Reconciling deep learning and first-principle modelling for the investigation of transport phenomena in chemical engineering

The use of machine learning in chemical engineering has the potential to greatly improve the design and analysis of complex systems. However, there are also risks associated with its adoption, such as the potential for bias in algorithms and the need for careful oversight to ensure the safety and reliability of machine learning-powered systems. This paper explores the opportunities and risks of using machine learning in chemical engineering and provides a perspective on how it may be integrated into engineering practices in a responsible and effective manner. We generated the text of this abstract with GPT-3, OpenAI's large-scale language-generation model. Upon generating the draft, we ensured that the language was to our liking, and we take ultimate responsibility for the content of this publication.