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With the rise of deep learning, there has been renewed interest within theprocess industries to utilize data on large-scale nonlinear sensing and controlproblems. We identify key statistical and machine learning techniques that haveseen practical success in the process industries. To do so, we start withhybrid modeling to provide a methodological framework underlying coreapplication areas: soft sensing, process optimization, and control. Softsensing contains a wealth of industrial applications of statistical and machinelearning methods. We quantitatively identify research trends, allowing insightinto 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 andreinforcement learning. Throughout these application areas, we discuss theirrespective industrial requirements and challenges.  A common challenge is the interpretability and efficiency of purelydata-driven methods. This suggests a need to carefully balance deep learningtechniques with domain knowledge. As a result, we highlight ways priorknowledge may be integrated into industrial machine learning applications. Thetreatment of methods, problems, and applications presented here is poised toinform and inspire practitioners and researchers to develop impactfuldata-driven sensing, optimization, and control solutions in the processindustries.

Machine learning for industrial sensing and control: A survey and practical perspective