Maximizing information from chemical engineering data sets: Applications to machine learning

TL;DR

This paper reviews how unique data challenges in chemical engineering affect machine learning applications and discusses recent research efforts to address these issues, highlighting future research directions.

Contribution

It identifies four key data characteristics in chemical engineering that challenge classical AI methods and reviews how current research extends data science to tackle these issues.

Findings

Different data characteristics require tailored machine learning approaches

Current research is adapting data science methods for chemical engineering challenges

Future research needs to address physics-based data limitations

Abstract

It is well-documented how artificial intelligence can have (and already is having) a big impact on chemical engineering. But classical machine learning approaches may be weak for many chemical engineering applications. This review discusses how challenging data characteristics arise in chemical engineering applications. We identify four characteristics of data arising in chemical engineering applications that make applying classical artificial intelligence approaches difficult: (1) high variance, low volume data, (2) low variance, high volume data, (3) noisy/corrupt/missing data, and (4) restricted data with physics-based limitations. For each of these four data characteristics, we discuss applications where these data characteristics arise and show how current chemical engineering research is extending the fields of data science and machine learning to incorporate these challenges. Finally, we identify several challenges for future research.