Computational Notebooks in Chemical Engineering Curricula

  • Jonathan Verrett The University of British Columbia http://orcid.org/0000-0003-4709-6276
  • Fani Boukouvala Georgia Institute of Technology
  • Alexander Dowling University of Notre Dame
  • Zachary Ulissi Carnegie Mellon University
  • Victor Zavala University of Wisconsin-Madison

Abstract

Computational notebooks are an increasingly common tool used to support student learning in a variety of contexts where computer programming can be applied. These notebooks provide an easily distributable method of displaying text and images, as well as sections of computer code that can be manipulated and run in real-time. This format allows instructors to introduce content and methodologies to students in a single document that can be copied and manipulated. In this paper we outline case studies of computational notebook usage at a variety of institutions. These notebooks have been successfully used in required and elective courses from the undergraduate sophomore-level to the graduate level. In each case study, implementation, pedagogical strategies, and results are discussed. 

Author Biographies

Jonathan Verrett, The University of British Columbia

Jonathan Verrett is an Instructor in the Department of Chemical and Biological Engineering at the University of British Columbia. He teaches a variety of topics with a focus on plant design in chemical engineering. His research interests include design education, open education, peer-learning, and academic program quality enhancement.

Fani Boukouvala, Georgia Institute of Technology

Fani Boukouvala is an Assistant Professor in the School of Chemical & Biomolecular Engineering at the Georgia Institute of Technology. Her research lies in the area of process systems engineering and specifically in data-driven modeling and optimization of complex systems. At Georgia Tech she has been teaching process design & economics and separations processes, and she has also introduced a new elective course for senior undergraduate and graduate students on data-driven process systems engineering. She has a strong interest on the development of teaching modules for introducing data-science, optimization and programming to chemical engineering students at the undergraduate and graduate levels.

Alexander Dowling, University of Notre Dame

Alexander Dowling is an assistant professor in the Department of Chemical and Biomolecular Engineering at the University of Notre Dame in Indiana, USA. His research and teaching interests lie in mathematical modeling, computational optimization, and uncertainty quantification with applications in energy and sustainability.

Zachary Ulissi, Carnegie Mellon University

Zachary Ulissi joined Carnegie Mellon University in 2016.  He received his B.S. in Physics and B.E. in Chemical Engineering from the University of Delaware in 2009, a Masters of Advanced Studies in Mathematics from the University of Cambridge in 2010, and a Ph.D. in Chemical Engineering from MIT in 2015.  His thesis research at MIT focused on the the applications of systems engineering methods to understanding selective nanoscale carbon nanotube devices and sensors under the supervision of Michael S. Strano and Richard Braatz. Prof. Ulissi was then a postdoctoral fellow at Stanford with Jens K. Nørskov where he worked on machine learning techniques to simplify complex catalyst reaction networks, applied to the electrochemical reduction of N2 and CO2 to fuels.

Victor Zavala, University of Wisconsin-Madison

Victor Zavala is the Baldovin-DaPra Associate Professor in the Department of Chemical and Biological Engineering at the University of Wisconsin-Madison. Before joining UW-Madison, he was a computational mathematician in the Mathematics and Computer Science Division at Argonne National Laboratory. He holds a B.Sc. degree from Universidad Iberoamericana and a Ph.D. degree from Carnegie Mellon University, both in chemical engineering. His research interests are in the areas of mathematical modeling of energy and agricultural systems, high-performance computing, optimization under uncertainty, and model predictive control. 

Published
2020-07-15
Section
Manuscripts