Maximizing information from chemical engineering data sets: Applications to machine learning
It addresses data challenges for chemical engineering applications, but is incremental as it reviews existing research.
This review identifies four challenging data characteristics in chemical engineering that hinder classical machine learning approaches, and discusses how current research is extending data science and machine learning to address these issues.
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.