Min Wu , Ulderico Di Caprio , Florence Vermeire , Peter Hellinckx , Leen Braeken , Steffen Waldherr , M. Enis Leblebici
{"title":"An artificial intelligence course for chemical engineers","authors":"Min Wu , Ulderico Di Caprio , Florence Vermeire , Peter Hellinckx , Leen Braeken , Steffen Waldherr , M. Enis Leblebici","doi":"10.1016/j.ece.2023.09.004","DOIUrl":null,"url":null,"abstract":"<div><p><span><span>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 </span>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 </span>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.</p></div>","PeriodicalId":48509,"journal":{"name":"Education for Chemical Engineers","volume":"45 ","pages":"Pages 141-150"},"PeriodicalIF":3.5000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Education for Chemical Engineers","FirstCategoryId":"95","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1749772823000465","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Education for Chemical Engineers was launched in 2006 with a remit to publisheducation research papers, resource reviews and teaching and learning notes. ECE is targeted at chemical engineering academics and educators, discussing the ongoingchanges and development in chemical engineering education. This international title publishes papers from around the world, creating a global network of chemical engineering academics. Papers demonstrating how educational research results can be applied to chemical engineering education are particularly welcome, as are the accounts of research work that brings new perspectives to established principles, highlighting unsolved problems or indicating direction for future research relevant to chemical engineering education. Core topic areas: -Assessment- Accreditation- Curriculum development and transformation- Design- Diversity- Distance education-- E-learning Entrepreneurship programs- Industry-academic linkages- Benchmarking- Lifelong learning- Multidisciplinary programs- Outreach from kindergarten to high school programs- Student recruitment and retention and transition programs- New technology- Problem-based learning- Social responsibility and professionalism- Teamwork- Web-based learning