top of page

Coordination of European Research on Industrial Safety towards Smart and Sustainable Growth

RASEM

Robot-assisted Environmental Monitoring for Air Quality Assessment in Industrial Scenarios

  • Major accident prevention frameworks traditionally focus on technical and organisational safety measures, while resilience aspects such as adaptive capacity, learning and recovery are not systematically assessed. The COVID-19 pandemic and other disruptive events have highlighted the importance of organisational resilience for maintaining safety under abnormal and rapidly changing conditions. However, resilience concepts are rarely operationalised within safety management systems for major accident hazards. The RASEM project addresses this gap by integrating resilience concepts into major accident risk assessment and safety management, supporting more robust prevention and response capabilities.

  • The project investigates how organisational resilience can be operationalised and assessed within the context of major accident prevention. It examines which indicators and assessment approaches can capture adaptive capacity, learning and preparedness in organisations handling hazardous activities. Another research question concerns how resilience-oriented assessment can complement existing safety management systems and support improved decision-making under disruptive conditions.

  • RASEM will deliver a resilience-oriented assessment framework and a set of indicators supporting major accident prevention and safety management. Outputs include guidance on integrating resilience considerations into risk assessment and safety management practices, supporting improved preparedness and adaptive capacity.

  • The project is structured into work packages covering project management, development of a resilience assessment concept, definition of resilience indicators, application and validation through case studies, and dissemination of results.

  • Super-resolution for Gas Distribution Mapping

    GNN-DM: A Graph Neural Network Framework for Real-World Gas Distribution Mapping 

    RASEM - Robot-Assisted Environmental Monitoring for Air Quality Assessment

    Final Report

    Mid-term Report

    Development of a Low-Cost Sensing Node with Active Ventilation Fan for Air Pollution Monitoring

    Boosting a Low-Cost Sensor Network with Mobile High-Quality Sensors

    Using Redundancy in a Sensor Network to Compensate Sensor Failures

    Gather Dust and Get Dusted: Long-Term Drift and Cleaning of Sharp GP2Y1010AU0F Dust Sensor in a Steel Factory

    Learning From the Past: Sequential Deep Learning for Gas Distribution Mapping

    High-quality meets low-cost: Approaches for hybrid-mobility sensor networks 

    Presentation at SAF€RA's 2022 symposium

  • Sergej Johann

    BAM

    Germany

    Natalia Albizu

    BAM

    Germany

    Nicolas Winkler

    BAM

    Germany

    Paula Jussheikki

    Outokumpu Oyj

    Finland

    N/A

    Tallink Silja Oy

    Finland

    Jessica Erdmann

    BAM

    Germany

    Mikko Poikkimäki

    FIOH

    Finland

    Anneli Kangas

    FIOH

    Finland

    Henna Veijalainen

    FIOH

    Finland

    Arto Säämänen

    FIOH

    Finland

    Erik Schaffernicht

    Örebro University

    Sweden

    Achim Lilienthal

    Örebro University

    Sweden

Next
Previous
bottom of page