• Digital twins: machine learning-based predictive modeling and real-time failure impact assessment
  The rapid development of machine-learning (ML) and artificial-intelligence (AI) is driving progress across many areas of engineering, including the monitoring and operation of transmission infrastructure. Predictive solutions based on these technologies significantly enhance operational safety, particularly for carbon-dioxide (CO2) transport. In modern infrastructure projects–such as CO2 and hydrogen installations–quantitative risk-assessment models and industrial-accident consequence analyses (e.g., flammability, toxicity and explosion zones) are increasingly being applied.
  Digital representations of transmission systems (digital twins) make it possible to detect failures and estimate their potential consequences before they occur, directly contributing to shorter response times, more efficient alerting and more effective implementation of emergency-response plans. Exemplary applications include the predictive detection of pipeline leaks and the pre-incident or real-time assessment of failure impacts. Integrating data-acquisition, processing and visualization modules enables more effective risk management, helping to reduce both environmental and material losses... dalej

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