Data Science in the Chemical Engineering Curriculum

Friday, March 24, 2023 11:30 am - 11:30 am EDT (GMT -04:00)

Tom Duever and Engineering building 6


Data Science in the Chemical Engineering Curriculum

The terms “Data Science”, “Big Data” and “Data Analytics” are becoming pervasive affecting
many aspects of our lives, many professional disciplines and certainly the profession of chemical
engineering. The reason for this is of course the increasing availability of data brought on by the
proliferation of inexpensive sensors and instrumentation, new measurement capabilities related
to the developments of the Internet of Things and smart sensors, and improved data storage
power like cloud computing. Large amounts of data are therefore readily available.
Broadly speaking Data Science can be divided into three components, namely data management,
statistical and machine learning, and visualization. It is an interdisciplinary field that uses
scientific methods, processes, algorithms, and systems to extract knowledge and draw
conclusions from data. Some view the term data science to be synonymous with statistics.
Others view this as a burgeoning new discipline and compare the emergence of data science with
the dawn of computer science.
There are of course many examples of the use of data science methodologies in the chemical
engineering community including multivariate analysis, on-line fault detection, inferential
sensors, batch data analytics, experimental design approaches, parameter estimation and model
discrimination. Applications range from materials manufacturing to pharmaceutical production,
oil and gas, pulp and paper and many others.
Historically, chemical engineering undergraduate programs, certainly in Canada, have
incorporated some form of statistical training. This has been accomplished by incorporating one
or more courses in applied statistics into the undergraduate core curriculum, in many cases
taught by chemical engineering faculty. However, it can be argued that chemical engineering
education and training may not have kept pace with the proliferation of data described above
and that therefore techniques from a data-poor era are being used to extract information from
data available today from plants, refineries and data-rich experiments. The challenge then is what
can be done to better prepare graduate chemical engineers for the realities of today when it
comes to data analysis?


Tom Duever is Dean of the Faculty of Engineering and Architectural Science and a
Professor of Chemical Engineering at Toronto Metropolitan University. Prior to his
role at TMU, Dr. Duever served as chair of the University of Waterloo’s Department of
Chemical Engineering for nine years, navigating the department toward
unprecedented growth. He has also taught industrial short courses in experimental
design and polymer reaction engineering.
Dr. Duever is an accomplished researcher with interests including applied statistics,
experimental design, polymer reaction engineering and product development. He
has more than 100 articles in journals and conference proceedings to his credit and
has supervised the research projects of over 40 graduate students.
Dr. Duever is a registered Professional Engineer in the Province of Ontario, a Fellow
of the Chemical Institute of Canada and a Fellow of the Canadian Academy of
Engineering. He holds Bachelor, Master’s and PhD degrees in chemical engineering
from the University of Waterloo, where he completed his graduate work under the
supervision of the late Professor Park M. Reilly.