Physics

Accounting

Business

Data mining

Machine learning

Engineering

Artificial intelligence

Variance (accounting)

Volume (thermodynamics)

Data science

Computer science

Quantum mechanics

Big data

Science and engineering

Engineering ethics

Chemical Engineering Science

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.

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