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

•We identify four characteristics of data arising in chemical engineering applications.•We discuss high variance, low volume data and low variance, high volume data.•We explore noisy/corrupt/missing data and restricted data with physics-based limitations.•We show how research intersecting chemical engineering and AI derives value from data. 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.