Machine learning for industrial sensing and control: A survey and
  practical perspective
Nathan P. Lawrence Seshu Kumar Damarla Jong Woo Kim Aditya Tulsyan Faraz Amjad Kai Wang Benoit Chachuat Jong Min Lee Biao Huang R. Bhushan Gopaluni
With the rise of deep learning, there has been renewed interest within the
process industries to utilize data on large-scale nonlinear sensing and control
problems. We identify key statistical and machine learning techniques that have
seen practical success in the process industries. To do so, we start with
hybrid modeling to provide a methodological framework underlying core
application areas: soft sensing, process optimization, and control. Soft
sensing contains a wealth of industrial applications of statistical and machine
learning methods. We quantitatively identify research trends, allowing insight
into the most successful techniques in practice.
  We consider two distinct flavors for data-driven optimization and control:
hybrid modeling in conjunction with mathematical programming techniques and
reinforcement learning. Throughout these application areas, we discuss their
respective industrial requirements and challenges.
  A common challenge is the interpretability and efficiency of purely
data-driven methods. This suggests a need to carefully balance deep learning
techniques with domain knowledge. As a result, we highlight ways prior
knowledge may be integrated into industrial machine learning applications. The
treatment of methods, problems, and applications presented here is poised to
inform and inspire practitioners and researchers to develop impactful
data-driven sensing, optimization, and control solutions in the process
industries.