Explainable proactive control of industrial processes

One of the goals of Industry 4.0 is the adoption of data-driven models to enhance various aspects of the manufacturing process, such as monitoring equipment conditions, ensuring product quality, detecting failures, and preparing optimal maintenance plans. However, many machine-learning algorithms require a large amount of training data to reach desired performance. In numerous industrial applications, such data is either not available or its acquisition is a costly process. In such cases, simulation frameworks are employed to replicate the behavior of real-world facilities and generate data for further analysis. Simulation frameworks typically provide high-quality data but are often slow which can be problematic when real-time decision-making is required. Control approaches based on simulation-based data commonly face challenges related to inflexibility, particularly in dynamic production environments undergoing frequent reconfiguration and upgrades. This paper introduces a method that seeks to strike a balance between the reliance on simulated data and the limited robustness of simulation-based control methods. This balance is achieved by supplementing available data with additional expert knowledge, enabling the matching of similar data sources and their combination for reuse. Furthermore, we augment the methods with an explainability layer, facilitating collaboration between the human expert and the AI system, leading to informed and actionable decisions. The performance of the proposed solution is demonstrated through a case study on gas production from an underground reservoir resulting in reduced downtime, heightened process reliability, and enhanced overall performance. This paper builds upon our conference paper (Kuk et al., 2023), addressing the same problem with an extended, more generic methodology, and presenting entirely new results.