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Generative Artificial Intelligence in Chemical Process Engineering

machine learning

artificial intelligence

optimization

research

teaching

presentation

chemical engineering

chemistry

prediction

graph neural networks

inverse design

AI

Künstliche Intelligenz

KI

maschinelles Lernen

talk

RWTH Aachen University

Artur Schweidtmann

global optimization

nonlinear optimization

nonconvex optimization

hybrid modeling

physics-informed neural networks

Artificial neural networks

Gaussian processes

self-optimization

automated flow chemistry

Автор: Process Intelligence Research

Загружено: 2024-02-21

Просмотров: 9965

Описание: Video of a IChemE webinar on "Generative Artificial Intelligence (AI) in Chemical Process Engineering​"

Speaker: Prof. Dr. Artur Schweidtmann
20. February 2024

​​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.

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Generative Artificial Intelligence in Chemical Process Engineering

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