ML@ChemE: Past, Present, and Future of Machine Learning in Chemical Engineering

Although the initial machine learning (ML) applications were mainly on fault detection, signal processing, and process modeling, they extended to new areas like property estimation and material screening in later years; energy technologies, environmental issues, health, and new materials will likely be more important in future with the use of larger databases and new approaches like physics-informed ML and generative AI.

Abstract

This paper aims to review the machine learning (ML) applications in chemical engineering (ChemE) and provide perspectives for the future. First, the evolution of ML, data structures, and ML applications in ChemE were reviewed; then, the current state of the art in ML and its ChemE applications were summarized. Finally, a perspective for the future developments, including recently popularized tools like generative artificial intelligence (AI) and large language models (LLMs), as well as major challenges and limitations, was provided. Although the initial applications were mainly on fault detection, signal processing, and process modeling, the focus had been extended to other fields involving material development, property estimation, and performance analysis in later years with the use of more complex models and datasets. In future, new developments like LLMs will likely spread more; the other new applications like automated ML, physics-informed ML, and transfer learning, as well as field-specific databases, will also get more attention. ML applications in ChemE-related fields, like new energy technologies, environmental issues, and new material discovery, are expected to grow further.

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