Numerical Methods in Chemical Process Engineering Using Python: Tools for Modeling, Simulation, and Optimization - Hardcover

Jorquera, Hector; Gelmi, Claudio A.

Hardcover

ISBN 10: 303222957X ISBN 13: 9783032229571

Publisher: Springer, 2026

Synopsis

This book provides an overview of the most widely used numerical methods in chemical process engineering organized by increasing levels of complexity.

The book begins with numerical linear algebra, which is essential for solving large systems of equations, and continues with nonlinear equations, a cornerstone for modeling equilibrium and reaction kinetics. Ordinary differential equations are then addressed, covering both initial value and boundary value problems, with an emphasis on their role in describing dynamic behavior and transport phenomena. The section concludes with partial differential equations, which are fundamental for capturing spatial and temporal variations in heat, mass, and momentum transfer.

The second part of the book presents a curated set of solved problems, each supported by Python code and figures. Covering topics such as parameter estimation, confidence intervals, and bioreactor optimization, the problems emphasize both steady-state and dynamic systems. Each example covers the deriving governing equations, related code, and interpreting results, providing a consistent learning path, while additional discussions encourage students to explore related concepts beyond the presented problem.

About this title

About the Author

Héctor Jorquera is Professor in the Department of Chemical and Bioprocess Engineering, Pontificia Universidad Católica de Chile. He holds BSc and MSc degrees in Chemical Engineering from Universidad de Chile and a PhD from the University of Minnesota. Professor Jorquera has been teaching numerical methods to engineering students since 1992. He has been a visiting professor in ENSBANA (Dijon, France), Imperial College (London, UK), University of Iowa (CGRER) and Harvard University (ACMG). He has published more than 60 papers in scientific journals. He is the author of the textbook Introduction to Air Pollution Engineering (in Spanish).

Claudio A. Gelmi earned a B.Sc. in Industrial and Chemical Engineering and an M.Sc. in Chemical Engineering from Pontificia Universidad Católica de Chile (UC), and a Ph.D. in Chemical Engineering from the University of Delaware (Newark, USA). He served as an Assistant Professor in UC’s Department of Chemical and Bioprocess Engineering and held leadership roles as Associate Dean for Engineering Education in the School of Engineering and Director of the Center for Teaching Development within the Academic Vice-Rector’s Office. He has authored more than 20 research articles and two textbooks, among other publications. He has led data-driven initiatives as an independent consultant and has taught artificial intelligence in executive education programs at UC. He currently serves as Director of the Artificial Intelligence Center at Universidad San Sebastián (Chile).

From the Back Cover

This book provides an overview of the most widely used numerical methods in chemical process engineering organized by increasing levels of complexity.

The book begins with numerical linear algebra, which is essential for solving large systems of equations, and continues with nonlinear equations, a cornerstone for modeling equilibrium and reaction kinetics. Ordinary differential equations are then addressed, covering both initial value and boundary value problems, with an emphasis on their role in describing dynamic behavior and transport phenomena. The section concludes with partial differential equations, which are fundamental for capturing spatial and temporal variations in heat, mass, and momentum transfer.

The second part of the book presents a curated set of solved problems, each supported by Python code and figures. Covering topics such as parameter estimation, confidence intervals, and bioreactor optimization, the problems emphasize both steady-state and dynamic systems. Each example covers the deriving governing equations, related code, and interpreting results, providing a consistent learning path, while additional discussions encourage students to explore related concepts beyond the presented problem.

Edition details

• Publisher: Springer
• Publication date: 2026
• Language: English
• ISBN 10: 303222957X
• ISBN 13: 9783032229571
• Binding: Hardcover
• Number of pages: 277