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

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.