PROSAFE · Smart Integration of Process Systems Engineering & Machine Learning for Improved Process Safety in Process Industries

Smart Integration of Process Systems Engineering & Machine Learning for Improved Process Safety in Process Industries

The process industries – chemical, petrochemical, pharmaceutical, and energy – are cornerstones of our modern society. However, they operate under conditions that present inherent safety risks. We're seeing increasing plant complexity, aging infrastructure, and the potential for subtle, hard-to-detect failures that can cascade. Furthermore, human operators often face information overload. Simultaneously, the Industry 4.0 revolution provides an unprecedented opportunity. We now have access to vast amounts of data from sensors and control systems, coupled with significant advances in computational power. This sets the stage for AI and Machine Learning to provide new, predictive insights that can transform safety management. ProSafe is a strategic response to these challenges and opportunities. At its heart, ProSafe is a collaborative research and doctoral training program, bringing together expertise in Process Safety, Process Systems Engineering, and Machine Learning. Our vision is to improve safety in the process industries. We aim to do this by creating data-driven methodologies, crucially informed by process domain knowledge, that allow for real-time risk assessment and better operational decision support. Our mission is twofold: firstly, to train 12 doctoral candidates who will become the future leaders in this interdisciplinary field. Secondly, to foster strong, synergistic collaborations between top universities and key industrial players across Europe, ensuring our research is relevant and impactful. The ProSafe research program is meticulously structured to tackle key challenges in process safety. At the foundation, we have three core methodological Work Packages WP2: This focuses on 'Model-based foundations for improved risk assessment and process safety.' Here, we leverage Process Systems Engineering principles to build robust models that help us understand and quantify risks. WP3: This is dedicated to 'Artificial Intelligence and Machine Learning for risk monitoring and safe process operation.' This WP explores how AI can analyze vast amounts of data for early warnings, anomaly detection, and improved operational safety. WP4: This crucial work package focuses on 'Hybrid approaches and tools integration.' The goal here is to synergistically combine the strengths of the model-based approaches from WP2 with the data-driven techniques from WP3, creating powerful new tools. Each DC contributes to the specific objectives of these WPs. Our research is strongly application-driven, which is covered in WP5: 'Domain applications to selected high-hazard multisector process industries.' This ensures that the methodologies developed in WP2, 3, and 4 are relevant and tested in real-world contexts.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз