mlsanityData to 14 Oct 2026

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

arXiv 2401.13836 ↗First posted 24 Jan 2024eess.SY cs.LG cs.SYcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 37 of its 177 references that are in the map. First posted on arXiv 24 Jan 2024.

Builds on

37 of 177 references.

References · 37

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25 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 4.

8 papers.

  1. 2024Control-Informed Reinforcement Learning for Chemical Processescited by 9
  2. 2025A view on learning robust goal-conditioned value functions: Interplay between RL and MPCcited by 7
  3. 2025Reinforcement learning meets bioprocess control through behaviour cloning: Real-world deployment in an industrial photobioreactorcited by 1
  4. 2026DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecastingcited by 1
  5. 2025Improving Coverage in Combined Prediction Sets with Weighted p-valuescited by 0
  6. 2026GLU: Global-Local-Uncertainty Fusion for Scalable Spatiotemporal Reconstruction and Forecastingcited by 0
  7. 2026Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systemscited by 0
  8. 2026Embedding Linear Equality Constraints in Probabilistic Neural Networks for Dynamic Modellingcited by 0

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