It is envisioned that generative artificial intelligence (AI) will have a huge impact on chemical process engineering. Generative AI has gained immense traction across diverse sectors, exemplified by remarkable achievements such as ChatGPT's language generation and GitHub Copilot's code generation. Generative AI also holds immense potential to reshape chemical process engineering by offering advanced data handling, modeling and decision-support capabilities, ultimately driving innovation and efficiency in the industry. However, there are only limited applications in chemical engineering so far. Promising applications are proposed for generative AI in process engineering including autocompletion of flowsheets, autocorrection of engineering documents, P&ID generation and AI-assisted HAZOPs.
It is felt there is a need to conduct research and development in three main areas to ultimately develop useful generative AI tools in our domain: data, information representation, and model architectures including mechanistic information.
Artur M. Schweidtmann, Professor, TU Delft
Artur is a tenure-track assistant professor for chemical engineering at Delft University of Technology and director of the Process Intelligence Research lab (www.pi-research.org). His research focuses on the combination of artificial intelligence and chemical engineering. He received his Master of Science from RWTH Aachen University in 2017 and defended his PhD from RWTH in 2021, both in Chemical Engineering. During his studies, he spent the academic year 2013/2014 at Carnegie Mellon University as a visiting student via the DAAD ISAP program. He performed his Master thesis at the University of Cambridge.
The material presented in this webinar has not been peer-reviewed. Any opinions are the presenter's own and do not necessarily represent those of IChemE or the Computer Aided Process Engineering Special Interest Group. The information is given in good faith but without any liability on the part of IChemE.
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