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.
Highlights
Data have always played a critical role in chemical engineering applications, but recent advances in artificial intelligence enable new possibilities for increasing the information gained from chemical engineering data sets
Experiments are often guided by applying physical principles, model-based design of experiments, and intuition derived from previous experiments
While robust optimization is traditionally used for addressing parametric uncertainty, it can be applied to black-box function uncertainty, e.g., Wiebe et al (2020) use Gaussian processes (GPs) to model black-box functions based on data and develop a method for robust reformulation of constraints depending on these black-box functions
Summary
Data have always played a critical role in chemical engineering applications, but recent advances in artificial intelligence enable new possibilities for increasing the information gained from chemical engineering data sets. This review article discusses the upper left and lower right quadrants (shaded blue): these are regimes where current chemical engineering research offers transformations to increase the information content of relevant data sets. We first discuss 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:. Data veracity can be construed as a third characteristic, Artificial neural networks
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