Risk assessment in hydrogen supply chains is a complex task due to the inherent uncertainties and interdependencies across production, storage, transportation, and utilization. Bayesian Networks (BNs) provide a robust probabilistic framework for modeling these risks, integrating diverse data sources, and supporting decision-making under uncertainty. Unlike deterministic models, BNs capture conditional dependencies between variables, enabling dynamic updates as new evidence becomes available. This article explores the application of BNs to hydrogen supply chains, focusing on hydrogen-specific risk factors, probabilistic dependencies, and practical implementation challenges.

A Bayesian Network is a directed acyclic graph where nodes represent random variables and edges denote probabilistic dependencies. Each node has a conditional probability table (CPT) quantifying relationships with its parent nodes. For hydrogen supply chains, key nodes may include production method reliability, storage integrity, transportation delays, and end-use safety. For example, the probability of a storage failure may depend on the storage method (compressed gas, liquid hydrogen, metal hydrides) and operational conditions (temperature, pressure cycles). Similarly, transportation delays could be influenced by route conditions, regulatory approvals, and vehicle availability.

In hydrogen production, BNs can model risks such as feedstock shortages, equipment malfunctions, or energy supply interruptions. Steam Methane Reforming (SMR) nodes might include natural gas price volatility, carbon capture system efficiency, and catalyst degradation. Electrolysis nodes could incorporate renewable energy variability, membrane durability, and stack lifetime. By integrating historical failure rates and real-time sensor data, BNs update probabilities dynamically, providing actionable insights. For instance, if a PEM electrolyzer shows increased degradation, the BN adjusts the likelihood of production shortfalls and triggers maintenance alerts.

Hydrogen storage introduces unique risks like embrittlement, leakage, and thermal management failures. A BN for compressed gas storage might link tank material fatigue, filling frequency, and inspection intervals to leakage probabilities. For cryogenic storage, boil-off rates and insulation performance become critical nodes. Metal hydrides add variables such as absorption-desorption cycle stability and impurity sensitivity. By quantifying these relationships, BNs help optimize storage designs and maintenance schedules. For example, if data indicates higher leakage rates at certain pressure thresholds, operators can adjust protocols to mitigate risks.

Transportation risks vary by mode. Pipeline networks face challenges like hydrogen embrittlement in steels and third-party damage. Truck transport nodes may include accident rates, route congestion, and refueling station availability. Maritime transport introduces additional variables like ammonia or LOHC decomposition efficiency and port handling safety. BNs can simulate scenarios such as delayed shipments due to weather or regulatory hurdles, enabling contingency planning. A BN might reveal that blending hydrogen into natural gas pipelines reduces transportation risks but increases separation costs at the destination, guiding infrastructure investments.

End-use applications like fuel cells or industrial processes also benefit from BN risk assessment. Fuel cell nodes could include membrane degradation, contamination sensitivity, and load cycling effects. In steel manufacturing, hydrogen purity and injection rate variability affect product quality and furnace stability. By modeling these factors, BNs support reliability-centered maintenance and process optimization. For instance, a BN could identify that impurity levels above 10 ppm increase fuel cell failure rates by 30%, prompting stricter purification measures.

Data integration is a critical challenge in BN implementation. Hydrogen supply chains generate heterogeneous data from IoT sensors, maintenance logs, weather forecasts, and market reports. BNs require high-quality data to calibrate CPTs accurately. Missing or noisy data can lead to biased inferences. Techniques like expectation-maximization or Bayesian estimation help address data gaps. For example, if historical leakage data is sparse, expert elicitation can supplement empirical observations to define initial probabilities. Over time, sensor networks provide continuous updates, refining the model’s accuracy.

Decision-making under uncertainty is another strength of BNs. They enable forward-looking analyses like predictive maintenance and backward-looking diagnostics like root cause analysis. Sensitivity analysis identifies high-impact nodes, prioritizing risk mitigation efforts. For instance, a BN might show that transport delays have a cascading effect on storage occupancy and production scheduling, suggesting investments in redundant transport capacity. Scenario analysis evaluates trade-offs, such as comparing centralized vs. decentralized production models under demand fluctuations.

Hydrogen-specific nodes require careful parameterization. Storage failure probabilities may derive from accelerated aging tests showing metal hydrides lose 5% capacity per 1,000 cycles. Transport delay nodes could use logistics data indicating a 15% probability of delays exceeding 24 hours in winter months. Combustion risk nodes might incorporate experimental data on hydrogen-air mixture flammability limits (4-75% vol). These parameters ensure the BN reflects real-world conditions.

Despite their advantages, BNs face limitations. Computational complexity grows with node count, requiring simplification in large networks. Dynamic BNs extend the framework to temporal risks, such as seasonal demand variations or gradual material degradation, but increase data demands. Validation against real-world outcomes is essential to avoid overfitting. For example, a BN predicting pipeline leaks should be tested against actual incident records to verify its predictive power.

In summary, Bayesian Networks offer a powerful tool for risk assessment in hydrogen supply chains. By modeling probabilistic dependencies among production, storage, transport, and end-use nodes, they provide a systematic approach to uncertainty management. Data integration from multiple sources enhances their accuracy, while scenario analysis supports informed decision-making. As hydrogen infrastructure expands, BNs will play a vital role in ensuring safety, reliability, and efficiency across the value chain. Future advancements in data analytics and computational methods will further refine their applicability, making them indispensable for the hydrogen economy.