Kakkar, S;
Kwapinski, W;
Howard, CA;
Kumar, KV;
(2021)
Deep neural networks in chemical engineering classrooms to accurately model adsorption equilibrium data.
Education for Chemical Engineers
, 36
pp. 115-127.
10.1016/j.ece.2021.04.003.
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Abstract
The latest industrial revolution, Industry 4.0, is progressing exponentially and targets to integrate artificial intelligence and machine learning algorithms with existing technology to digitalise chemical processes across the industry, especially in the area of online monitoring, predictive analysis and modelling. Machine learning algorithms are being constantly implemented in both academic laboratories and industry to uncover the underlying correlations that exist in the high-dimensional and complex experimental and synthetic data that describes a chemical process. Indeed soon, proficiency in artificial intelligence methodology will become a required skill of a chemical engineer. It is therefore becoming essential to train chemical engineers with these methods to help them to adapt to this new era of digitised industries. Keeping these issues in mind, we introduced deep neural networks to the final-year chemical engineering students through a computer laboratory exercise. The exercise was delivered in fast-track mode: the students were asked to develop deep neural networks to model and predict the equilibrium adsorption of uptake of three different acids by activated carbon at four different temperatures. In this manuscript, we discuss in detail this laboratory exercise from delivery and design to the results obtained and the students’ feedback. In the classroom, the students compared the adsorption equilibrium data obtained using the established theoretical adsorption isotherms and empirical correlations with the neural networks developed in the classroom. The experience obtained from the classroom confirmed that this exercise gave the students the essential knowledge on the AI and awareness on the jargons in the world of machine language and obtained the required level of coding skills to develop a simple neural net with one layer or a sophisticated deep networks to model an important unit operation in chemical engineering and to accurately predict the experimental outcomes.
| Type: | Article |
|---|---|
| Title: | Deep neural networks in chemical engineering classrooms to accurately model adsorption equilibrium data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ece.2021.04.003 |
| Publisher version: | http://doi.org/10.1016/j.ece.2021.04.003 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10127757 |
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