TY - CONF A1 - Sarif, Raduan A1 - Tiebe, Carlo A1 - Herglotz, Christian T1 - Early Response Prediction for H2 Sensors N2 - Green hydrogen (H2) is essential for the global transition to clean energy; it will significantly reduce emissions from heavy industry and the long-distance transport system. H2 can be used as fuel in fuel cells, storing surplus renewable energy, and as a feedstock in industrial processes. However, H2 faces significant safety challenges during storage and transportation. Accidents due to H2 leakage and explosions raise serious concerns due to its high flammability, rapid diffusion in air, and extremely low ignition energy. To mitigate risks associated with H2 leakages, reliable and automated H2 safety systems are essential for emergency repairs or shutdown. An early response from H2 sensors is crucial for early warning in accidents. The earlier response time of H2 sensors is often constrained by their sensor principle, which is heavily influenced by the sensor material’s properties. This study explores methods for earlier sensor response through predictive algorithms. Specifically, we investigate transient response predictions using a First-Order (FO) model and propose improvements through the First-Order with early response and the First-Order with adapted early response model. Both models can predict the stable value of the H2 sensor response from a small time window, which is 70.89% and 83.72% earlier, respectively, than the time required for the sensor hardware to reach it physically. The model’s performance is evaluated by calculating the fitting error with a 2 % threshold. Our current research lays the groundwork for future advancements in real-time sensor response predictions for hydrogen leakage. T2 - IARIA Congress 2025 : The 2025 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications CY - Venice, Italy DA - 06.07.2025 KW - H2 Safety KW - H2 leakage detection KW - First-Order (FO) model KW - H2 Sensor data analysis KW - H2 sensor response predictions PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-640676 UR - https://www.thinkmind.org/articles/iaria_congress_2025_1_250_50159.pdf SN - 978-1-68558-284-5 SP - 1 EP - 8 PB - IARIA Press CY - Wilmington AN - OPUS4-64067 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Sarif, Raduan A1 - Tiebe, Carlo A1 - Herglotz, Christian T1 - Analysis of Methods for Predicting H2 Sensor Responses N2 - Hydrogen (H2) is crucial for replacing fossil fuels and achieving net-zero emissions, but its flammability and explosiveness pose safety challenges. Rapid H2 leak detection is essential for triggering emergency accidents. However, H2 sensor response is constrained by material properties and gas flow dynamics, causing response and detection delays. Our current study explores various available algorithms for H2 sensor response prediction from early responses with a small time window, accelerating leakage detection. Our findings identify the most efficient algorithms for real-time implementation, enhancing H2 safety systems. T2 - SMSI 2025 2025-05-06 - 2025-05-08 Nürnberg CY - Nuremberg, Germany DA - 06.05.2025 KW - H2 safety KW - Early H2 leakage detection KW - Prediction algorithms KW - Stable H2 value prediction PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-640617 SN - 978-3-910600-06-5 DO - https://doi.org/10.5162/SMSI2025/C6.3 SP - 169 EP - 170 PB - AMA Verband für Sensorik und Messtechnik e.V. CY - Berlin AN - OPUS4-64061 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Sarif, Raduan A1 - Tiebe, Carlo A1 - Herglotz, Christian T1 - Early Response Prediction for H2 Sensors N2 - Green hydrogen (H2) is essential for the global transition to clean energy; it will significantly reduce emissions from heavy industry and the long-distance transport system. H2 can be used as fuel in fuel cells, storing surplus renewable energy, and as a feedstock in industrial processes. However, H2 faces significant safety challenges during storage and transportation. Accidents due to H2 leakage and explosions raise serious concerns due to its high flammability, rapid diffusion in air, and extremely low ignition energy. To mitigate risks associated with H2 leakages, reliable and automated H2 safety systems are essential for emergency repairs or shutdown. An early response from H2 sensors is crucial for early warning in accidents. The earlier response time of H2 sensors is often constrained by their sensor principle, which is heavily influenced by the sensor material’s properties. This study explores methods for earlier sensor response through predictive algorithms. Specifically, we investigate transient response predictions using a First-Order (FO) model and propose improvements through the First-Order with early response and the First-Order with adapted early response model. Both models can predict the stable value of the H2 sensor response from a small time window, which is 70.89% and 83.72% earlier, respectively, than the time required for the sensor hardware to reach it physically. The model’s performance is evaluated by calculating the fitting error with a 2 % threshold. Our current research lays the groundwork for future advancements in real-time sensor response predictions for hydrogen leakage. T2 - IARIA Congress 2025 : The 2025 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications CY - Venice, Italy DA - 06.07.2025 KW - H2 Safety KW - H2 leakage detection KW - First-Order (FO) model KW - H2 Sensor data analysis KW - H2 sensor response predictions PY - 2025 AN - OPUS4-64195 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Sarif, Raduan A1 - Tiebe, Carlo A1 - Herglotz, Christian T1 - Analysis of Methods for Predicting H2 Sensor Responses N2 - Hydrogen (H2) is crucial for replacing fossil fuels and achieving net-zero emissions, but its flammability and explosiveness pose safety challenges. Rapid H2 leak detection is essential for triggering emergency accidents. However, H2 sensor response is constrained by material properties and gas flow dynamics, causing response and detection delays. Our current study explores various available algorithms for H2 sensor response prediction from early responses with a small time window, accelerating leakage detection. Our findings identify the most efficient algorithms for real-time implementation, enhancing H2 safety systems. T2 - SMSI 2025 Conference – Sensor and Measurement Science International CY - Nuremberg, Germany DA - 06.05.2025 KW - H2 safety KW - Early H2 leakage detection KW - Prediction algorithms KW - Stable H2 value prediction PY - 2025 AN - OPUS4-64194 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -