Projet PROSAFE (Horizon Europe, appel à projets 2022)
Action : Doctoral Networks
Panel : Information Science and Engineering
Statut du projet : SIGNED

PROSAFE proposes a novel doctoral training program in the multidisciplinary field combining machine learning, artificial intelligence, and process systems engineering with domain knowledge of process industry and process safety. PROSAFE will pioneer new foundations by integrating Quantitative Risk Assessment, Process Systems Engineering (PSE) with interpretable machine learning (ML) and artificial intelligence (AI) disciplines as targeted breakthroughs to achieve the objectives. To this end, PROSAFE will develop new synergistic tools and train skilled professionals to address this very important societal, economic, and environmental challenge of safe and sustainable process industries. PROSAFE research objectives are: 1: Harmonize robust QRA methods and implementation strategies for effective and improved risk assessment and process safety 2: Develop AI and ML (interpretable ML) models using domain knowledge for efficient, safe, and reliable operations 3: Develop synergistic integration of model-based with data-based methods for improved process safety operation and monitoring 4: Demonstration and validation of PROSAFE novel concepts and methods on industrial relevant case studies for safer operation PROSAFE's major training objectives are: 1: Training of doctoral candidates (DCs) through individual projects combining multidisciplinary competences in the areas of AI, ML, and PSE within the domain of process safety 2: Establish and pilot the concept of a truly interdisciplinary European multicenter training program in AI/ML, QRA, and PSE within the domain of safety in process industries through relevant network-wide events, courses, workshops, and on-site industry training that complements training in soft skills for effective communication and entrepreneurship. Through this research and training program, PROSAFE will contribute to realizing the promising potential of the new artificial intelligence paradigm with a particular focus on process safety in process industries.

Mots clés : process safety, quantitative risk assessment, process systems engineering, machine learning, machine learning, high hazard process industries, lng processing, renewable hydrogen, urea production

Participants :
- Sandra giordano (Etats-Unis) — Partner, associated partner, Organismes privés (hors enseignement), subvention : 0.0 €
- Université technique du Danemark (Danemark) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 905364.0 €
- Université norvégienne de sciences et de technologie (Norvège) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 893246.4 €
- Université Koç (Turquie) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 219168.0 €
- Kairos technology aps (Danemark) — Partner, associated partner, Organismes privés (hors enseignement), subvention : 0.0 €
- Risktec solutions limited (Royaume-Uni) — Partner, associated partner, Organismes privés (hors enseignement), subvention : 0.0 €
- Autoritat portuaria de barcelona (Espagne) — Partner, associated partner, Organismes publics (hors enseignement & recherche), subvention : 0.0 €
- Novotec consultores sa (Espagne) — Partner, beneficiary, Organismes privés (hors enseignement), subvention : 251971.2 €
- Université polytechnique de Catalogne (Espagne) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 503942.4 €
- Imperial College London (Royaume-Uni) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €