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

• DOI: 10.1016/j.conengprac.2024.105841
• Publication Date: 2024-01-19T17:38:36Z
• Authors: 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
• Keywords: Interpretability

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