  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.
