Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/49543| Title: | An artificial intelligence course for chemical engineers | Authors: | Wu , M Di Caprio, U Vermeire, F Hellinckx, P BRAEKEN, Leen Waldherr, S Leblebici, ME |
Issue Date: | 2023 | Publisher: | ELSEVIER SCI LTD | Source: | Education for chemical engineers, 45 , p. 141 -150 | Abstract: | Artificial intelligence and machine learning are revolutionising fields of science and engineering. In recent years, process engineering has widely benefited from this novel modelling and optimisation approach. The open literature can offer several examples of their applications to chemical engineering problems. Increasing investments are devoted to these techniques from different industrial areas, but insufficient information on a structured course covering these topics in a chemical engineering curriculum could be found. The course in this paper intends to reduce this gap. We introduce one of the first courses on artificial intelligence applications in a chemical engineering curriculum. The course targets Master's students with a chemical engineering background and insufficient knowledge of statistical approaches. It covers the main aspects by utilising frontal lectures and hands-on exercises with active learning methods. This paper shows the methodology we adapted to introduce students to machine learning techniques and how they responded to each class. The student performances for each test are shown, as well as the survey results based on student feedback and suggestions. This work contains essential guidelines for educators who will provide an artificial intelligence course in a chemical engineering curriculum. | Keywords: | Modelling;Optimisation;Chemical engineering;Artificial intelligence;Python | Document URI: | http://hdl.handle.net/1942/49543 | e-ISSN: | 1749-7728 | DOI: | 10.1016/j.ece.2023.09.004 | ISI #: | 001148083400001 | Rights: | 2023 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved. | Category: | A1 | Type: | Journal Contribution |
| Appears in Collections: | Research publications |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| main.pdf Restricted Access | Published version | 1.88 MB | Adobe PDF | View/Open Request a copy |
| 2604f3motoMda.pdf | Peer-reviewed author version | 1.24 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.