatory processes include transcriptional, transcriptomic, and
post-translational regulation. The foregoing cybernetic approach is
based on the postulate that the details of the foregoing mechanisms do
not need to be included explicitly as they represent the implementing
mechanism of the optimal strategies ensuring cellular survival goals.
Thus, consistency of regulatory patterns predicted by cybernetic models
with temporal gene expression profiles shows their capability to include
regulatory effects in metabolism.

Regulatory processes evidently control directly or indirectly the
distribution of all components that participate in life processes in
various ways. Consequently, modeling of biological processes and the
quantitative understanding to be had from it is clearly contingent on
consideration of regulation. The success with modeling regulatory
effects in bacterial metabolism provides considerable incentive to
extend the approach to eukaryotic systems in spite of the latter's
greatly added complexity.

Some beginnings have been made by Aboulmouna et
al.\textsuperscript{69}with cybernetic modeling of regulation in
macrophage cells which are concerned with the organism's immune
response. This system is riddled with uncertainties from various
sources. First, choices for cellular goals are not as suggestive for
eukaryotic cells as they are for bacterial cells. Second, multiple goals
may be involved at different stages of the cells' development. Third,
metabolic interconnections are not known in their entirety for a
rational cause-and-effect representation of all cellular events.
Aboulmouna's model was predicated on the goal of maximizing production
of the cytokine by cybernetic control of arachidonic metabolism. While
the details are best left to the cited reference, multiple goals were
included in dealing with different network components. The issue of
unknown links between the production rate and those of different
metabolites was accomplished by a linear fit of their respective time
series data. This model successfully predicted the regulatory
consequences of certain perturbations. The terse view just presented is
more to provide a broad perspective of the model assemblage than for
suggesting any generality of its specific features.

Regulatory processes are studied experimentally in considerably more
detail by measuring the concentrations of messenger RNA's (\emph{m}
-RNA), constituting crucial data for cybernetic modeling of eukaryotic
systems. The regulatory dynamics is represented by the\emph{m} -RNA
profiles so that the cybernetic model features \emph{m} -RNA
concentrations as variables among other components of
metabolism.\textsuperscript{70} The incentive for such modeling is the
possibility of laying a foundation for improved quantitative
understanding of eukaryotic systems with potential applications to
fighting disease and developing drugs.

\textbf{Population Balances}

Although population balances date back to the days of Boltzmann, this is
an area in which chemical engineering leadership has been notable since
the 1960s.\textsuperscript{71,72,55} The contribution of chemical
engineers has been especially creative since the coinage of the term
``internal'' coordinates by Hulburt and Katz\textsuperscript{71} for
variables other than spatial location of an entity which opened the
flood gates to a plethora of applications. Numerous reviews provide
perspectives of population balances.\textsuperscript{73,74,75,76}. An
application of consequence to biology is the formulation of signal
transduction problems in a population setting. The signaling variables
become internal coordinates of the cell and the stochastic process
characterizing the dynamics of intracellular reactions translating into
the convective and diffusive transport of cells in the abstract space of
internal coordinates. This transport can also occur in conjunction with
spatial transport if the cells were in motion through physical
space.\textsuperscript{77,78} The transfer of drug resistance between
bacterial species, which has been of major concern in combating
bacterial infection, is a significant area of
application.\textsuperscript{79,80} This transfer occurs by exchange of
resistance bearing plasmid DNA from the ``donor'' cell to the
``recipient'' following signaling reactions in the donor as shown in
Figure 1. A single cell stochastic model, oblivious to the presence of
other cells in the neighborhood, would show a steady state population
distribution in which individual cells exhibit stochastic dynamics,
whereas the more relevant population balance model would paint a
different picture of the same scenario as shown in Figure 2.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\par\null

Figure 1. Transfer of antibiotic resistance from donor to recipient
through transfer of plasmid.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

(\textbf{a) (b)}Figure 2. Temporal evolution of donor population towards
steady state. PrgB is a protein made by the donor cell in response to
the signal dispatched by the recipient which facilitates transfer of
plasmid. The ``on'' cells are ready to transfer plasmid while the
``off'' cells are not. Note the substantial difference in the steady
states predicted by the population balance model and the single cell
model. (a) Population balance model. (b) Single cell model

\textbf{Data Science and Machine Learning}

Data ridden systems are a frequent occurrence in all fields of human
endeavor as computers have the means to store an incredible amount of
data. Biological engineering, of particular interest to us, is no
exception in this regard as advances in measurement techniques have
helped collect massive amounts of data in numerous areas in which
chemical engineers are active. Venkatasubramanian provides an
interesting perspective of artificial intelligence in chemical
engineering.\textsuperscript{81} He is cautiously optimistic, and
observes that data science and machine learning with suitable infusion
of first principles could be an attractive combination for chemical
engineering applications. Diagnostic issues in health science are indeed
a promising area of application. Verma, in quest of the source of
peripheral neuropathy from the use of Vincristine in the treatment of
leukemic cancer, used machine learning to identify a handful of pain
associated metabolites.\textsuperscript{82} Early detection of such
metabolites from blood samples could lead to drug dosage adjustments for
improving the quality of life.

Lumping of chemical species in systems with a very large number of
species has been of interest to chemical reaction engineers. When
nonlinear reaction kinetics is involved, lumping strategies could be of
interest using data science and machine learning. Similarly, order
parameters or collective variables have been of interest in molecular
systems towards reducing their dimension as a means to study rare events
such as nucleation. Data science and machine learning could be a
potential source to elucidate a small number of collective variables in
terms of which the free energy of the system can be determined. While
such applications abound, the likelihood of pedestrian usage of the
methodology cannot be ignored.

\textbf{Concluding Remarks}

Our objective has been to present a perspective of current educational
and research trends in chemical engineering with an eye on maintaining a
strong core. It is our position that a conscious effort to nurture core
areas of chemical engineering through both education and research would
be essential to perpetuate the success of chemical engineers in
contributing to society. Chemical engineering embraces analysis of
transport and chemical reaction on systems encompassing a wide spectrum
of spatio-temporal scales that offer unlimited opportunities for
synthesis, optimal design control, and scope for local and global
perspectives. The 2018 Nobel Prize of Arnold for deploying directed
evolution techniques to engineer new enzymes is a striking example of
what chemical engineering expertise can accomplish. Seinfeld's
contributions to aerosol science and atmospheric chemistry have paved
the way for quality control regulations in the United States. Enormous
contributions have come about in the drug delivery area from
Langer,\textsuperscript{83} and Peppas.\textsuperscript{84}

Preservation and growth of chemical engineering core areas cannot occur
without allocation of resources for teaching and research. Graduate
programs, regardless of their research thrusts, must invest in high
quality core teaching. The present situation does not appear conducive
in this regard as researchers dedicated to core areas have to find
sagacious means of survival.

\textbf{Acknowledgments}

It is a pleasure to acknowledge the influence of several Purdue
colleagues in conceptualizing this article and that of Professor Shankar
Subramaniam of the Department of Bioengineering at the University of
California, San Diego who has been a source of education on biological
issues.

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